Searchlight | Digital Marketing & AI | Aztek Web

Aztek Marketing News Roundup (10/26- 10/30)

Mon, 26 Oct 2026 08:00:00 Z

Searchlight is Aztek's marketing news roundup that brings together the week’s most relevant developments in marketing, search, AI, and digital strategy, all in one place. We update this article throughout the week with news we think is worth your time, along with context to help you understand what changed, why it matters, and what it could mean for your business.

This week's topics:

AI Search Can't Agree on Which Brands to Recommend

10/27: AI Search Can't Agree on Which Brands to Recommend

Getting your business recommended by ChatGPT or another AI platform sounds like a pretty good marketing win. As more people use these tools to research products and compare companies, businesses naturally want to show up in those conversations. The tricky part is figuring out how often that's actually happening.

A recent study from AI visibility platform Envoyra found that over a four-week period, AI tools changed which brands they recommended in 79% of the tests conducted, even when researchers asked the exact same questions each week.

For marketers, this revolving door of recommendations makes it harder to know whether their efforts to improve AI search visibility are actually working.

What the Research Found About AI Brand Recommendations

Envoyra analyzed 2,720 responses from ChatGPT, Claude, Gemini, and Perplexity. Researchers asked each platform questions about business software, including CRM systems and marketing automation tools, to see which brands they recommended.

For marketing automation, ChatGPT recommended HubSpot most frequently, while Claude favored Klaviyo. Gemini and Perplexity leaned toward ActiveCampaign. Even when researchers returned to the same platforms with the same questions, the recommendations sometimes changed.

Of the 680 question-and-platform combinations tested, only 144 returned the exact same list of brands across all four weeks. There was some consistency among the most frequently recommended brands, but results were less predictable beyond those leaders.

The study was limited to five software categories and specific AI models, so we shouldn't assume every industry will see the same results. Still, it offers a useful look at how much AI recommendations can vary.

Why AI Search Visibility Is Harder to Measure

Marketers are used to watching their Google rankings move around. It's frustrating when a keyword drops a few positions, but at least there's an established way to track where a website appears and how that affects traffic. AI search doesn't offer that same level of consistency.

Imagine checking ChatGPT on Monday and finding your company recommended alongside several competitors. You check again the following week, and your business is nowhere to be found. Did something change with your website? Did a competitor suddenly become more relevant? Or did the AI tool simply generate a different answer?

As more businesses start tracking AI brand recommendations, this uncertainty becomes a problem. A report showing that your company appeared in ChatGPT five times might sound encouraging, but the number doesn't tell you much without knowing how often you were tested or how those results compare over time.

What Should Businesses Do About AI Search Visibility?

AI search is worth watching, especially if your customers are using it to research potential vendors. Businesses just need to be realistic about what they can measure. For starters, checking your visibility once a month probably isn't enough to understand how often you're being recommended. Looking at the same relevant questions regularly can help you identify patterns, while monthly performance can help you track growth over time. It's also worth checking multiple AI platforms rather than assuming ChatGPT represents everyone's experience.

There are still plenty of reasons to invest in the Search Engine Optimization (SEO) fundamentals, too. SEO and generative engine optimization (GEO) work together to establish your business' identity and authority to make it easier for AI to cite your site. If your website does a poor job explaining your services or answering common customer questions, that's worth fixing regardless of what an AI tool recommends. AI mentions may eventually become a useful part of marketing reports, but they shouldn't take priority over the metrics that show whether your marketing efforts are delivering results.

Don't Get Too Caught Up in AI Rankings

AI search is changing how people research businesses, and marketers have good reason to understand how their brands appear in those results. This study suggests that measuring that visibility will require more patience than checking a traditional keyword ranking report.

Businesses should be cautious about putting too much weight on individual AI recommendations, especially when those recommendations can change without an obvious explanation. There's value in knowing whether AI tools are mentioning your brand. There's a lot more value in understanding whether your marketing is helping the right customers find you and eventually become leads.

Aztek Marketing News Roundup (10/19- 10/23)

Mon, 19 Oct 2026 08:00:00 Z

This week's topics:

Facebook's New Algorithm Puts Content Relevance First
Meta's TikTok Ad Ban Is a Wake-Up Call for Advertisers

10/20: Facebook's New Algorithm Puts Content Relevance First

On Oct. 13, Meta announced a new feature called "Your Algorithm", which allows Facebook users to tell the platform what topics they're interested in seeing more or less of. Instead of relying entirely on Facebook to figure out their preferences based on previous activity, users can now provide direct feedback using their own words.

How Facebook's New Algorithm Controls Work

Facebook has long used signals like likes, comments, and viewing activity to determine what appears in someone's feed. The new "Your Algorithm" feature gives users another way to influence those recommendations.

Users can now access tools to manage their interests and provide more detailed feedback. If someone enjoys gardening, they can tell Facebook they want to see more gardening content. If they're tired of seeing certain topics, they can express that preference, too.

Meta says these changes can influence recommendations in real time, giving users more control over the content they encounter in their feeds and Reels.

While Meta hasn't announced specific changes to how business page content will be ranked, the writing is on the wall. Facebook wants to show people more of what they're personally interested in, which makes audience relevance an important consideration for businesses.

Your Facebook Page Isn't a Company Bulletin Board

Take a look at the average business Facebook page. You'll probably find announcements about upcoming trade shows, employee milestones, company awards, and links to the latest blog posts. There's nothing wrong with sharing those updates. They're part of telling your company's story, and some of them may be genuinely interesting to your followers.

The problem is when a Facebook page becomes little more than a running list of everything happening inside the business. Just because something is important to your company doesn't automatically make it interesting to your audience.

Consider a manufacturer attending an industry trade show. A post announcing its booth number might be useful to people attending the event, but probably won't mean much to everyone else following the page. A post discussing an industry problem the team plans to address at the show could have broader appeal. It's still promoting the company's involvement, but it gives people something relevant to think about.

The same goes for employee spotlights. Rather than simply announcing an anniversary, share something interesting about the person's work or a contribution that customers would recognize. Give the audience a reason to care about the story.

What Makes Facebook Content Worth Seeing?

Businesses don't need to stop posting company news or turn every update into an educational lesson. Sometimes a behind-the-scenes photo or a funny moment from the office is exactly the kind of content people enjoy. The important part is understanding who follows your page and what they're likely to find interesting.

A few questions can help when planning content:

Who is this post for? Consider whether it speaks to customers, potential buyers, employees, or another audience you want to reach.
Why would someone care? Think about what makes the information useful or interesting to someone outside the company.
Does the post need more context? An announcement might be more engaging if you explain why the news is meaningful.
Would it be interesting without a link? Give people something worthwhile in the post itself, even if they never visit your website.

It's also worth paying attention to which posts generate genuine conversations rather than looking only at impressions. Those responses can offer clues about the subjects your audience cares about.

Give People a Reason to Follow Along

There's also no evidence yet that these new controls will automatically hurt organic reach for business pages. What the update does offer is a good reason to review how you're using the platform.

If your Facebook page mostly documents company activities, consider how those same stories could be more interesting to the people following you. Think about what customers ask your team, what they care about in your industry, and which parts of your business they might enjoy seeing. Your company has plenty of things worth sharing on social media. The challenge is figuring out which ones your audience wants to hear about and giving those stories the attention they deserve.

10/22: Meta's TikTok Ad Ban Is a Wake-Up Call for Advertisers

What happens when an advertising platform decides it no longer wants your business? TikTok is finding out. On Oct. 8, Meta stopped allowing TikTok's parent company, ByteDance, to advertise on Facebook and Instagram in seven countries, including the United States. The restriction also affects third-party advertisers running campaigns that link to TikTok or other ByteDance properties.

It's an unusual move between two of the biggest names in social media, and one that raises an interesting question for marketers. How much control do you really have over your advertising when the platforms you're using can change the rules whenever they want?

Why Meta Banned TikTok Ads

Meta and TikTok have spent years competing for users' attention, particularly as short-form video has become a major part of social media consumption. Now, that competition is affecting how the companies advertise.

Meta defended its decision to ban TikTok ads by arguing that it isn't obligated to help a competitor attract users away from Facebook and Instagram. The restriction also affects other advertisers whose campaigns link to ByteDance properties in the countries covered by the policy.

Whether the decision makes good business sense for Meta is another conversation. For advertisers, it's worth paying attention to how much influence these platforms have over who can advertise and what they're allowed to promote.

TikTok Is Moving Its Advertising Dollars Elsewhere

TikTok hasn't wasted much time adjusting its advertising strategy. According to Business Insider, YouTube's share of TikTok's advertising spending increased from 9% to 17% following Meta's restriction. Pinterest's share grew from 3% to 9%, while mobile advertising networks also received a larger portion of its budget.

TikTok has the resources to make those adjustments relatively quickly. A smaller business that relies heavily on Facebook or Instagram advertising might have a much harder time replacing that traffic if it suddenly lost access. Of course, most businesses aren't going to find themselves in a direct advertising dispute with Meta. The more likely concerns are familiar ones, like changing ad policies or rising costs that make campaigns less profitable.

What the Meta TikTok Ad Ban Means for Advertisers

It's easy to become dependent on a particular advertising platform, especially when it's consistently generating leads. If Facebook is bringing in qualified prospects at a reasonable cost, there's little reason to start shifting significant portions of your budget elsewhere. Businesses should absolutely invest in the channels that are producing results.

The concern is what happens when that performance changes. We've seen social platforms adjust their algorithms, introduce new advertising restrictions, and change how content reaches audiences. Marketers have to work within those decisions, regardless of how much time or money they've invested in a channel.

The Meta TikTok ad ban is a particularly visible example of that relationship. Businesses can optimize their campaigns and build effective audiences, but they ultimately operate within someone else's platform. That makes it worth considering how easily your marketing could adapt if your strongest channel suddenly became less reliable.

How to Avoid Relying Too Heavily on One Platform

Businesses don't need to advertise on every available platform to protect themselves. In fact, doing so could spread an already limited marketing budget too thin. A better starting point is understanding how dependent you are on your current channels and whether you have realistic alternatives.

For example, if most of your qualified leads come from Meta advertising, consider testing another channel with a small portion of your budget. You might find that Google Ads reaches buyers who are actively looking for your services, or that LinkedIn is more effective for reaching specific B2B decision-makers.

Those tests may not produce immediate results, and some alternatives won't be worth pursuing. However, knowing what works before you're forced to make a change puts your business in a better position.

It's also worth continuing to invest in your website and email marketing. These give you opportunities to attract and maintain relationships with customers beyond social media. They still depend on outside technology, but they offer more direct control over your content and customer relationships.

Your Marketing Strategy Needs Room to Adapt

Meta's decision to block TikTok advertising probably won't change how most businesses run their campaigns tomorrow. Still, the situation offers a useful reason to review how much of your marketing depends on a single platform.

If your strongest channel is performing well, keep investing in it. Just make sure you understand the risks of that dependence and have considered what you'd do if performance changed.

Aztek Marketing News Roundup (10/12- 10/16)

Mon, 12 Oct 2026 08:00:00 Z

This week's topics:

TikTok and Meta Are Giving AI More Control Over Advertising
New UX Research Shows Why Website Navigation Still Deserves Attention

10/13: TikTok and Meta Are Giving AI More Control Over Advertising

For years, advertising platforms have used AI to make campaign decisions based on user behavior and performance data, reducing the amount of manual work advertisers need to do. Most advertisers have gotten comfortable letting those systems handle at least some of the work.

Now, TikTok and Meta want to take things further. During Advertising Week New York earlier this month, both companies announced new AI capabilities that could handle more of the day-to-day responsibilities of managing advertising campaigns. As these platforms take on more of the work, advertisers have to decide how much control they're comfortable giving up, especially when the platforms making those decisions are also selling them the ad space.

What TikTok and Meta Announced

On Oct. 5, TikTok introduced several AI-powered advertising updates designed to make campaign management more automated. Meta made similar announcements on Oct. 6.

Some of the notable updates include:

TikTok Smart+ Creative Selection: Recommends combinations of existing brand assets, AI-generated creative, and creator content for advertisers to review and approve.
TikTok Search Advertising Automation: Gives the platform more control over decisions about keywords and ad creative within search campaigns.
TikTok Agentic Leads: Uses AI-powered conversations to engage potential customers and help businesses qualify leads.
Meta AI Business Assistant: Helps advertisers understand campaign performance and receive recommendations.

Some of these features are still being tested, so advertisers shouldn't expect to find every option in their accounts just yet.

AI Ad Buying Is Moving Beyond Basic Automation

Automated bidding and audience targeting are nothing new. What's changing is the amount of responsibility these platforms want AI to take on. The term agentic AI comes up frequently in these announcements. Essentially, it refers to AI systems that can combine an LLM with instructions, tools, and access to information so that it can act toward a specific goal instead of simply offering suggestions.

Think about the difference between a platform recommending that you adjust your advertising budget and one that can actually make the adjustment after you approve it. For smaller marketing teams, that could be genuinely helpful. Campaign management takes time, and having AI handle repetitive tasks could make it easier to maintain campaigns without constantly monitoring every setting.

Of course, the less time advertisers spend making those decisions themselves, the more important it becomes to understand how the platform is making them.

What Happens When AI Makes More Advertising Decisions?

Consider a manufacturer running paid campaigns to generate sales inquiries. An automated system might find that a particular audience generates more form submissions at a lower cost, so it shifts more of the budget toward that audience.

From the advertising platform's perspective, the campaign could be performing well. The business might see things differently if those submissions are coming from people looking for products it doesn't manufacture. This scenario is where AI ad buying becomes more complicated. Advertising platforms generally optimize around the goals and information they're given. They don't automatically know which leads are worth pursuing or how much a customer might be worth over several years.

There's also the issue of creative control. AI can generate variations of an ad quickly, but someone still needs to make sure the messaging is accurate and relevant to the people the business wants to reach. None of these concerns are new to digital advertising. Greater automation simply gives marketers more reason to pay attention to them.

How Should Businesses Approach AI Ad Buying?

There's no reason to avoid these tools simply because they automate more of the process. If they can help improve campaign performance or reduce unnecessary work, they're worth exploring. However, businesses should understand what they're agreeing to automate before turning on new features.

Start by reviewing the settings within existing campaigns.

Which decisions are already automated?
What performance goals are those systems working toward?
Do those goals reflect what the sales team considers a successful lead?

As new capabilities become available, test them with a manageable budget and compare the results against existing campaigns. Look beyond the advertising dashboard when evaluating success, particularly if lead quality has historically been an issue.

Better Tools Still Need Someone Paying Attention

TikTok and Meta are making it increasingly possible for advertisers to manage campaigns with less manual involvement. For busy marketing teams, there's plenty of potential value in that.

Still, a campaign that runs efficiently isn't necessarily a campaign that's helping the business grow. Marketers need to understand what the automation is doing and be willing to intervene when the numbers look good but the actual business results tell a different story.

10/15: New UX Research Shows Why Website Navigation Still Deserves Attention

Businesses spend plenty of time figuring out how to bring more people to their websites. Between paid advertising, SEO, and newer AI search opportunities, there's no shortage of ways to invest in attracting visitors. Yet something as basic as website navigation can still make it difficult for those visitors to find what they need.

According to updated research from Baymard Institute, 61% of desktop websites and 65% of mobile websites in its 2026 benchmark delivered mediocre-to-poor homepage and category navigation experiences. The research evaluated more than 180 leading ecommerce websites across the U.S. and Europe.

Those findings are a good reminder that even well-established businesses can struggle to make their websites easy to navigate. Yes, Baymard's research focuses on ecommerce, but many of the same issues apply to B2B websites, especially when visitors need to dig into products or services before deciding whether to reach out.

Why Website Navigation Still Creates Problems

Most businesses know their own websites pretty well. They understand their services, recognize the industry terminology, and know exactly where to find important information. Unfortunately, that familiarity can make it difficult to recognize when a website is confusing to someone visiting for the first time.

A manufacturer, for example, might organize its website around internal business divisions. That organization makes perfect sense to employees but may mean very little to a purchasing manager searching for a specific manufacturing capability.

Navigation can also become cluttered as businesses grow. New products get added, services expand, and pages accumulate without much consideration for how everything fits together. Eventually, visitors are left sorting through menus that reflect the company's history more than their own needs.

Baymard's research identified category organization as the weakest-performing navigation area across desktop and mobile websites. The findings reinforce the importance of organizing information in ways that make sense to the people actually using the site.

How Poor Website Navigation Can Affect Conversions

Consider someone researching a potential supplier. They've found a company through Google and want to confirm that it offers a particular service before reaching out. The service is available, but it's buried under an unfamiliar menu category. After a few unsuccessful attempts to find the right information, the visitor returns to the search results and tries another company.

When visitors struggle to find information, businesses risk losing opportunities they've already invested in attracting. This is especially relevant for B2B companies, where buyers often need detailed information before they're comfortable contacting a sales representative.

Good website navigation helps visitors understand what a business offers and find the information they need to move forward. It should also make the next step easy to identify when someone is ready to request a quote or start a conversation.

Website Navigation Best Practices Worth Revisiting

Improving website navigation doesn't automatically mean investing in a complete redesign. Sometimes the most useful improvements come from reviewing your existing website through a visitor's eyes.

A quick navigation audit is a good place to start:

Review your menu labels. Would someone unfamiliar with your company understand what each option means? Replace internal terminology with language your customers are more likely to recognize.
Evaluate how information is organized. Related products and services should be easy to find without forcing visitors to guess which category they belong to.
Test the mobile experience. Make sure menus work properly on smaller screens and don't make visitors dig through unnecessary layers to reach important pages.
Follow your conversion paths. Choose a common customer task, such as finding a product specification and requesting a quote. See how easily someone can complete it.
Ask someone unfamiliar with the site to try it. Give them a specific task and watch where they struggle. Their experience may reveal problems that employees have stopped noticing.

These website navigation best practices are relatively simple to evaluate, although deciding how to address the issues may require a closer look at your site's structure.

If you're unsure where to start, focus on the products or services that generate the most business inquiries. Improvements to those paths may offer more immediate value than reorganizing every page at once.

Better Website Experiences Start With the Basics

There's always another marketing tool or technology promising to help businesses reach more customers. Those investments can be worthwhile, but they won't do much good if visitors arrive at a website and struggle to find what they're looking for.

Baymard's findings suggest that website navigation remains an area where many businesses have room to improve. Taking another look at how your own site is organized is a reasonable place to start. After all, you've already done the work to get someone interested enough to visit. Your website design should make it easy for them to keep going.

Aztek Marketing News Roundup (10/05- 10/09)

Mon, 05 Oct 2026 08:00:00 Z

This week's topics:

Trying to Fake E-E-A-T for AEO? Google Is Paying Attention
Facebook Is Limiting Link Posts: What Businesses Need to Know

10/6: Trying to Fake E-E-A-T for AEO? Google Is Paying Attention

As more businesses look for ways to improve their visibility in AI search, E-E-A-T has naturally become part of the conversation. If search engines and AI systems are looking for trustworthy, authoritative sources, it makes sense to show who created your content and why they know what they’re talking about. The problem starts when brands try to manufacture that authority instead of earning it.

Google recently updated its guidance to specifically warn against deceptive authorship information. That includes fake creator profiles, made-up names, AI-generated headshots, or false credentials designed to make content appear as though it was written by a human expert. Google says those practices make a page less trustworthy to both users and its automated quality systems.

Why AEO Creates More Pressure to Look Authoritative

AEO has put more attention on whether a brand looks like a credible source. That need for credibility can lead marketers to focus heavily on signals such as author bios, credentials, expert reviews, or first-hand experience. None of those things are a problem when they’re accurate, and Google encourages clear authorship information while emphasizing the importance of real expertise where appropriate.

The issue is treating those signals like shortcuts. Adding an impressive-sounding author bio doesn’t make weak content more useful, and calling something “expert reviewed” doesn’t help if no qualified expert actually reviewed it. Making up a persona to give an article more authority may create the appearance of E-E-A-T, but it works against the trust Google says is most important.

E-E-A-T for AEO Isn’t a Checklist

This idea is where E-E-A-T often gets misunderstood. Google says E-E-A-T itself isn’t a single ranking factor, and its systems use a mix of signals to identify content that demonstrates experience, expertise, authority, and trustworthiness. Of those qualities, Google says trust is the most important.

That reality makes the idea of faking E-E-A-T for AEO pretty contradictory. If the goal is to appear trustworthy by using information that isn’t actually true, the strategy falls apart before it starts. It may look optimized on the surface, but it doesn’t give search engines or users a real reason to trust the content.

Make Real Expertise Easier to Find

A better approach is to look at the expertise your business already has and make it more visible. For many B2B companies, that expertise already exists inside the organization, whether it’s product knowledge, technical experience, customer insights, or years of working through the same problems their audience is trying to solve.

Marketing can turn that knowledge into stronger content without inventing an expert persona. These tactics an include:

Connecting articles to real subject matter experts
Including first-hand insight
Clearly explaining where information came from
Giving readers enough background to understand why an author is qualified to discuss the topic

Google’s broader guidance still comes back to the same idea: create content that offers original value, demonstrates real knowledge, and helps people accomplish what they came to the page to do.

AEO Still Comes Back to Trust

AI search may change how people discover information, but it hasn’t changed the basic requirement that the information needs to deserve their trust. That’s why Google’s authorship update is worth paying attention to beyond traditional SEO, especially as more businesses start thinking about E-E-A-T for AEO.

The goal shouldn’t be to manufacture more authority signals. It should be to make your real expertise clearer, easier to verify, and more useful to the people looking for answers, because that’s a much stronger foundation than trying to guess what an algorithm wants to see.

10/09: Facebook Is Limiting Link Posts: What Businesses Need to Know

Sharing a link to your latest blog post or product page on Facebook has been a pretty standard part of social media marketing for years. Write the content, publish it on your website, and share it with your followers. Simple enough.

Facebook has recently started experimenting with making that process a little more complicated. Meta has expanded a test that limits how many external links certain business Pages can share organically each month without paying for a subscription. The change isn't affecting every business just yet, but it raises some interesting questions about how much companies should rely on social media to drive website traffic.

How Facebook's Link Post Limits Work

Meta began testing restrictions on external link sharing in December 2025. The company expanded the test in September 2026 alongside the introduction of its Meta One for Business subscription packages.

Under the test, selected Facebook Pages without a qualifying subscription can share external links in just two organic posts or comments per calendar month. Businesses that want to share more links may need to subscribe to Meta One, which offers higher posting allowances.

There are some important exceptions. The restrictions don't apply to personal profiles or Facebook Groups, and paid advertisements aren't included in the limit. According to Sprout Social's Oct. 1 guidance, the restriction currently applies to Facebook's own publishing tools, with no observed impact on posts published through Sprout Social.

For now, these Facebook link post limits are still being tested, so businesses shouldn't assume their Pages are automatically affected.

Should Businesses Rethink Their Facebook Strategy?

Before making any major changes, businesses should first check whether their Page is included in the test. If it is, this is a good opportunity to evaluate how Facebook fits into their overall marketing strategy.

A few things worth considering:

Look at your actual results. How much website traffic comes from Facebook? More importantly, does that traffic lead to inquiries or other meaningful actions?
Be selective about what you share. Not every blog post or company update needs an external link. Prioritize the content that's most valuable to your audience and business goals.
Make your Facebook content useful on its own. Instead of posting a short teaser that requires someone to click away, share more of the actual information directly in the post.
Evaluate the cost before subscribing. If Facebook consistently generates quality leads, paying for additional link-sharing capabilities might be worth considering. If it doesn't, a subscription probably won't solve that problem.

Your Marketing Strategy Shouldn't Depend on Facebook's Rules

Facebook's latest test is another reminder that businesses don't control the platforms they use. Features can change, algorithms can shift, and something that was once free can eventually become a paid service.

Social media can still be valuable for building awareness and maintaining relationships with customers. However, relying on any single platform to consistently deliver website traffic leaves businesses vulnerable whenever the rules change.

Your website and the content you publish there remain assets you control. Email marketing can also help maintain direct connections with your audience without depending entirely on a social platform's distribution decisions.

Aztek Marketing News Roundup (09/28- 10/02)

Mon, 28 Sep 2026 08:00:00 Z

This week's topics:

Google Search Console Adds Multimodal Search Reporting
LinkedIn Is Leaning Further Into B2B Creator Marketing

9/29: Google Search Console Adds Multimodal Search Reporting

On Sept. 24, Google announced new multimodal search reporting in Search Console, making it possible to see how website content performs when people search using images through tools like Google Lens, Circle to Search, image uploads, and Chrome’s “Search this image” feature.

The update isn’t a reason to rethink your entire SEO strategy, but it is another sign that search behavior is expanding, and marketers now have a little more visibility into how people are finding them.

What Is Google Search Console Multimodal Search Reporting?

Google Search Console already gives marketers data on how their websites appear in Google Search, including impressions, clicks, search queries, and page performance. The new multimodal search filter adds another layer to that reporting. Someone might take a photo with Google Lens, circle an object on their Android phone, upload an existing image, or right-click an image in Chrome to search for something similar.

Google Search Console multimodal search reporting gives site owners a way to isolate that activity and see how their content is performing within those searches. Google is also including the data within its Generative AI performance reporting.

Why Visual Search Data Matters

The interesting part of this update is what the filter tells us about how search itself is changing. Typing “industrial rubber gasket with six bolt holes” into Google requires knowing how to describe what you’re looking at. Taking a photo of the gasket and asking Google to identify it is much easier.

Visual search isn’t limited to shoppers trying to find a pair of shoes they saw on Instagram. It can potentially play a role in discovering replacement parts, identifying materials, researching equipment, or finding products based on how they look. For B2B marketers, that makes the images on a website more than filler. Product photos, application images, diagrams, and other original visuals can contribute to how people discover content.

What Marketers Should Watch in Search Console

Google says the multimodal search reporting is rolling out globally, and sites will begin seeing metrics when they receive traffic from those types of searches. If that data starts showing up, look at which pages are getting multimodal impressions and whether any patterns emerge. Product pages or application pages may perform differently than more text-heavy resources. Certain types of imagery may also appear more often than expected.

Clicks matter, too. Search Console’s existing performance reporting lets marketers compare impressions and clicks, which can provide some context around whether visibility is actually turning into website traffic.

Search Is Becoming More Visual

Traditional search isn’t disappearing. People will continue typing queries into Google, just as they always have. They just have more ways to search now.

For now, check the data when it becomes available, see which pages are showing up, and pay attention to what your audience tells you through their search behavior. Sometimes the next useful SEO insight may come from an image instead of a keyword.

10/1: LinkedIn Is Leaning Further Into B2B Creator Marketing

The term B2B creator marketing can sound like another attempt to bring influencer marketing into the business world. There’s some overlap, but that doesn’t really capture what’s happening on LinkedIn right now.

In B2B, a creator might be an industry consultant, an engineer who regularly shares useful advice, a company executive, or another subject matter expert with credibility in a specific field. Brands can work with these people to create or promote content that reaches buyers through a recognizable human voice rather than relying entirely on posts from a company page.

LinkedIn is making that easier. The platform has invested more heavily in tools that help businesses discover relevant creators and turn their content into paid campaigns.

What B2B Creator Marketing Looks Like on LinkedIn

LinkedIn's Creator Marketplace gives advertisers a way to find people based on their expertise and audience. The company has also been testing additional creator discovery capabilities to make it easier for brands to identify people who fit a particular campaign or subject area.

Imagine a manufacturing company trying to reach plant managers. A creator partnership might involve working with someone who has spent years in manufacturing and regularly posts practical insights about operations. A software company could partner with a respected professional who talks about the problems its customers deal with every day. The value comes from expertise first. Audience size can help, but a huge following isn't necessarily the goal.

Why LinkedIn Is Investing More in Creators

B2B buyers already spend a lot of time learning from other people in their industries. LinkedIn's own research has found that marketers believe peer and expert voices play an important role as buyers research and validate decisions.

A company's website can explain what a product does, and branded content can provide useful information. Buyers may still want to hear what someone with firsthand experience thinks about the problem.

LinkedIn is essentially creating more formal ways for businesses to participate in behavior that's already happening on the platform. Its Thought Leader Ads are another part of that approach. They allow companies to promote posts from individuals, including employees or outside experts, instead of creating a traditional brand ad from scratch.

Expertise Matters More When Content Is Everywhere

There's also a bigger content problem behind this trend. Creating polished marketing content has become much easier to produce en masse (although it still takes human oversight to ensure quality). AI tools can help companies produce posts quickly, which means there's more content competing for attention and a lot of it starts to sound similar.

Someone who can explain what they're seeing in the field, share an informed opinion, or answer a specific industry question gives people a reason to pay attention. The content doesn't need to sound like a carefully polished campaign, and in many cases, it may work better when it doesn't.

For B2B brands, that means some of their most valuable content opportunities may already exist inside the organization.

Your Next Creator Might Already Be on Your Team

Companies don't necessarily need a formal creator program to start experimenting with this approach. Look at the people who already have useful knowledge to share. Maybe that's someone in sales who hears the same customer questions every week. It could be a technical expert who can explain complicated topics without making them more complicated.

Marketing can help those people turn what they know into useful LinkedIn content without trying to make everyone sound like a professional content creator. Outside creators can also make sense when they genuinely know the industry and already have credibility with the audience you're trying to reach.

B2B Marketing Is Getting More Personal

LinkedIn's investment in B2B creator marketing doesn't mean company pages or traditional content are going away. It does suggest that the person delivering the message is becoming a bigger part of how buyers judge it.

For marketers, the opportunity isn't to find someone with a large following and pay them to talk about a product. It's to identify people your audience already has a reason to listen to, then give those voices a meaningful role in your marketing.

Aztek Marketing News Roundup (09/21- 09/25)

Mon, 21 Sep 2026 08:00:00 Z

This week's topics:

ChatGPT Ads Are Turning Into Conversational Sales Experiences
Why Landing Pages Still Matter in an AI-Driven Search World

9/22: ChatGPT Ads Are Turning Into Conversational Sales Experiences

On Sept. 16, OpenAI announced Sponsored Agents, a new digital advertising experience that lets someone move from an ad into a clearly labeled conversation with an AI representative of the advertiser. Users can ask follow-up questions about a product or service and then visit the advertiser’s website when they’re ready to take the next step.

What’s Changing With ChatGPT Ads?

Sponsored Agents are part of a broader expansion of ChatGPT ads. OpenAI is also introducing AI-assisted campaign creation, new creative tools, and integrations with HubSpot and Shopify.

The bigger idea connecting those features is context. Traditional search advertising often starts with a relatively short query. In ChatGPT, users may explain what they’re looking for, compare options, describe constraints, and ask increasingly specific questions.

OpenAI is building its advertising product around those conversations. Its current advertiser guidance says ChatGPT ads can consider conversational intent rather than relying only on keyword matching. That creates opportunities for ads to feel more relevant, but Sponsored Agents take the idea one step further by letting the advertiser participate in the research process.

Conversational Ads Could Change What Happens Before The Click

Consider someone shopping for paint. A normal ad might highlight price, quality, or a promotion and hope one of those messages earns a visit. With a conversational experience, that person might first ask whether that type of paint works well in bathrooms, if it's safe for kids, or if it's easy to apply.

Those questions tell businesses much more about what the customer actually needs. It also changes the role of the click. Instead of clicking just to start researching, someone could arrive at a website after already answering their most important questions.

Better AI Ads Still Depend On Better Marketing Fundamentals

If an AI agent is representing your business, it needs clear information to work with. Customers need to be able to understand what you sell, who it’s for, what makes it different, and what they should do next.

That means useful service and product pages still matter. So do FAQs, accurate product information, clear positioning, and consistent messaging. If the AI experience gives someone one answer while the website gives them another, that’s not a great customer experience. If a prospect asks a basic question and your business doesn’t have a clear answer anywhere, adding AI won’t magically solve the problem.

What Marketers Should Do Next

Sponsored Agents are still in a limited alpha test, so there’s no reason to chase the format just because it’s new. The more practical move is to pay attention to the behavior behind it.

What questions do customers consistently ask before they buy? Does your website answer them clearly? Are your product, service, and brand messages consistent? And when someone is ready to act, is the next step obvious? Those are good questions for digital marketing teams today, whether or not ChatGPT ads ever become a major part of their media mix.

9/24: Why Landing Pages Still Matter in an AI-Driven Search World

AI can introduce someone to your company, but once that person reaches your site, your landing page still has to convince them to keep going. Visitors need to quickly understand what you offer and whether it fits their situation, while also seeing enough proof to trust what you’re saying.

They also need a next step that makes sense for where they are in the buying process. For a B2B company, that could mean detailed service information, technical resources, examples of past work, or answers to questions buyers typically ask before contacting sales. AI search may change how someone discovers your business, but it doesn’t replace the experience they have once they arrive.

Generic Landing Pages Have Less Room to Hide

If someone already received a basic explanation from an AI tool, clicking through to a page that repeats the same generic information isn’t especially useful. Landing pages need to give people something they couldn’t get from the summary that brought them there in the first place.

Show how your approach works, explain what makes an option different, address real customer questions, and give people information that helps them make a decision. As more basic information is handled before the click, the value of what happens after the click becomes more important.

Give Your Landing Pages a Quick Audit

You don’t need to rebuild every landing page because AI search is changing. A good place to start is reviewing the pages you already have and asking whether they’re doing enough for someone who may arrive with more context than they did a few years ago.

Look at a few basics:

Is the page immediately clear? A visitor should be able to tell what you offer, who it’s for, and why it’s relevant without digging through several paragraphs.
Does it add something useful? Look for details an AI summary probably can’t provide, such as examples, technical information, customer proof, pricing context, or specifics about how your process works.
Does the page answer real buyer questions? Sales conversations, customer emails, and search data can all point to questions worth addressing directly.
Is there a clear next step? The CTA should match the visitor’s likely intent, whether that’s contacting sales, requesting a quote, downloading a resource, or exploring a related service.
Is there enough proof? Case studies, testimonials, certifications, project examples, or other relevant evidence can help visitors validate what they learned before reaching your site.
Does it work well on mobile? Pages still need to load quickly, be easy to scan, and make important actions simple on a smaller screen.

The goal is to make sure the page earns the click by giving visitors useful information and a clear path forward.

Measure What Happens After the Click

AI search also makes it harder to judge performance using traffic alone. A decline in organic sessions still deserves investigation, but marketers should also look closely at what the visitors who do arrive actually do. Are they converting? Are they reaching high-value pages? Are they turning into qualified leads or moving further into the buying process?

Landing Pages Still Have a Job to Do

Google says its AI-powered search features are increasing overall search activity and sending billions of clicks to websites each week, even as individual search journeys change. That means landing pages aren’t becoming obsolete because of AI search; they’re becoming responsible for a different part of the customer journey.

Aztek Marketing News Roundup (09/07- 09/11)

Tue, 08 Sep 2026 08:00:00 Z

This week's topics:

Google AI Max Continues to Change How Much Control Advertisers Have Over Search Campaigns
AI Holiday Shopping Is Changing How Brands Get Discovered Before Peak Season

9/8: Google AI Max Continues to Change How Much Control Advertisers Have Over Search Campaigns

Paid search used to reward marketers who were willing to get granular. They picked the keywords, chose the match types, wrote the ads, built the landing pages... the list goes on. Google, however, has been moving away from that level of manual execution for years, but Google AI Max makes the direction especially clear.

Starting in September 2026, eligible Search campaigns using Automatically Created Assets, now called text customization, or campaign-level broad match are being automatically upgraded to AI Max. Google originally planned to include Dynamic Search Ads in that September transition, but pushed the DSA migration to February 2027.

This change isn’t Google suddenly taking every campaign decision away from advertisers, but it does represent a bigger shift in what managing paid search looks like.

What Google AI Max Actually Changes

First things first, AI Max is not a new campaign type. It’s a set of automation features layered onto existing Search campaigns. Those features can expand which searches a campaign reaches, generate customized ad copy and, when enabled, choose a different page from an advertiser’s website when Google believes it better matches the searcher’s intent.

Put those capabilities together, and Google can influence three pretty important parts of a search ad experience:

Who sees the ad
What the ad says
Where that person lands

Control Isn’t Disappearing

It would be easy to frame this as Google taking control away from advertisers. The reality is a little more useful than that. Google AI Max still includes controls around brands, locations, URLs, and search term matching. Advertisers can also turn individual asset optimization features on or off.

Google has also expanded reporting so marketers can see AI Max search terms, assets, and selected landing pages. The bigger change is where control sits. Instead of manually defining every possible execution, marketers increasingly define the goal, provide the inputs, and set the boundaries within which Google’s system operates.

That puts more pressure on the decisions automation can’t make for you, such as:

What actually counts as a valuable conversion?
Which customers matter most?
What claims should your brand make?
Which parts of your website should Google be allowed to use?

Your Website Is Part Of Your Paid Search Strategy

There’s another consequence that’s easy to overlook. As Google relies more heavily on website content to understand campaigns, generate messaging, and select landing pages, the quality of the website matters even more.

Text customization can use landing page and domain content to generate headlines and descriptions. Final URL expansion can send visitors to a different relevant page on the advertiser’s domain when Google predicts it will perform better.

If your service pages are vague, your positioning is inconsistent, or important pages are outdated, Google has weaker material to work with. That reality doesn’t mean every advertiser should hand Google free rein over their website, but it does mean the line between “the ad account” and “the website” keeps getting blurrier.

Better Automation Still Needs Better Inputs

The takeaway here isn’t to turn every AI Max feature on and hope Google figures it out. It also isn’t to reject automation because paid search used to work differently. It’s to recognize that the marketer’s job is moving upstream.

As Google handles more matching, creative variation, and routing, businesses need better conversion tracking, clearer messaging, stronger landing pages, and smarter guardrails. Marketers also need to keep reviewing what the system is actually doing instead of assuming automation equals optimization.

Google AI Max may reduce some of the manual work inside a Search campaign; it doesn’t reduce the need for strategy. If anything, it makes the fundamentals harder to avoid.

9/10: AI Holiday Shopping Is Changing How Brands Get Discovered Before Peak Season

Holiday marketing has always been a fight for attention. Brands plan promotions, increase media budgets, build gift guides, and prepare for the Black Friday rush. This year, there’s another part of the shopping journey: getting into the consideration set before someone ever clicks an ad.

Microsoft’s latest holiday research says 76% of U.S. shoppers surveyed plan to use AI to help plan their seasonal spending. The company describes shoppers using AI-powered experiences to brainstorm, compare, validate, and make decisions about purchases.

That shift doesn’t mean traditional search, paid media, email, or retailer websites suddenly matter less. It just means AI holiday shopping is becoming another layer in an already messy discovery process.

AI Holiday Shopping Can Start Before Traditional Search

Think about how someone might shop for a gift. Instead of searching Google for “best carry-on suitcase,” they could ask an AI assistant for a lightweight carry-on under $250 for someone who travels twice a month and cares more about durability than extra compartments.

That’s a much more detailed request. The AI system can help narrow the criteria, compare options, and create a shortlist before the shopper ever visits a retailer. ChatGPT, for example, can surface and compare products based on factors like budget, preferences, features, reviews, and other constraints a shopper provides. OpenAI has also expanded its shopping tools to use merchant product data and other retail sources when researching products.

Product Information Is Becoming Marketing Information

That shift puts more pressure on something considerably less flashy than AI: good product data. OpenAI allows merchants to provide product feeds so ChatGPT can access more complete and current information about their products. Shopify product data is also integrated into ChatGPT through Shopify Catalog.

Vague product descriptions and incomplete attributes don’t give people, or automated systems, much to work with. “Premium quality for every lifestyle” might sound nice in a brand deck, but it doesn’t tell someone whether a product weighs five pounds, fits in an overhead bin, works outdoors, comes in a particular size, or solves the problem they’re actually researching.

Clear features, accurate pricing, strong descriptions, comparison information, reviews, shipping details, FAQs, and real differentiation help shoppers make decisions. They also give AI systems better context for understanding those products.

This Shift Is Already Sending Some Traffic Back To Retailers

There’s evidence that these behaviors are moving beyond experimentation. Adobe reported that traffic from generative AI tools to U.S. retail websites increased 693.4% year over year during the 2025 holiday season.

Adobe was also careful to note that the overall base of AI-driven traffic remained modest. A huge growth percentage does not mean AI referrals are replacing Google, paid media, email, or direct traffic anytime soon. It does mean enough shoppers are using these tools for product discovery that businesses should probably stop treating the behavior as theoretical.

Holiday Marketing Still Needs The Fundamentals

The temptation with AI holiday shopping will be to turn it into another optimization race: How do we rank in ChatGPT? How do we get recommended by AI? What new tactic do we need?

Brands should first make sure their product feeds are accurate, their website clearly explains what they sell, their product pages answer real buyer questions, and their reviews and trust signals are easy to find. They should also be creating useful content early enough to reach shoppers before the peak November buying period.

The brands that make themselves easier to understand, evaluate, and trust have a better chance of making the shortlist, whether that shortlist is built in a search engine, an AI assistant, or the shopper’s own browser tabs.

Aztek Marketing News Roundup (08/31- 09/4)

Mon, 31 Aug 2026 08:00:00 Z

This week's topics:

ChatGPT Is Becoming a Marketing Platform
Where AI Helps Marketing Teams, and Where It Doesn’t

9/1: ChatGPT Is Becoming a Marketing Platform

ChatGPT took another step toward becoming more than an AI assistant this week. On August 31, the European Commission designated ChatGPT a Very Large Online Search Engine under the EU’s Digital Services Act. The designation applies to services that reach at least 45 million average monthly users in the European Union and brings additional requirements around transparency, algorithmic systems, and risk management.

For U.S. marketers, the specific European regulations are probably not the main takeaway here. The more interesting part is what the designation says about ChatGPT itself. Regulators are increasingly treating ChatGPT like a major search and discovery platform, and when you look at how the product has evolved, that makes sense.

ChatGPT Is Becoming a Marketing Platform

People are already using ChatGPT to research and make decisions about certain products and services. OpenAI has been building more commerce directly into that experience, including richer shopping results that allow users to compare products based on things like price, reviews, and features. Merchants can also provide product data to help ensure that information shown inside ChatGPT is current and complete.

Advertising is becoming part of the picture too. OpenAI began testing ChatGPT ads in the U.S. earlier this year and has since expanded the program internationally. The company says ads remain separate from ChatGPT’s answers, while product recommendations in its shopping experiences are selected independently rather than being sponsored placements.

Put those pieces together and ChatGPT starts looking less like a standalone AI tool and more like a marketing platform where research, discovery, digital advertising, and commerce can increasingly happen in the same environment.

AI Search Is Becoming a Real Discovery Channel

None of this means ChatGPT has replaced Google, or that businesses should suddenly rebuild their marketing strategy around AI search. It does, however, mean marketers should pay attention to how discovery behavior is changing. Someone researching a new software platform, comparing home improvement products, or trying to understand which professional service they need may increasingly start that process with an AI conversation rather than a traditional search results page.

For brands, that creates a familiar question in a new environment: What information does the platform have to work with when someone asks about your category? That is where the AI conversation quickly comes back to some very traditional marketing fundamentals.

Clear website content, accurate product and service information, brand credibility, and consistency across your digital presence matter. For retailers, structured product data is becoming especially relevant as AI platforms build more sophisticated shopping experiences.

What U.S. Businesses Should Do Now

Start by looking at the questions customers ask when they are researching your products or services. Consider whether your website actually answers those questions clearly. You'll also want to make sure important information about your company, offerings, and products is accurate and easy to understand.

Businesses experimenting with ChatGPT advertising should also keep paid visibility separate from organic visibility. Buying an ad does not replace the work required to make your brand useful and relevant when someone asks an AI system for an answer or recommendation.

Focus on the Fundamentals

The ChatGPT marketing platform is still taking shape, and there will likely be plenty of new features, experiments, and hype along the way. For now, businesses do not need to chase every development. The smarter move is to make sure their brands are clear, useful, and trustworthy wherever customers might discover them.

9/3: Where AI Helps Marketing Teams, and Where It Doesn’t

AI has already worked its way into the day-to-day marketing process for a lot of teams. It can speed up research, help generate ideas, analyze information, create first drafts, and handle work that used to take a surprising amount of manual effort.

The problem starts when saving time turns into handing over responsibility. Just because AI can perform some version of a marketing task doesn’t mean it should own the entire process. A more useful way to think about AI is to look at where it can remove friction from the work and where human judgment still makes the difference.

Where AI in Marketing Actually Helps

AI works especially well when the task is clearly defined and the person using it already has some idea of what a good result should look like. Research is a good example. Instead of starting with a blank page and a pile of sources, people can use AI to summarize information, compare themes across documents, or identify questions that deserve a closer look. It doesn’t necessarily replace the research itself, but it can make getting oriented much faster.

Brainstorming is another natural fit. AI can help explore campaign angles, headline options, content topics, or different ways to explain a complicated idea. Not every suggestion will be useful, and that’s fine. The benefit often comes from having more starting points to react to.

Some of the biggest time savings can come from even simpler work. Think about summarizing meeting transcripts, organizing notes, reformatting information, or adapting existing copy for another channel. These are tasks that take time but don’t always require deep strategic thinking.

That time savings is where AI can be especially valuable. Saving 20 minutes on a repetitive task isn’t exactly sexy on its own, but giving that time back to someone who can spend it solving a harder problem is.

Where AI Still Needs a Human in the Room

AI's limitations become more obvious when it moves from supporting a decision to making one. Human judgment still matters most when:

Developing marketing strategy. AI can suggest tactics, but it may not understand important business context, such as sales challenges, upcoming launches, or leadership priorities.
Maintaining brand voice. AI can write in an established voice, but it can’t reliably determine what makes that voice distinct without clear direction.
Reviewing accuracy. AI can misread sources, miss context, or produce false information. The person publishing the work is still responsible for checking the final result.

Use AI to Remove Friction, Not Responsibility

Yes, marketing teams should experiment with AI. There are worthwhile opportunities to move through routine work faster, reduce repetitive tasks, and spend less time staring at a blank page. Used correctly, AI can help teams organize and create without taking over the parts of marketing that require experience and judgment.

The goal, however, shouldn’t be finding the maximum amount of marketing work that can be handed over to a model. Moving quickly is only helpful when the work is relevant and connected to a larger business objective. Automating a weak process or producing more generic content doesn’t make a marketing team more effective.

A better goal is figuring out where AI makes marketers more capable while keeping people responsible for the decisions that shape the work. That means using AI to reduce friction while marketers continue to set the strategy, understand the audience, protect the brand, and review what ultimately gets published.

Aztek Marketing News Roundup (08/24- 08/28)

Mon, 24 Aug 2026 08:00:00 Z

This week's topics:

Google AI Overviews Lawsuits Put Search Traffic Tensions in the Spotlight
Google’s New Preferred Sources Button Gives Publishers a New Way to Build Visibility

8/25: Google AI Overviews Lawsuits Put Search Traffic Tensions in the Spotlight

Google AI Overviews were designed to give searchers faster answers without requiring them to open website after website to find them. That convenience is now at the center of a growing fight between Google and the publishers whose content helps make those answers possible.

Companies including Chegg and Penske Media have filed lawsuits arguing that Google is using its dominance in search to keep more users on Google instead of sending them to the websites that created the underlying content. Google disputes those claims and has asked the court to dismiss both cases. As of August 25th, those motions remain pending.

The Google AI Overviews lawsuits are still developing, but they highlight a problem marketers have already been watching closely: what happens when strong search visibility no longer produces the same amount of website traffic?

Why Google AI Overviews Lawsuits Are Getting Attention

Publishers have been warning about declining search traffic since AI Overviews began appearing more prominently in Google results. A recent Pew study found that users clicked a traditional search result during 8% of visits where an AI summary appeared, compared with 15% when no AI summary was shown. Links included directly within the AI summary received clicks in just 1% of visits.

Traffic patterns vary considerably by website, so AI Overviews aren’t necessarily responsible for every decline. Google also says its total organic click volume has remained relatively stable and argues that the clicks it sends are becoming more valuable. Still, some publishers are seeing substantial changes. Semrush data cited by Nieman Lab showed search traffic falling more than 40% for certain publications between June 2025 and June 2026, while other publishers actually gained traffic.

Regulators Are Getting Involved Too

This debate isn’t limited to U.S. courtrooms. In June, the U.K. Competition and Markets Authority introduced new requirements giving publishers more control over whether their content can be used in Google’s generative AI search features. The rules also require clearer attribution and more detailed information about how publisher content performs within AI-powered results.

For publishers, that creates a meaningful choice over how their content participates in AI results.

What Does This Mean for Marketers?

Most marketers aren’t preparing to sue Google, but the underlying traffic problem is still relevant. Start by looking beyond rankings. A page can hold a strong organic position while receiving fewer clicks if an AI Overview answers much of the query directly. Search Console data can help identify pages where impressions remain healthy while click-through rates are slipping.

It’s also another reason to invest in channels you have more control over. Email subscribers and direct traffic become increasingly valuable when search platforms can change how much traffic they send without changing where you rank.

Content quality matters here too. Original research, genuine subject-matter expertise, and information people can’t easily get from a short summary give searchers more reason to visit the source.

Search Visibility Is No Longer the Whole Story

The Google AI Overviews lawsuits won’t settle the future of search overnight. What they do show is that the relationship between search engines and content creators is being renegotiated in real time. For marketers, the lesson isn’t to abandon SEO; it’s to stop treating rankings and traffic as the same thing.

8/27: Google’s New Preferred Sources Button Gives Publishers a New Way to Build Visibility

Google has already been giving Search users more control over which websites they want to hear from. Now, publishers have a much easier way to encourage them to make that choice. On Aug. 20, Google introduced an embeddable Google Preferred Sources button that websites can add directly to their pages. When someone clicks it, they can select the site as a preferred source and then return to the content they were viewing.

It’s a small addition to a website, but it could become a useful audience-building tool as Google continues personalizing both traditional and AI-powered search experiences.

What Does the Google Preferred Sources Button Do?

Preferred Sources allows Google users to identify websites they want to see more often. In Top Stories, content from those sites becomes more likely to appear when it’s timely and relevant. Google also highlights preferred sources with a badge within AI Overviews and AI Mode.

Before this, users generally had to manage those preferences within Google. The new button gives publishers a direct way to promote the feature from their own websites.

Implementation is also relatively simple. Google provides a standard button that can be added with two lines of HTML, along with options for custom designs or a direct link to the Preferred Sources settings.

Why Preferred Sources Are Worth Watching

The bigger story here is what this update says about how discovery in Google is changing. Search visibility has traditionally depended heavily on Google deciding which result deserves attention. Preferred Sources adds a user-controlled signal to that equation. Someone who already values your content can essentially tell Google, “I want to see more from this website.”

There’s evidence that signal can translate into traffic. Google says people are twice as likely to click through to a website after selecting it as a Preferred Source. More than 345,000 unique sources had already been selected as of May 2026. For publishers and brands investing heavily in useful content, that creates another opportunity to turn a one-time reader into a repeat visitor.

What Marketers Should Do Next

Any website that publishes content can potentially be eligible, so this isn’t limited to major news organizations. B2B blogs and other frequently updated resource centers can participate too.

That doesn’t mean every website needs to drop the button into its header tomorrow. Think about where the request makes sense. An article page, newsletter signup confirmation, or another high-engagement area could be a natural place to invite someone to see more of your content on Google.

The messaging matters too. Instead of asking visitors to “make us a Preferred Source,” explain what they get from doing it. Something as simple as “See more of our articles on Google” makes the benefit clearer. Most importantly, keep earning the preference. A button may help people tell Google they value your website, but it can’t make generic content more useful.

Another Reason to Build an Audience, Not Just Rankings

The Google Preferred Sources button doesn’t replace SEO, and it isn’t a shortcut to better rankings. What it does provide is another way for strong content to build momentum after someone discovers it.

As Google Search becomes more personalized, earning a reader’s preference could become increasingly valuable. The opportunity isn’t simply to show up once; it’s to create content good enough that someone actively asks Google to show them more.

Aztek Marketing News Roundup (08/17- 08/21)

Mon, 17 Aug 2026 08:00:00 Z

This week's topics:

AI Search Goes Mainstream
Email Deliverability: Why It’s More Important Than Ever

08/18: AI Search Goes Mainstream

AI search is no longer something marketers can write off as an interesting experiment. ChatGPT Search, Google’s AI-powered search experiences, and other conversational tools are giving people new ways to research questions, compare options, and discover businesses.

That doesn’t mean Google is disappearing, and it also doesn’t mean businesses need to abandon their SEO strategy in favor of optimizing entirely for AI. What it does mean, though, is the way people find and evaluate information online is becoming more varied, and businesses need to ensure their digital presence can keep up.

AI Search Changes How People Find Answers

Traditional search typically gives someone a list of results and leaves them to decide which links are worth exploring. AI search can now do that work for them, pulling information together into a conversational response and allowing the user to ask increasingly specific follow-up questions.

The path from a question to a business is becoming less predictable now that someone researching a product or service can learn about a company before ever visiting its website. AI search is still much smaller than traditional search, so this isn’t the moment to redirect an entire marketing strategy toward it, but it is another discovery channel worth understanding.

Good SEO Still Matters in AI Search

For all the talk about optimizing for AI, many of the fundamentals look familiar. OpenAI has documented technical requirements businesses should consider if they want their content to be eligible for inclusion in ChatGPT Search, including allowing its search crawler to access their websites. Technical accessibility, however, is only part of the picture.

Businesses still need websites that clearly explain who they are, what they offer, and why someone should trust them. Useful content that answers real customer questions still matters, and so does crawlability, clear site structure, and accurate information. In other words, AI search creates some new considerations, but it doesn’t make good SEO obsolete.

Your Website Still Has a Job to Do

There’s another important distinction between being discovered and winning business. An AI tool might introduce someone to your company, but a potential customer may still visit your website to validate that recommendation. They might look for service details, examples of your work, pricing information, reviews, or basic evidence that your business is credible. If that experience is confusing or unconvincing, appearing in an AI answer won’t solve the problem.

That’s why businesses shouldn’t respond to AI search by chasing shortcuts. Start with the foundation: a technically sound website, clear positioning, useful content, and real evidence of expertise. Then make sure AI search tools can access and understand that information.

Pay Attention Without Chasing the Hype

AI search is worth watching because customer behavior is changing, not because traditional search suddenly stopped working. Businesses should begin monitoring AI referral traffic where possible, review how their brands appear in conversational searches, and pay closer attention to the questions customers actually ask. At the same time, they should keep investing in traditional SEO and the website experience that ultimately turns visibility into action.

Email Deliverability: Why It’s More Important Than Ever

Email marketing has never been the flashiest channel in the room, but it keeps earning its spot in the budget because it works. Industry research continues to show strong returns from email, with many companies reporting anywhere from $10 to more than $50 back for every dollar spent.

None of that matters, though, if your emails never reach the inbox. Email deliverability is what determines whether your carefully planned campaign actually reaches subscribers or gets bounced, filtered into spam, or blocked entirely. You can spend hours improving subject lines and polishing creative, but deliverability problems can quickly undermine all of it.

Five Email Deliverability Fundamentals

1. Keep Your Email List Healthy

A bigger list isn’t automatically a better list. Old addresses, hard bounces, and subscribers who haven’t engaged in months can hurt your sender reputation over time. Regularly clean your list and remove addresses that consistently bounce. Avoid purchased lists altogether. Most importantly, make sure people actually asked to hear from you. Permission is still one of the strongest foundations for good email performance.

2. Get Email Authentication Right

SPF, DKIM, and DMARC aren’t exactly exciting marketing topics, but they’re increasingly difficult to ignore. These authentication standards help email providers verify that your messages really came from your organization. Without them, your emails are more likely to look suspicious, and your domain is more vulnerable to spoofing.

If your team hasn’t reviewed its authentication setup recently, this is a good place to start before experimenting with more advanced deliverability tactics.

3. Pay Attention to Engagement

Email providers notice how recipients interact with your messages. Consistently sending to people who never open or click can eventually send the wrong signals about the quality of your email program.

Segmenting audiences by recent activity can help. You may also want to establish a sunset policy that suppresses subscribers after an extended period of inactivity rather than continuing to email them indefinitely.

4. Don’t Ignore the Email Itself

Technical setup is important, but content still plays a role. Image-heavy designs, oversized files, or overly aggressive sales language can cause problems. Poorly built emails can also create a frustrating experience even when they successfully reach the inbox.

Keep layouts readable, optimize images, and test messages across common email clients before sending. Small details like useful preheader text and descriptive alt text can improve the experience without overcomplicating production.

5. Build a Consistent Sending Rhythm

Going silent for weeks and suddenly sending a huge campaign can look unusual to mailbox providers. A more predictable cadence helps establish normal sending behavior.

Testing should be part of that rhythm too. Watch bounce rates, spam complaints, and inbox placement so you can catch changes before they become larger problems.

Connect Deliverability to Email Performance

Deliverability metrics shouldn’t live in a separate technical report nobody looks at. Compare inbox placement and bounce rates with revenue per send, conversions, or whatever business outcome your email program is supposed to produce.

If campaign performance suddenly drops, don’t immediately assume the copy or offer is the problem. Check whether fewer emails are reaching people in the first place. A quarterly list cleanup and authentication review can prevent a lot of headaches. It’s also worth revisiting your deliverability setup whenever you change email platforms, domains, or sending infrastructure.

Aztek Marketing News Roundup (08/10- 08/14)

Tue, 11 Aug 2026 08:00:00 Z

This week's topics:

Open Astra Pause: What Businesses Can Learn From AI’s Safety Limits
Claude Is Adding Invisible Watermarks to AI-Generated Content

08/11: OpenAI Astra Pause: What Businesses Can Learn From AI’s Safety Limits

OpenAI recently paused parts of its internal work on Astra, an upcoming model that shows major advances in agentic coding and cybersecurity. The move is less about a single model and more about a broader signal: frontier AI is getting close enough to meaningful offensive cyber capability that even general-purpose systems can’t be treated as neutral tools anymore.

According to OpenAI, early evaluations were strong enough that the company couldn’t rule out Astra reaching its highest cybersecurity risk category, called Critical. That threshold includes models capable of developing zero-day exploits against hardened systems or executing complex cyberattacks with limited human direction. As a result, OpenAI is tightening security requirements and restricting certain development and testing environments before work on Astra can continue.

The Astra Pause Is AI Governance in Practice

AI safety conversations can get abstract quickly. This Astra situation makes them much more concrete. OpenAI has a system called its Preparedness Framework, which is a way of checking how powerful and potentially risky a new AI model could be before it’s widely released. Think of it like a safety rating system. Some models are considered “high risk” in certain areas, but still manageable with the right safeguards.

Previous models, including GPT-5.6 Sol, were assessed at the lower High cybersecurity threshold. Astra is the first upcoming model OpenAI has publicly said could potentially cross into Critical territory. That doesn’t mean businesses should suddenly stop using AI, but it does reinforce something we’ve been saying for a while: more capable AI also requires more intentional controls around how it’s used.

What Businesses Should Take From OpenAI’s Astra Decision

The bigger lesson isn’t that AI has become “too powerful.” It’s that organizations need boundaries that evolve along with the technology. Start by understanding where AI already exists inside your workflows, including third-party software that may use models behind the scenes. Vendors can change their underlying technology, so teams should know what systems have access to sensitive information and what happens when those systems change.

It also makes sense to keep meaningful human checkpoints around higher-risk automation. AI can help analyze information, draft work, identify patterns, or speed up repetitive processes without giving it unrestricted control over important systems.

Stronger AI Needs Stronger Guardrails

Astra is a useful reminder that AI adoption and AI governance can’t live in separate conversations. As models become more capable, businesses will need to periodically revisit what their tools can access, where human review belongs, and whether yesterday’s safeguards still make sense.

You don’t need a frontier cybersecurity model to start having those conversations. Waiting until something goes wrong is a much harder way to figure it out.

8/13: Claude Is Adding Invisible Watermarks to AI-Generated Content

AI-generated content may be getting easier to trace, even when there’s no obvious label telling you AI was involved. Anthropic, the company behind Claude, is rolling out technology designed to mark content created by its newer AI models. The markings are intended to be invisible to users but detectable by machines, creating a way to identify content that originated with Claude.

The move comes as AI companies face growing pressure to make AI-generated material more transparent and traceable. For businesses already using AI to write, edit, brainstorm, or create digital assets, this is worth knowing about, but it’s probably not a reason to rethink your entire AI strategy.

What Is Claude’s Invisible Watermark?

Anthropic wants content generated by Claude to carry information about where it came from without sticking a giant “MADE BY AI” label on everything. For text, reporting indicates that Claude’s watermark is embedded into the output itself rather than added as visible text or traditional file metadata.
It’s designed to remain imperceptible to readers and survive actions such as copying and pasting, as well as at least some editing. Anthropic has also said it plans to make third-party verification possible.

For supported images and files, Anthropic is using signed provenance metadata based on C2PA, an open standard designed to provide information about a digital asset’s origin and editing history.

The rollout is tied in part to new transparency requirements under the European Union’s AI Act, but reporting indicates Anthropic is applying the approach more broadly rather than limiting it to European users.

Why AI Watermarking Matters

The bigger story isn’t really the watermark itself. It’s the growing push for AI-generated content transparency.

For the past few years, much of the conversation has focused on whether someone can detect AI content after the fact. Watermarking takes a different approach. Instead of asking another tool to guess whether something looks AI-generated, the AI provider intentionally adds a signal when the content is created.

AI detection tools have never been a particularly clean answer to questions of authorship. A detectable Claude signal could indicate that Claude was involved with a piece of content, but that doesn’t automatically tell you how it was used. There’s a big difference between asking AI to produce a finished article and asking it to clean up a paragraph you already wrote.

What Should Businesses Do?

For most businesses, the practical takeaway is not “stop using Claude.” AI is already part of plenty of legitimate marketing, research, and content workflows. A watermark doesn’t suddenly make those uses less useful. What it does reinforce is the importance of having clear standards around how your team uses AI and how much human review happens before something reaches a customer.

Businesses should know which AI tools are being used, where they’re being used, and what their policies are for reviewing and disclosing AI-assisted work. There are still open questions around how reliably these watermarks will survive substantial editing, how verification will work in practice and how platforms will ultimately use these signals.

Anthropic’s move won’t settle the broader debate over AI-generated content overnight, but it does point toward where things are heading: AI content may become easier to trace, and businesses should assume transparency around AI use will matter more, not less.

Aztek Marketing News Roundup (08/03- 08/07)

Tue, 04 Aug 2026 08:00:00 Z

This week's topics:

Google Ranking Volatility Spiked Again

08/04: Google Ranking Volatility Spiked Again

Google search rankings experienced another round of turbulence between Saturday, August 1, and Monday, August 3. Several third-party tracking tools recorded increased movement, while website owners reported unexpected changes in rankings and organic traffic.

Google hasn’t confirmed an algorithm update connected to the spike. The company’s Search Status Dashboard shows that its most recent confirmed ranking event was the June 2026 spam update, which followed the May 2026 core update. That means the latest movement may be significant, but it’s still unconfirmed.

Before rewriting pages or changing your SEO strategy, take a closer look at what actually changed on your site.

What Happened Between August 1 and 3?

Search Engine Roundtable reported that volatility began on August 1 and increased through August 3. Its tracking aggregator showed movement across tools such as Semrush, MozCast, Advanced Web Ranking, and Similarweb. SEO forums also included reports of rankings shifting throughout the weekend.

This wasn’t the first period of unexplained movement in recent weeks. Search Engine Roundtable also documented unconfirmed volatility around July 18, July 24, and several earlier dates.

That pattern doesn’t prove Google launched a specific update. Ranking tools monitor different keywords and markets, so they won’t always reflect what’s happening to your website. They’re most useful as an early warning that broader changes may be underway.

Why an Unconfirmed Update Can Still Affect Your Business

An update doesn’t need an official name to create problems. A meaningful drop in rankings can reduce website traffic and lead volume regardless of whether Google publishes an announcement. The harder part is determining what caused the change.

Google notes that organic traffic can fall because of an algorithmic adjustment, a technical issue, changing search interest, or a problem affecting only certain pages. That’s why a volatility report shouldn’t automatically trigger a sitewide content rewrite. It should prompt you to check your own data and look for patterns.

How to Check Whether Your Website Was Affected

Start with the Performance report in Google Search Console. Once the data is available:

Compare August 1 through August 3 with the previous Saturday-through-Monday period.
Review clicks, impressions, average position, and click-through rate.
Sort your pages and queries by the largest losses.
Check whether the decline affects one section of the site or appears across most pages.
Look at device, country, and search appearance data for additional patterns.

If clicks dropped while impressions remained relatively steady, review average position and click-through rate. Your rankings may have slipped, or the appearance of the search results may have changed.

If impressions and clicks both fell, investigate whether the affected pages are still indexed. You should also check for technical changes, recent website updates, tracking problems, and seasonal shifts in demand.

What to Do After You Find a Drop

A few days of movement isn’t enough to justify major changes. Rankings can shift while Google’s systems recalculate results, even when no formal update has been announced.

Document the pages and queries that changed, then continue monitoring them. If the decline persists, review the affected content with a critical eye.

Look for pages that:

Repeat information already available on competing websites
Provide broad answers without useful examples
No longer match the intent behind the search
Contain outdated information
Were created primarily to target a keyword rather than help the reader

Google recommends focusing on helpful, reliable content created for people. It also encourages site owners to make it clear who produced the content, how it was created, and why it exists. That doesn’t mean every page needs a lengthy author biography or a complete rewrite. It means your content should offer enough expertise, context, or practical value to justify its place in the search results.

Respond to the Data, Not the Panic

Ranking volatility is becoming a regular part of managing organic search performance. Third-party sensors can help you recognize when widespread movement is occurring, but they can’t tell you whether your site has a serious problem. Use volatility reports as a reason to investigate, not as proof that your website was penalized. Check your own performance, rule out technical causes, and give the data enough time to show a clear pattern.

Aztek Marketing News Roundup (07/27- 07/31)

Tue, 28 Jul 2026 08:00:00 Z

This week's topics:

The Rising Cost of “Free” AI Tools
What Is Google AI Max?

07/28: The Rising Cost of “Free” AI Tools

Microsoft recently announced that Copilot’s Deep Research feature will no longer be available to free users after August 18, 2026. Continued access will require a Microsoft 365 Premium plan priced at $30 per user each month. For businesses that have started building Deep Research into their workflows, the change turns a useful free feature into a new operating expense.

Microsoft isn’t the only company following this model. Google, OpenAI, Adobe, Slack, and other software providers have introduced usage limits, premium tiers, and paid access to features that were previously free or widely available.

That shift shouldn’t come as a surprise. Many AI tools were initially priced to encourage adoption, give users time to experiment, and help vendors understand which features people valued most. Now that businesses have started relying on those features, providers have more room to charge for them.

Why AI Tool Costs Will Keep Changing

Generative AI is expensive to operate. Model providers have to pay for computing infrastructure, energy use, product development, security, and ongoing improvements. Free access can help attract users, but it isn’t always sustainable as usage grows.

Vendors also have a growing amount of information about how customers use their products. They can see which features become part of everyday workflows and which ones people only test occasionally. The tools that save the most time or become difficult to replace are often the strongest candidates for premium pricing.

Businesses should expect more AI features to move between plans, gain usage caps, or become separate add-ons. A tool that fits comfortably within the budget today may look different by the next renewal.

The Budget Impact Adds Up Quickly

A single subscription might not seem significant. The cost becomes harder to ignore when licenses are purchased across an entire department. A 50-person team paying $30 per user each month would spend about $18,000 per year. Adding paid AI features within Slack, Adobe, ChatGPT, or other platforms can quickly create another five-figure expense.

Finance teams are likely to ask a reasonable question before approving that spend: What measurable value is the company receiving? When AI tools were free, teams could justify experimentation with broad claims about efficiency. Rising AI tool costs require more specific answers. Businesses need to understand whether a tool is reducing production time, improving the quality of the work, or helping the organization generate more revenue.

How to Prepare for Rising AI Tool Costs

Businesses don’t need to abandon AI tools because prices are increasing, but they do need a more disciplined approach to purchasing and managing them.

Create an AI tool inventory. Document which platforms employees are using, what they use them for, and whether the tools are free, paid, or included in another subscription. Use current pricing to estimate the annual cost if free features become paid.
Measure value before expanding access. Start with a smaller group of users and compare their performance against an established baseline. Useful measurements might include time spent drafting content, revision cycles, or cost per completed deliverable.
Review licenses regularly. A tool may be valuable for one role and unnecessary for another. Usage reports and employee feedback can help teams remove inactive licenses before renewal.
Keep alternative tools in view. Free and lower-cost options may not offer every feature, but they can reduce dependence on a single provider. Teams should understand which workflows can move elsewhere if pricing changes.
Document important processes. Prompts, workflows, and approval steps shouldn’t live only inside one platform. Keeping that information accessible makes it easier to switch tools without rebuilding the process from scratch.

Treat AI Like Any Other Software Investment

The free phase of generative AI was never going to last forever. As these products mature, vendors will continue testing new pricing models and charging more for the features businesses use most.

Smart teams won’t assume every AI subscription is worth keeping. They’ll forecast the expense, measure the outcome, and review whether each tool still supports the work it was purchased to improve. AI can still create meaningful value; businesses just need to make sure that value grows along with the bill.

07/29: What Is Google AI Max?

Google AI Max is designed to help advertisers reach searches that may not match the exact keywords in their campaigns. Instead, it uses Google’s AI systems to understand what someone is trying to accomplish and determine whether an advertiser’s products, services, or landing pages are relevant.

Google says AI Max is already being used in more than 500,000 advertiser accounts. The company also reports that advertisers using it have seen an average 15% increase in conversions at a similar return on ad spend.

Why does it matter? Google Ads is continuing to move away from matching ads through carefully constructed keyword lists alone. It’s relying more heavily on the information advertisers provide through their websites, product feeds, conversion tracking, and ad creative.

Why Google Is Moving Beyond Keywords

People don’t always search using predictable phrases. They may describe a problem, ask a detailed question, or refine their search several times before making a decision. A traditional keyword campaign can miss some of those searches, especially when the wording doesn’t closely match the advertiser’s keyword list. AI Max attempts to close that gap by looking beyond the individual words in a query and evaluating the likely intent behind it.

For advertisers, that can create opportunities to reach relevant audiences they may not have anticipated. It can also make campaign performance more dependent on the quality of the information Google has available. A clear landing page gives the system more context. Accurate product titles help it understand what’s being sold. Strong conversion tracking tells Google which actions are actually valuable to the business.

More Reach Comes With Less Visibility

The potential downside is familiar to anyone who has worked with Google’s more automated campaign tools. As Google takes greater control over targeting and matching, advertisers may have less visibility into exactly where their ads appeared.

AI Max reporting focuses more on search themes and broader performance insights than complete lists of individual search queries. That can make it harder to determine whether the campaign is reaching entirely new customers or simply capturing traffic that would have converted through an existing campaign.

Advertisers should pay close attention to more than the conversion total. Lead quality, revenue, landing-page engagement, and branded search activity can help show whether AI Max is generating meaningful growth.

How to Test Google AI Max

AI Max is better treated as a controlled experiment than an immediate replacement for existing search campaigns.

Start with a limited budget and a clearly defined product, service, or audience segment. Avoid making major changes to your other campaigns during the test period, since this can make it difficult to compare results.
Before launching, review the information Google will use to understand your business. Make sure product feeds are accurate and landing pages clearly explain what you offer. Ensure conversion tracking distinguishes between actions that look promising and outcomes that actually contribute to revenue.
If you have a longer sales cycle, import qualified lead data or offline sales data into Google Ads. Without this, the system may optimize toward form submissions that do not turn into customers.
Review campaign insights regularly during the test. Watch for irrelevant search themes or weak landing-page performance, as these may indicate issues with page content, product descriptions, or campaign settings.

What AI Max Means for Search Advertising

AI Max doesn’t mean keywords are suddenly irrelevant. Keywords can still provide useful direction and control, particularly in campaigns with narrow audiences or highly specific services. What is changing is the role keywords play. They’re becoming one of several signals Google uses rather than the only foundation for determining when an ad should appear.

Advertisers will need to spend less time trying to predict every possible search variation and more time improving the information behind their campaigns. Clear website content, reliable conversion data, and useful creative assets will have a larger influence on whether Google’s automated matching produces good results.

AI Max may help businesses uncover demand that keyword targeting misses. It may also create inefficient spending when the system doesn’t have enough context. Starting with a focused test gives advertisers a chance to evaluate that balance without handing over the entire budget.

Aztek Marketing News Roundup (07/20 - 07/24)

Tue, 21 Jul 2026 08:00:00 Z

This week's topics:

07/21: ChatGPT Ads: Big Ambitions, Bigger Questions for Marketers
07/23: Instagram Reels Ranking in 2026: Why Watch Time and DM Shares Matter More
07/24: AI Influencers on TikTok Shop: What Brands Gain and What Trust They Risk

07/21: ChatGPT Ads: Big Ambitions, Bigger Questions for Marketers

ChatGPT ads are quickly evolving from an experiment into a legitimate media channel, but they’re still far from a must-have in most marketing budgets. The core appeal makes sense. Ads appear directly inside conversations where users may already be researching products or comparing options.

That access to high-intent moments could become valuable over time. For now, though, marketers need to weigh that potential against limited targeting, incomplete measurement, and ongoing brand safety concerns.

What ChatGPT Ads Look Like

ChatGPT ads show up as sponsored answers or small cards beneath the chatbot’s response. The platform limits placements to one ad per conversational turn, creating a cleaner experience than traditional search or display environments.

The format launched earlier this year and is now available in several countries, with a self-service Ads Manager for U.S. advertisers. From a buying perspective, it feels closer to paid search than social media.

The opportunity lies in timing. Users often come to ChatGPT with specific questions or problems, meaning ads can reach them earlier in the research process. While this doesn’t replace search advertising, it introduces another place where high-intent discovery can happen.

Big Revenue Goals, Early-Stage Reality

OpenAI has reportedly set ambitious revenue targets for ChatGPT ads, projecting billions in near-term revenue and much larger growth over time. External forecasts are far more conservative, reflecting how early the market still is. Audience size alone doesn’t make a mature ad platform. Marketers also need reliable targeting, clear reporting, and confidence in where their ads appear. Those systems are still developing.

Early Results Show Promise

Some early advertisers have reported strong click-through rates and efficient acquisition costs, with a few claiming performance that rivals or exceeds display campaigns. These results are encouraging but should be viewed cautiously. ChatGPT’s focused environment may naturally drive higher engagement, and comparisons to display don’t fully capture differences in campaign goals or audience targeting.

Key Limitations to Know

Despite early momentum, several gaps remain:

Limited targeting: There are no lookalike audiences or detailed demographic filters, and custom audiences require large datasets. This makes the platform harder to use for smaller or niche advertisers.
Incomplete measurement: New tools like a tracking pixel and Conversions API improve visibility, but reporting is still aggregated. Advertisers can’t see the exact queries that triggered ads or fully map the user journey.
Brand safety concerns: While keyword exclusions exist, advertisers don’t have full control over conversational context, which can be a concern for regulated or risk-sensitive brands.

What Needs to Improve

For ChatGPT ads to become a core channel, three areas need to mature:

Targeting: More flexible audience options and lower thresholds for custom audiences.
Attribution: Better insight into assisted conversions and incremental impact.
Brand safety: Greater transparency and control over where ads appear.

Until then, most marketers should treat ChatGPT ads as a test channel rather than a primary investment.

What Marketers Should Watch

ChatGPT ads offer a clean user experience and access to potentially valuable intent, but the platform is still in its early stages. Marketers should focus less on bold revenue projections and more on practical improvements in targeting, attribution, and control.

At the same time, brands can prepare by strengthening their own assets. Clear messaging, helpful landing pages, and structured product information will matter more as conversational advertising evolves.

The bigger shift isn’t just about ChatGPT. It’s that conversational platforms are starting to monetize the questions people ask while making decisions. For now, ChatGPT ads are best viewed as a promising experiment that is worth testing, but not yet ready to replace proven channels.

07/23: Instagram Reels Ranking in 2026: Why Watch Time and DM Shares Matter More

Views and likes show initial interest, but they don’t explain why Instagram keeps distributing a Reel. What matters more is what people do after they see it.

Instagram head Adam Mosseri has highlighted three key metrics:

Average watch time
Likes per reach
Sends per reach

Watch time shows if the content holds attention. Sends signal whether it’s worth sharing.

Instagram Is Looking Beyond Visible Engagement

Instagram hasn’t introduced a brand-new algorithm, but its guidance makes one thing clear: not all engagement is equal. Likes still matter, especially with existing followers, but private shares carry more weight because they show intentional value. AKA, someone thought the content was worth sending directly to another person.

That aligns with user behavior. Messaging is now the primary way people share content on Instagram, with billions of Reels reshared daily. The TL;DR: a Reel can get plenty of likes without earning broader distribution.

Why DM Shares Expand Reach

Liking is quick. Sending takes effort. That extra step makes sends-per-reach a strong signal of relevance beyond the current viewer. Mosseri has noted that sends are especially important for reaching non-followers.

Shareable content doesn’t need to be viral or entertaining. It just needs to be useful or relatable—something that solves a problem or captures a shared experience.

Where Saves Fit

Saves aren’t one of Instagram’s top stated ranking signals, but they still matter. People save content they want to revisit, whether that be tutorials, comparisons, checklists, etc. A high save rate suggests lasting value, not just quick engagement. For educational or B2B brands, this can be especially important.

Create Reels Worth Sharing or Saving

You don’t need to force engagement prompts. Instead, build shareability into the content:

Start with value. Hook viewers immediately.
Solve a clear problem. Focus on one specific need.
Make it reusable. Give viewers something worth revisiting.
Create natural share moments. Content should remind viewers of someone else.

Loops can help with watch time, but clarity and usefulness matter more.

Measure What Matters

Don’t rely on totals alone. Look at engagement relative to reach:

Traditional Metric	Better Metric
Total views	Average watch time
Total likes	Likes per reach
Total shares	Sends per reach
Follower growth	Non-follower reach
Total saves	Saves per reach

There are no universal benchmarks, so compare performance against your own past results.

Test for Real Engagement

Review which Reels drive higher watch time, sends, or saves. Look for patterns in topics and structure. Then iterate. Improve the hook, tighten the message, or refine the example. Don’t rely on reposting trends alone.

Strong organic performance can also inform paid creative, even if it doesn’t guarantee results.

Focus on Value, Not Just Visibility

Watch time and private sharing reinforce a simple idea: visibility isn’t the same as value. Reels perform best when people watch closely, save them, or send them to others. Instead of chasing metrics, create content people actually want to pass along.

07/24: AI Influencers on TikTok Shop: What Brands Gain and What Trust They Risk

TikTok Shop works because people trust people. AI influencers challenge that by removing the human element. Synthetic creators can quickly produce product demos, testimonials, and shoppable videos at scale, and some sellers are already generating hundreds of videos for the same products using AI tools.

The upside? Sure, you can pump out more content faster. The impact on trust? That is less obvious.

AI Makes TikTok Shop Content Easier to Scale

Early TikTok Shop success came from creators testing formats and learning what drove sales. That process took time and resources. AI compresses that timeline. Sellers can generate multiple hooks, swap avatars, tweak voiceovers, and publish variations in minutes.

This is especially useful during peak shopping periods when brands need volume quickly, but it also means users may see many near-identical videos from seemingly different creators.

The Business Case Goes Beyond Cost

AI reduces production friction. No shipping products, coordinating creators, or reshooting content. Brands also gain tighter control over messaging. That control can backfire, though. Real creator content works because it feels lived-in. It's specific, imperfect, and personal. Highly polished, repetitive AI videos can feel generic and easy to scroll past.

TL;DR: Efficiency doesn’t always translate to effectiveness.

Synthetic Testimonials Create a Trust Problem

TikTok is already working to detect and label AI content, and regulators are paying closer attention to synthetic endorsements. The biggest risk is misleading claims. An AI avatar shouldn’t imply personal use or experience. Even accurate product claims can become deceptive when framed as a testimonial.

Disclosure helps, but it’s not enough on its own. Brands still need to evaluate how the message will be interpreted. Before publishing:

Clearly label AI-generated content
Avoid claims of personal use or experience
Verify all product claims
Document tools and source assets
Follow platform and regulatory guidelines
Test Beyond Sales Volume

AI content shouldn’t be deployed at scale without testing. Start small. Compare AI videos with human creator content. Look beyond clicks and GMV: comments, returns, and repeat purchases can reveal trust issues.

Most importantly, don't forget that human creators still matter. As AI content increases, authentic voices may stand out even more.

Brands Still Have to Earn the Recommendation

AI can speed up production and testing, but it can’t replicate genuine credibility. The goal isn’t choosing between AI and human creators; it’s using each where they work best. AI supports scale and experimentation. Humans provide trust and perspective.

Aztek Marketing News Roundup (07/13 - 07/17)

Mon, 13 Jul 2026 08:00:00 Z

This week's topics:

Meta’s Muse Image Backlash: Lessons for Brands From Instagram’s AI Misstep
An Overlooked Ethical AI Risk Is an Unwritten Workflow

07/13: Meta’s Muse Image Backlash: Lessons for Brands From Instagram’s AI Misstep

Meta introduced Muse Image on July 7, 2026... then removed it three days later. Why? Users could mention any public Instagram account and ask Meta AI to generate an image using that account’s publicly available photos. Within days, the feature sparked backlash over consent and deepfake risks.

Muse Image itself remains available. Still, the controversy highlights a broader issue: just because AI can do something doesn’t mean users (or regulators) will accept it.

Meta Muse Image Timeline, Controversy, and Key Issues

Muse Image is Meta’s first image-generation model from its Superintelligence Labs, designed to go beyond basic text-to-image tools. It allows users to blend photos, edit visuals with prompts, and generate images with readable text. Meta also introduced presets for tasks like restoring old photos or reimagining people in different styles.

The @-mention feature was meant to make image creation more social and personalized. Instead, it raised immediate privacy and consent concerns. Public Instagram accounts were included by default, meaning anyone could use someone else’s photos to generate new images without explicit permission.

Critics argued that this blurred the line between public content and consent. SAG-AFTRA and privacy advocates warned that the feature could be misused, including for misleading or harmful depictions of real people. Meta acknowledged it had “missed the mark” and pulled the feature.

At the same time, questions emerged about Meta’s safeguards. The company introduced an invisible watermark, called Content Seal, to identify AI-generated images. However, testing showed that the watermark could fail under common edits as simple as cropping, raising doubts about its effectiveness in real-world use.

Why The Backlash Matters for Brands and AI Marketing

The backlash wasn’t about rejecting AI image generation altogether. It was about expectations, specifically how people expect their content and likeness to be used. For years, “public” content has been treated as fair game for reuse within certain limits. Generative AI challenges that assumption. Turning someone’s photos into entirely new images feels fundamentally different from resharing or remixing existing content.

This tension is showing up across the industry. Other AI platforms have faced criticism for generating recognizable people or copyrighted material without clear permission. As tools become more powerful, the gap between what’s technically possible and what’s socially acceptable is widening.

For brands, this creates a new layer of risk. AI can accelerate creative production, but it also introduces questions about ownership, consent, and accuracy. If something goes wrong, the brand, not the tool, is held accountable.

Key Trends to Watch in AI Image Generation

The Muse Image episode points to a few trends brands should keep an eye on:

Stronger expectations around consent. Opt-out models are increasingly seen as insufficient, especially when AI can alter someone’s likeness. Expect more pressure for explicit, opt-in permissions.
Scrutiny of AI safeguards. Watermarking and detection tools are becoming standard, but their limitations are under the microscope. Brands shouldn’t assume these systems will work perfectly once content is edited or shared.
Regulatory attention. As controversies like this grow, regulators are likely to step in with clearer rules around AI-generated content, especially when it involves real people.
Platform-level automation risks. As AI tools are integrated into advertising platforms, brands may encounter generated content they didn’t fully control. Oversight will become more important, not less.
Key Takeaways From the Meta Muse Image Backlash

Key Takeaways From the Meta Muse Image Backlash

Meta’s Muse Image rollout shows how quickly AI innovation can run into real-world expectations. Sure, the technology worked as designed, but the assumptions behind it didn’t align with how people think about consent and control.

AI can speed up creativity, but it doesn’t replace judgment. The companies that succeed with these tools will be the ones that balance experimentation with clear permissions, careful review, and a realistic understanding of what their safeguards can and can’t do.

07/14: An Overlooked Ethical AI Risk Is an Unwritten Workflow

AI is already part of most marketing workflows, whether companies have formally approved it or not. A writer uses it to explore headline options. A designer removes an object from a photo. Someone in sales pastes a customer email into a chatbot and asks for a cleaner response. None of these actions may feel significant on their own, but the risk appears when every employee makes up their own rules.

One person discloses AI assistance. Another assumes editing the output is enough. One team avoids entering customer information, while another uploads a full spreadsheet without considering where that data may go. The issue isn’t necessarily that people are using AI; it’s that everyone’s making up their own rules as they go.

A Consistency Problem

Conversations about ethical AI content often revolve around things like deepfakes, fabricated sources, or misleading images. Those risks deserve attention, but they aren’t the situations most marketing teams encounter every day. The more common problem is inconsistency.

Without clear expectations, employees have to decide for themselves:

Which tools are acceptable
What information they can enter
How closely the results need to be reviewed

Even well-intentioned people will reach different conclusions. That creates uneven quality and unnecessary risk. It also makes AI harder to scale because nobody is certain where the boundaries are. A useful AI playbook turns personal judgment into a repeatable process.

Start With Decisions, Not Principles

Many AI policies begin with broad statements about transparency or responsible innovation. Those ideas sound good, but they don’t always help someone who is staring at a blank prompt box.

Your playbook should answer practical questions:

Can employees enter client information into this tool?
Which types of content need subject matter review?
When should AI involvement be disclosed?
Who approves a new platform before the team starts using it?
What should someone do when an output includes a questionable claim?

Clear answers make responsible behavior easier. They also remove some of the anxiety employees feel when they know AI is encouraged, but don’t know what “safe use” actually means.

Review Should Match the Risk

Not every AI-assisted task needs the same level of scrutiny. Using AI to reorganize internal meeting notes is different from using it to draft medical content or create an image of a real person. A minor social caption doesn’t carry the same consequences as a case study that includes performance claims.

Instead of requiring a single universal approval process, define levels of review based on potential impact. Low-risk work may only need a quick accuracy check. Public-facing claims should receive closer editorial review. Content involving legal guidance, private information, or sensitive subjects may need additional approval before it moves forward. This keeps the process responsible without turning every AI-assisted task into a compliance exercise.

Make Human Review Specific

“Keep a human in the loop” has become a common AI talking point. It can also become meaningless when nobody defines what the human is supposed to do.

Human review shouldn’t mean glancing at a draft and fixing a few awkward sentences. The reviewer should confirm that the content is accurate, appropriate for the audience, and supported by reliable information. They should also ask whether AI was the right tool for the task in the first place.

Efficiency isn’t always the same as value. Some content depends on firsthand experience or a point of view that a model cannot supply. Using AI in those situations may produce polished copy while removing the insight that made the piece worth reading.

Build the Playbook Around Real Work

The best AI policy won’t be written once and stored in a folder. It should change as teams discover new use cases and new points of confusion. Pay attention to the questions employees repeatedly ask. Review where mistakes are happening. Update the guidance when new tools introduce different data, copyright, or disclosure concerns.

The goal isn’t to predict every possible AI scenario. It’s to give people a dependable way to make better decisions when new situations arise.

Responsible AI Should Make Work Clearer

An ethical AI playbook isn’t meant to slow adoption. It should help employees use AI with more confidence because they understand what’s allowed and what still requires judgment.

AI use will continue to change. The organizations that handle it best won’t be the ones that trust every tool or ban them entirely. They’ll be the ones that give their people clear boundaries, practical review standards, and permission to question whether automation improves the work.

Aztek Marketing News Roundup (07/06 - 07/10)

Tue, 07 Jul 2026 08:00:00 Z

This week's topics:

How to Track TikTok, Instagram, and YouTube Performance in Search
Google’s Recent Search Updates Show Why AI-Only Content Is at Risk
Microsoft Performance Max Experiments: How to Test If Automation Is Actually Driving Results

07/07: How to Track TikTok, Instagram, and YouTube Performance in Search

Google has started rolling out platform properties in Google Search Console, giving marketers a clearer look at how public social content performs in Google Search. The new view is designed to show which Google queries surface your TikTok, Instagram, X and YouTube posts, plus what happens after someone clicks.

According to Google, the report helps users track which search terms lead people to their social content and understand how audiences interact with those posts from Search. Social posts have been showing up in search results for years, but reporting has been limited. You could see engagement inside each social platform, and you could see website traffic inside analytics tools, but the connection between Google Search and public social content was harder to measure. Platform properties help close that gap.

What Google Search Console Platform Properties Track

The new reporting appears in three main areas inside Search Console.

The Performance report shows familiar SEO metrics, including clicks, impressions, click-through rate, and average position for social posts Google has indexed.
The Insights report gives a higher-level view of traffic trends, top-performing posts, and how people are discovering your content through Search.
The Achievements report highlights milestones, such as reaching a certain number of clicks from Google over a specific time period. It’s a lighter-weight feature, but it can still help teams spot momentum.

Verification happens through OAuth, which means brands don’t need DNS access or developer support to connect a profile. The first rollout supports Instagram, TikTok, X, and YouTube.

Why This Matters for SEO and Social Teams

This update makes one thing clear: Google is treating public social content as part of the searchable web. That doesn’t mean your website matters less, but it does mean your search presence is bigger than your website alone. A customer might find your brand through a blog post, a product page, a YouTube Short, or an Instagram Reel. Until now, it’s been harder to see how those social touchpoints performed inside Google Search.

SEO Teams

For SEO teams, platform properties create a better way to understand where social content is earning visibility. If a TikTok video appears for a product-related query, that’s useful search data. If a YouTube video earns clicks for a how-to search, that may influence future content planning.

Social Teams

For social teams, the report adds more context around content value. A post may not drive a flood of likes inside the platform, but it could still earn search visibility weeks or months later. That gives marketers a better way to evaluate content beyond short-term engagement.

It also makes reporting conversations more practical. If leadership wants to know whether social content supports discovery, Search Console can now provide a clearer answer.

How Marketing Teams Can Use Platform Properties

Once the feature is available in your account, setup should be relatively simple. Open Search Console, add a new property, choose the platform property option, and authorize the connected profile.

After verification, Google says data from the last 90 days can populate inside the report. That gives teams a starting point for benchmarks instead of waiting months to see patterns. From there, look for the queries that matter most. Brand searches are useful, but don’t stop there. Review product terms, service phrases, comparison searches, and how-to queries. These can show where your social content is already supporting demand.

It’s also worth comparing social post performance against website performance. If a Reel or Short is earning a stronger click-through rate than a blog post for the same topic, the format may be a better match for that search intent. The answer isn’t always to make more videos. Sometimes it’s a sign that the page needs a clearer angle, stronger visuals, or a better answer near the top.

What Brands Should Do Next

Start by claiming the platform properties you can access. Even if you don’t plan to overhaul your reporting right away, getting the data connected now gives you more visibility later.

Next, review how your captions, titles, and descriptions are written. Social content still needs to sound natural, but the opening language should make the topic clear. A vague caption might work inside a feed, but it’s less helpful when Google is trying to understand what the post is about.

Finally, bring this data into your regular reporting cadence. SEO and social shouldn’t be reviewed as completely separate channels if the content is showing up in the same search results. Platform properties give teams a shared view of how people discover the brand across formats.

Our Google Search Console Social Analytics

This is more than another Search Console report; it’s a reminder that search visibility now includes more than traditional webpages. For marketers, it's important to stop treating SEO and social as separate tracks. Your audience doesn’t think that way. They search, scroll, compare, and click across whatever format best answers their question.

Google Search Console platform properties give brands a better way to see those connections. Teams that use the data early can make smarter decisions about content planning, reporting, and budget allocation.

07/08: Google’s Recent Search Updates Show Why AI-Only Content Is at Risk

Google’s July search updates confirm that content created only to rank is getting harder to defend. That doesn’t mean Google is cracking down on every page touched by AI, since AI can still be useful for research, outlining, summarizing notes, and speeding up early drafts. The problem starts when brands use AI as the whole strategy, because if a page doesn’t add anything new or genuinely useful, it’s vulnerable.

What Google’s Search Updates Are Really About

Core updates are broad changes to Google’s ranking systems. They aren’t designed to punish one specific site or one specific tactic. Instead, they help Google reassess which pages are doing the best job of answering searchers’ needs.

A traffic drop after an update doesn’t always mean a page is “bad.” It may mean other pages are now doing a better job. They may be more current, easier to use, more specific, or backed by stronger expertise. This is where AI-only content runs into trouble.

A generic article that repeats the same surface-level points as every other search result doesn’t give Google much reason to keep ranking it. If the content could have been written without talking to a real expert, looking at actual data, or understanding the customer’s problem, it’s probably not strong enough.

Why Mass-Produced AI Content Is More Vulnerable

AI-generated content isn’t automatically against Google’s guidance. The issue is low-value automation. Pages built from lightly edited AI drafts often share the same problems.

They make broad claims without proof.
They answer the obvious part of the question but skip the nuance.
They use generic examples instead of real customer, product, or industry knowledge.

They may look complete at a glance, but they don’t leave the reader with much that they couldn’t find somewhere else. That’s a risky place to be. Search is moving toward usefulness, experience, and trust. If your content sounds like it was created to fill a keyword gap instead of help a real person make a decision, it’s worth revisiting.

How to Strengthen AI-Assisted Content

The goal isn’t to delete everything AI touched. The better move is to improve the content that still has strategic value. Start by reviewing pages that get impressions but don’t earn clicks or engagement. These are often pages Google understands, but users aren’t choosing or sticking with. Then look for ways to make the page more useful.

Add expert input from someone who works with the product, service, or audience every day. Include original examples, screenshots, process details, comparison points, FAQs from sales calls, or data your team can actually stand behind. Tighten the structure so the page answers the main question early, then supports it with useful detail.

This is also a good time to document your editorial process internally. Who reviews AI-assisted drafts? Who checks facts? Who confirms the recommendations match your actual service approach? That process doesn’t need to be complicated, but it does need to exist.

What to Watch in Search Console

Don’t panic over a few days of movement during an update. Rankings can shift while a rollout is still happening, and early data can be noisy.

Once enough time has passed, compare performance by page type, topic, and query. Look for patterns instead of obsessing over one keyword. A drop across many thin blog posts tells a different story than a dip on one older article. A stable ranking with lower clicks may point to changes in the search results page, especially if AI Overviews, rich snippets, or other features are changing how users interact with the results.

The pages worth prioritizing are the ones with clear business value, meaningful impressions, and room to become more helpful. Those are better candidates for improvement than quick deletion.

AI Can Help, But It Shouldn’t Lead

Google’s recent search updates reinforce what good content teams already know. The best pages are built with a clear purpose and a useful answer for the person searching. AI can support that work by helping organize ideas, speed up drafts, and make production more efficient. It can’t decide what your audience actually needs, what your brand knows better than competitors, or what proof belongs on the page.

That’s the part humans still need to own. The brands that treat AI like an assistant instead of an autopilot will be in a much better position, no matter what the next search update brings.

07/09: Microsoft Performance Max Experiments: How to Test If Automation Is Actually Driving Results

Most marketers are open to experimenting with AI-powered campaigns. Finance teams are usually open to them too, as long as there’s proof that the budget is being well spent. That’s where Microsoft Performance Max has been tricky.

Like other automated campaign types, Performance Max promises better performance by letting the platform optimize across formats, audiences, and placements. The tradeoff is visibility. Advertisers have had to trust more of the process without always being able to see what’s actually driving results.

Microsoft’s new Performance Max experiments help tighten up that blind spot. The company recently opened two experiment types in beta: Upgrade and Uplift. Both give advertisers a clearer way to test whether PMax is adding value, not just moving conversions around inside the account.

Why Microsoft Performance Max Experiments Matter

The two experiments answer different questions.

Upgrade experiments compare an existing Search or Shopping campaign against a PMax version of that same campaign. This is useful when you’re considering a move to Performance Max but don’t want to make the switch based on platform recommendations alone.

Uplift experiments are designed to measure incremental impact. Microsoft splits audiences into exposed and suppressed groups so advertisers can see whether PMax is actually creating additional conversions or simply taking credit for demand that already existed.

One of the hardest questions in paid media is whether a campaign caused a conversion or just happened to be nearby when the conversion happened. Automated campaigns make that question even harder because so much of the decision-making happens inside the platform.

With experiments built into Microsoft Performance Max, paid media teams have a better way to test the value of automation before they expand it. That doesn’t mean every test will prove PMax is worth more budget, but it does mean advertisers can make that decision with stronger data.

How to Run A Microsoft Performance Max Experiment

The first step is choosing the right test. If you’re thinking about moving a Search or Shopping campaign into PMax, start with an Upgrade experiment. This gives you a cleaner side-by-side comparison before changing the structure of your account.

If you’re already running PMax and need to justify more investment, an Uplift experiment is the better fit. It’s built to show whether the campaign is producing incremental conversions.

From there, keep the test as clean as possible.

Set one primary KPI before the experiment starts. That might be conversions, cost per acquisition, or return on ad spend. Avoid changing the goal halfway through the test because it makes the results harder to explain.
Give the experiment enough time to run. Microsoft recommends a minimum two- to four-week testing window, and shorter tests can limit the platform’s ability to learn. That’s especially true for accounts with longer sales cycles or lower conversion volume.
Limit unnecessary variables. Keep budgets, creative assets, audience inputs, and bid strategy as consistent as possible across the test. The goal is to measure the campaign type, not a mix of setup changes.
Wait for the lift report before making a decision. Early results can be misleading, especially if conversion lag or seasonality is in play. A test that looks strong in week one may flatten out once more data comes in.

Use PMax Experiments As A Recurring Check

Microsoft Performance Max experiments shouldn’t be treated as a one-time box to check. They’re more useful as part of an ongoing testing process. If an Upgrade experiment wins, that’s a good signal. It doesn’t mean every Search or Shopping campaign should be moved into PMax immediately. Test another product line or campaign group before making a broader account change.

If an Uplift experiment shows limited incremental value, that doesn’t always mean PMax should be shut off. The next step may be refining audience signals, improving creative, adjusting budgets, or limiting where PMax is allowed to overlap with other campaigns.

The bigger takeaway is that advertisers should expect proof from AI-driven campaign types. Google, Meta, Amazon, and Microsoft are all pushing automation deeper into ad accounts. That shift isn’t slowing down. The teams that get the most out of it will be the ones that test carefully instead of handing over budget and hoping the platform is right.

Microsoft Performance Max Experiments Give Marketers Better Proof

Microsoft Performance Max experiments give paid media teams a stronger bridge between automation and accountability. When the lift is real, advertisers have better numbers to support more investment. When the lift isn’t there, they can catch the issue before more budget gets moved into a campaign that isn’t creating new value. Either way, the data is more useful than another round of “just trust the AI.”

Aztek Marketing News Roundup (06/30 - 07/03)

Tue, 30 Jun 2026 08:00:00 Z

This week's topics:

LinkedIn’s 2026 Algorithm Update, One Month Later
GPT‑5.6 Preview Faces Government Review
Content Decay in 2026: Why Good Pages Go Stale

06/30: LinkedIn’s 2026 Algorithm Update, One Month Later

Last month, we warned that the classic B2B LinkedIn routine (dropping a blog link from the company page and waiting for likes) was fading fast. New data from multiple analyses published over the last two weeks shows the slide is real and accelerating.

Three Key Signals to Keep Watching

Time on post is the top factor. Gallium reviewed thousands of posts and found that LinkedIn decides whether to show your post to more people within the first 60 to 90 minutes after posting. Saves and thoughtful comments help it spread, but likes alone won't.
Better comments beat more comments. Replies of 12 or more words give a post extra reach, while quick emojis or one‑word reactions do very little to move the needle.
Posts with outside links reach fewer people. Posts asking users to leave LinkedIn get about 60% less reach than similar posts that keep readers on the platform.

TL;DR: Company pages are struggling. Posts from brand pages now reach only about 2% of their followers, while personal profiles get most of the algorithm’s attention. This happens because all three signals above (time on post, quality comments, and link penalties) favor content that feels personal, invites real discussion, and keeps people in the feed. Brand posts often push external links, receive quick but shallow reactions, and lack the human voice that prompts longer replies, so their reach drops.

4-Step Action Plan

Here’s a quick checklist to move your LinkedIn game from link drops to native, useful posts you can test this week.

Re‑package every asset natively. Turn the blog’s core insight or chart into a feed‑ready post and add the link tomorrow, not today.
Shift volume targets. Publish one to three high‑depth posts each week from executives and subject matter experts instead of daily link blasts.
Measure saves, quality replies, and profile visits. Likes tell only part of the story.
Audit monthly. Track reach per 1,000 followers; if the number keeps sliding, you are still leaning on links or shallow content.

Following these four moves will align your content with the signals LinkedIn rewards: time spent, quality conversation, and native value, without adding more work to your calendar.

What's Next For LinkedIn

LinkedIn hasn’t killed organic reach for B2B brands, but it is punishing the shortcut tactics that used to prop up company‑page posts. Give buyers a clear takeaway right in the post, lead the discussion from a subject‑matter expert’s profile, and wait to drop the blog link until tomorrow. Build depth first, ask for the click later, and both the algorithm and your next prospect will reward the effort.

07/01: GPT‑5.6 Preview Faces Government Review

OpenAI’s GPT‑5.6 release looked ready to extend the rapid cadence we have come to expect from frontier models. Instead, its debut comes with a new twist: the White House asked OpenAI to slow the public rollout while federal teams review national‑security and misinformation risks. That pause is a signal that future jumps in model capability may be gated as much by policy as by engineering. For marketers, it is a reminder to plan around reality, not hype.

What’s New in the GPT‑5.6 Release

GPT‑5.6 actually ships as three sibling models:Sol, Terra, and Luna. Each one is tuned for different workloads. OpenAI says they reason more accurately, switch contexts almost twice as fast as GPT‑4o, and come with a “next‑gen safety stack” that blocks risky outputs earlier in the generation pipeline.

Early partner testers highlight:

smoother long‑form content creation
richer data‑analysis prompts
more reliable brand‑tone controls

In short, the GPT‑5.6 release does what marketers hope: better thinking delivered faster and without guardrail headaches.

Why the White House Is Pumping the Brakes

According to reporting from TechCrunch and The Guardian, U.S. officials requested a staggered launch so government security teams could vet the model’s potential impact, particularly around deep‑fake generation and critical‑infrastructure threats. OpenAI agreed, limiting early access to about twenty trusted partners while hinting it does not want political review to become standard procedure.

Whether this review ends in days or weeks, the precedent is set: breakthroughs may now face an external timetable.

What It Means for Marketing Teams

Marketers have grown accustomed to a new flagship model every spring. The GPT‑5.6 release shows that cadence is no longer guaranteed. Tool vendors that rely on OpenAI’s API are already revising launch notes. Internal teams planning campaigns around fresh capabilities may need to push dates or downgrade expectations. At the same time, regulators are increasingly interested in how brands collect, process, and feed data into AI systems. Compliance checklists you could once ignore until procurement now belong at kickoff.

Next Steps

Max out GPT‑4/4o now. Many teams still leave accuracy and speed on the table because prompts and workflows are half‑baked. Tighten them up instead of waiting.
Document data flows. Map where personally identifiable information enters your AI stack and how it is masked, encrypted, or removed. You will need that record sooner than you think.
Build a buffer into AI‑driven timelines. Treat public launch dates as directional, not fixed.
Monitor official channels. Subscribe to OpenAI’s developer updates and trusted industry reporters so you see schedule changes in real time.

TL;DR: Keep experimenting, but anchor plans in what is actually available today. Regulatory scrutiny is unlikely to fade, and that is not a bad thing. Better‑vetted models are safer to integrate and easier to defend when stakeholders ask why they should trust the output.

07/02: Content Decay in 2026: Why Good Pages Go Stale

You open Search Console and see a paradox: your article still ranks between positions 3 and 5, and yet clicks have slid 20% in the last quarter. That’s content decay. Your traffic slips and hard‑earned authority fades even while average rank stays steady. The culprit is visibility, not position. AI Overviews, rich snippets, aggressive ad blocks; they all now crowd the fold, siphoning attention away from “older” pages.

Why Decay Is Accelerating in the AI‑Search Era

Three forces are turning slow fade into free‑fall:

AI Overviews change the citation pool. Only 38% of AI Overview sources now come from the top‑10 pages, down from 76% a year earlier. A page can look healthy in classic SERPs yet vanish from this new arena.
Freshness and E‑E‑A‑T matter more. Google’s March and June spam updates reward factors like recent data, clear authorship, and firsthand examples. Stale listicles that once coasted on links now look thin beside newer studies.
Content noise is exploding. Roughly 74% of new web pages contain AI‑generated copy. With so much “good‑enough” text flooding the index, a dated article loses distinctiveness fast.

Put those forces together and content decay arrives months, not years, after publication.

Spotting Decay in Your Analytics

Don’t wait for year‑over‑year comparisons. A quick monthly check can show signs of trouble early:

Compare clicks, not just rank. In GA4, create a report that plots clicks versus average position for the last 90 days. Diverging lines signal decay.
Layer in AI Overview impact. Filter queries that now trigger AI Overviews (look for Google’s “ai‑overview” impression line) and watch their click trend. A recent randomized trial pegged average click loss at 38% for these queries.
Flag slipping CTR segments. Segment URLs with a CTR drop greater than 15% quarter‑over‑quarter; these become top refresh candidates.

Forecast business impact. GA4’s predictive metrics let you model revenue loss if decay continues.

The Five‑Step Refresh Sprint

Audit aged pages. Pull everything published or last updated 18+ months ago. Prioritize those with traffic decline or strategic keywords.
Update substance first. Add 2026 data points, new visuals, expert quotes, and concrete examples. Fresh facts signal relevance to both readers and ranking systems.
Strengthen E‑E‑A‑T cues. Include an author bio with credentials, cite primary sources, and add original images or charts.
Republish and re‑index. Change the on‑page date, update schema dateModified, and request indexing in Search Console.
Track recovery. Monitor clicks, dwell time, and whether the page regains AI Overview citations over the next 30 days. Create a GA4 audience for likely purchasers tied to the refreshed URL to measure downstream revenue lift.

Get Ahead of Content Decay

Content decay isn’t a glitch; it’s the natural half‑life of digital content, sped up by AI‑forward search results and a flood of auto‑generated pages. Treat refreshes as routine maintenance, not emergency triage. Run a sprint every quarter, starting with high‑intent pages that have slipped, and your evergreen assets will keep earning their stay, no matter how the SERP evolves.

Aztek Marketing News Roundup (06/22 - 06/26)

Tue, 23 Jun 2026 08:00:00 Z

This week's topics:

Should Your Brand Use AI‑Generated Images? Pros, Cons, and Guardrails
How to Read Google’s New Generative AI Performance Reports in Search Console
LinkedIn’s New “Suggested Feed”

06/23: Should Your Brand Use AI‑Generated Images? Pros, Cons, and Guardrails

Generative‑AI image tools can crank out polished product shots, or even create entire digital influencers, in seconds and for pennies. That speed and savings sound irresistible, but they arrive with new questions about audience trust and regulatory scrutiny. The real debate isn’t if AI‑generated images belong in your social mix; it’s how to deploy them without torching credibility or running afoul of disclosure rules.

The Upside: Studio Looks Without the Studio Bill

AI tools have already helped mid‑market brands shave well over half of their photography budgets by letting them simulate photoshoot conditions on a laptop. When a marketing team can spin up forty variations of a product shot before lunch, it becomes a lot easier to A/B test angles, lighting, and messaging. In that sense, generative imagery levels the playing field. Suddenly, a brand with a modest budget can match the creative volume once reserved for deep‑pocketed competitors.

The Downside: Audiences Aren’t Buying “Bot Beauty”

Efficiency means nothing if the content feels fake. Consumer studies consistently show that most people can spot, and often distrust, imagery they think was created by a machine. Feed fatigue is already real: after a streak of obviously synthetic visuals, even a well‑targeted ad can come across as cold or uncanny. Some brands are pushing back hard in the opposite direction. Polaroid’s recent “Go Analog” campaign is a reminder that authenticity itself can be a differentiator.

The Compliance Squeeze Is Already Here

The Federal Trade Commission’s updated Endorsement Guides make clear that undisclosed synthetic endorsements can draw five‑figure penalties and headline‑grade enforcement actions. Social platforms are also moving quickly: Instagram now offers an “AI creator” badge and is experimenting with automated image‑labeling. Even if these controls feel optional today, history suggests they will become standard practice once a few enforcement examples hit the news.

Guardrails Before You Hit “Post”

Disclose clearly, every time. Pair #ad with a label like “This image was generated with AI.” Clarity matters more than hashtags.

Test for trust impact. Run identical posts: one human‑shot and one AI‑rendered. Watch engagement, saves, and comments. If numbers dip, that is telling you something.
Blend, don’t replace. Use AI for concepting, background swaps, or filling product catalog gaps, but keep real faces and moments in the feed to preserve brand warmth.
Audit your metadata. Make sure alt text and image data align with platform detection tools so helpful automation does not flag your own content as deceptive.
Review legal and brand tone together. Align every visual with the FTC guides and your own values. If it feels off‑brand, it probably is.

So, Should You Use AI‑Generated Images?

AI‑generated images are a powerful tool, not a magic wand. Used thoughtfully, they slash production costs and speed creativity. Used carelessly, they erode the credibility you spent years and real budget building. Start small, label everything, measure the trust impact, and keep a human heartbeat in the mix. Nail that balance and you will bank both the savings and the audience goodwill.

06/24: How to Read Google’s New Generative AI Performance Reports in Search Console

Google recently gave site owners a window into how their pages show up in AI Overviews and AI Mode. On June 3 2026, the Search Central team rolled out a dedicated Generative AI performance report inside Search Console.

If you’ve been wondering whether Google’s AI answers are cannibalizing or amplifying your visibility, this report won’t solve every mystery, but it’s a solid first step.

What Actually Changed

Until now, impressions from AI Overviews and AI Mode were buried inside your standard Web totals. The new Generative AI performance report breaks that slice into its own view for both Search and Discover.

You’ll see familiar dimensions such as pages, countries, devices, and dates, so you can pivot the data the same way you do with traditional results. That lets you answer questions like, “Which blog posts get cited in AI Overviews on mobile in the United States?”

Where to Find It and Why You Might Not

Open Search Console and look under Performance, then Generative AI. If the menu item is missing, Google either hasn’t released the feature to your property yet or you haven’t collected enough AI impressions to qualify.

Tip: The Export button works the same as other reports, so you can pull the numbers into Looker Studio dashboards without extra steps.

The Catch: Data Gaps to Know About

No clicks or click-through rate yet. The report counts impressions only.
No query or appearance filters. You can segment by page or device but not by search term.
Limited API support, so automated pipelines will have to wait.

Keep these limits in mind to avoid drawing conclusions the data can’t support.

Benchmark Your First 30 Days

Export a full month of Generative AI impressions.
Compare them to total Web impressions for the same period to see what share AI already commands.
Flag outlier pages, both high and low performers, for manual review.
Track weekly shifts to spot algorithm tweaks or content wins early.

The goal here is to establish a baseline before the numbers start to move.

Optimize Content for AI Answers Without Over-Engineering

Google’s own guidance says the same fundamentals still apply: clear structure, authoritative sourcing, helpful visuals, and content that answers a real question.

Generative systems look for pages they can quote with confidence. In practice:

Tighten headings so key facts sit in easily cited paragraphs.
Add unique angles or data that make your piece the obvious source.
Keep EEAT signals visible, including bylines, citations, and about pages.

Tip: Write for people first, then refine for AI.

Should You Use the Opt Out Toggle

Google is also testing a site-level control that lets you exclude your content from AI Overviews and AI Mode entirely. Opting out protects against miscontextual quotes, but you’ll forfeit any impressions and eventual clicks from generative results. Most brands are better served by monitoring performance first, then deciding if specific sections such as medical or financial advice need tighter control.

Turning Generative AI Data Into Action

The Generative AI performance report is an early-stage feature, but it turns guesswork into data. Add the report to your weekly SEO dashboards, benchmark your visibility now, and refine pages that should be winning AI citations but aren’t. Keep expectations realistic: impressions without click data are a directional signal, not gospel, but ignoring them means flying blind.

06/25: LinkedIn’s New “Suggested Feed”

Last week LinkedIn began testing a LinkedIn suggested feed that surfaces topic-focused posts outside a user’s network. Initial screenshots shared by the platform’s product-marketing team show swipeable options (think “AI at Work” or “Cannes Lions”) sitting beside the main feed. The pilot started with users on-site at Cannes and will expand if engagement stays strong.

This is not LinkedIn’s first alternate feed experiment, but it is the clearest push toward TikTok-style discovery the platform has attempted so far.

Why It Matters for Reach

LinkedIn’s core feed still leans on the connection graph, yet fresh usage data suggests there is more content than the main timeline can handle. The company reported a 14% year-over-year jump in posts and a 30% lift in paid video views in Q1 2026.

For brands, the visibility game is shifting from “How many followers do we have?” to “IS this post relevant for a specific professional topic?” Quality interaction signals (saves, thoughtful comments, and real dwell time) will count more than raw reaction totals.

Practical Next Steps

Tighten the hook. Open with a clear point of view or data point a busy professional cannot scroll past.
Post natively and context first. Links without commentary rarely earn placement; short narrative or quick slides outperform “Read our blog” blurbs.
Map three to five content pillars. If the LinkedIn suggested feed rolls out broadly, the algorithm will reward consistency around recognizable themes.
Track the “Other” bucket. In LinkedIn analytics, impressions from suggested surfaces often land in “Other.” Plot that trend line now to spot early lift later.
Prompt genuine conversation. Ask for perspectives, not perfunctory “thoughts?” Spammy engagement bait is already on LinkedIn’s naughty list.

The Bigger Shift

LinkedIn is evolving from a professional Rolodex into a discovery engine. The brands that win are the ones creating content worth discovering: useful, opinionated, and aligned to real expertise. If your feed strategy still treats LinkedIn like an email blast, you will miss the bar altogether. Flip the mindset; create for professionals first, algorithms second, and the algorithms are more likely to do the heavy lifting for you.

Aztek Marketing News Roundup (06/15 - 06/19)

Mon, 15 Jun 2026 08:00:00 Z

This week's topics:

The AI Cost Crunch
How to Build Human-Plus-AI Workflows That Actually Improve Marketing

06/15: The AI Cost Crunch

AI’s early story was speed and adoption. Now CFOs are asking a tougher question: what did that token spend actually deliver? Meanwhile, vendors are signaling a more competitive pricing environment. The Wall Street Journal reports that OpenAI is considering steep price cuts to compete more aggressively with Anthropic as both companies prepare for potential IPOs.

At first glance, lower prices sound like good news for buyers, but pricing is only part of the story. As AI vendors adjust their offerings and retire older models, organizations face a new challenge: managing costs and operational risk in a market that is changing faster than most budgets can keep up.

What’s Driving the Cost Crunch?

Several forces are hitting at once, and none of them are especially friendly to teams that have been treating AI usage as a loose experiment.

Enterprise sticker shock. The token-maxxing culture of 2025 created a wave of bloated AI bills. Some teams saw monthly AI costs climb 4x as employees pushed larger prompts, longer contexts, and more frequent requests through premium models. Now leaders are adding usage caps, tightening access, and rewriting prompts to cut waste.

Price-cut pressure. Cheaper open-source, regional, and smaller specialized models are proving good enough for many everyday workflows. Classification, extraction, summarization, routing, and draft generation do not always require the most expensive model on the market. Premium vendors need to defend share, and pricing is one of the fastest levers they can pull.

Model retirement risk. OpenAI’s summer and fall shutdown dates affect widely used model families, including gpt-4-0613 and gpt-3.5-turbo-0125. Any workflow still calling those IDs needs an update. The replacement may be better, but it may also behave differently.

Why Vendors Are Slashing Prices

Cloud providers and AI labs invested heavily in compute capacity during the surge. When infrastructure becomes more available, vendors have more room to compete on price. At the same time, smaller models like Phi-3-Mini and Gemma-2 are showing that many practical business tasks can be handled without sending every request to a frontier model.

There is also the IPO angle. Lower entry pricing can drive adoption and improve growth curves. More users, more usage, and faster expansion all look good on the road to a public filing.

What This Means for Teams Using AI Today

AI cost management now sits at the intersection of finance, product, legal, security, and operations.

For technical teams, the risk is brittle infrastructure. Hard-coded calls to retiring models can break workflows after shutdown dates.
For finance teams, the risk is budget variance. A small pricing change, model swap, or prompt expansion can shift the monthly run rate quickly.
For compliance teams, the risk is churn. Moving to a new model, provider, or cloud region may trigger privacy, security, or regulatory reviews, especially in healthcare, finance, and EU markets.

The companies that handle this well will not be the ones that chase the lowest token price. They will be the ones who know which models they use, why they use them, what each workflow costs, and where cheaper alternatives can be adopted without hurting quality.

Practical AI Cost Management Moves for 2026

Create a live inventory of all AI models and endpoints in use.
- Track model IDs, versions, owners, use cases, pricing, usage, and retirement dates.
- Prioritize any models scheduled to sunset soon.
Test lower-cost alternatives before migrations become urgent.
- Run 100–200 real prompts through alternative models.
- Compare business outcomes such as lead quality, resolution time, accuracy, and risk.
Set AI usage budgets by team and role.
- Reserve premium models for high-value workflows.
- Use lower-cost options for routine internal tasks.
Negotiate with vendors early.
- Explore volume discounts, enterprise pricing, and committed-use agreements.
- Use accurate usage data to strengthen your position.
Track total cost of ownership, not just token pricing.
- Include prompt engineering, evaluation, DevOps, migration, legal review, monitoring, and support costs.
- Factor in operational effort when comparing models.

Build AI Cost Management Into Your 2026 Planning

Generative AI still delivers value, but the free-for-all phase is ending. Price cuts can reduce costs, model retirements can add risk, and usage growth can offset both. The teams that succeed will audit their AI stack, test lower-cost options, prepare for model changes, and tie spend to business outcomes.

06/18: How to Build Human-Plus-AI Workflows That Actually Improve Marketing

AI can draft copy faster than any marketing team ever could. That does not mean it should be handed the keys and trusted to drive the whole strategy. The real opportunity is building better human plus AI workflows, where AI handles the mechanical parts of the work and people stay responsible for strategy, judgment, accuracy, and final approval.

Done well, these workflows help teams move faster without watering down the brand. Done poorly, they create a faster path to off-tone copy, made-up facts, messy approvals, and work that still needs to be redone by a human anyway.

Why Human Review Still Matters

AI-only workflows look efficient right up until a confident-sounding mistake lands in a client deck. Large language models are powerful, but they still make things up, miss context, and present shaky information as fact. That’s why human review isn’t optional; it’s the layer that turns AI output into work you can actually trust.

The human checkpoint should answer questions like:

Is this accurate?
Does this sound like us?
Is the claim supported?
Does the message fit the audience?
Does this help us reach the business goal?

Without that layer of judgment, teams may move faster, but they may not move in the right direction.

Start by Mapping the Work

The easiest way to build a better AI workflow is to break the process into steps before deciding where AI belongs. Too many teams start with the tool instead of the task. They open a chatbot, throw in a vague prompt, and hope the output saves time. A stronger approach starts with the workflow itself.

Workflow Step	AI Can Help With	Humans Should Own
Research	Summarizing source material	Choosing credible sources
Outline	Suggesting structure	Defining the angle
Drafting	Creating a rough first pass	Adding voice and nuance
Editing	Tightening or repurposing copy	Final quality control
SEO Review	Drafting metadata	Validating search intent

The same logic applies across marketing. In paid media, AI can help brainstorm ad variations or spot performance patterns, but people should decide whether the message fits the audience and offer. In reporting, AI can summarize data trends, but marketers need to decide which insights are worth acting on.

Where AI Should Not Make the Final Call

AI can support a lot of marketing work, but some decisions should stay firmly with humans.

AI should not be the final decision-maker for:

Brand positioning
Legal or compliance claims
Sensitive customer communications
Budget changes
Strategic recommendations
Final campaign approvals

It can provide inputs, drafts, summaries, and options. It should not be the source of truth. This is especially important for companies in complex industries, where the wrong phrasing can create real problems. AI does not always understand those boundaries unless humans define them clearly. Even then, the final review still matters.

Build AI Workflows That Make the Work Better, Not Just Faster

Human-plus-AI workflows are not about trading people for prompts. They are about giving marketers a better way to handle the parts of the job that slow them down without giving up the judgment that makes the work worth doing.

The strongest teams will not be the ones that automate everything. They will be the ones that know where AI fits, where humans need to lead, and how to protect quality along the way.

Aztek Marketing News Roundup (06/08 - 06/12)

Tue, 09 Jun 2026 08:00:00 Z

This week's topics:

Google’s Third-Party SEO Tools Guidance
Meta Business Agent: Turn Social DMs into 24/7 Sales & Support
Meta Feed Personalization Now Uses Off‑Site Data

Google’s Third-Party SEO Tools Guidance

Google recently published new guidance on third-party SEO tools and services. It came just days after the May 2026 core update finished rolling out, giving marketers another reason to take a closer look at the tools, reports, and recommendations guiding their SEO decisions.

The message in the new documentation is not that third-party SEO tools are bad. It does make it clear, however, that they are not Google and their dashboards should not be treated like a direct view into Google’s ranking systems.

As AI search changes how people find information, vendors are promoting more AEO, GEO, AI visibility, and answer engine optimization services. Some of these tools can help teams spot issues and find opportunities. Others make confident claims that imply access, certainty, or Google approval they simply do not have.

What Google Is Really Saying

Google’s guidance draws a clear line:

Third-party tools do not have access to Google’s internal ranking data.
Google does not endorse specific SEO tools or services.
Scores, forecasts, and rankings predictions are not guarantees.
Strong SEO recommendations should be backed by clear reasoning, reliable data, or official Google documentation.
Google Search Console remains the first-party source for performance data from Google Search.

In other words: use SEO tools, but do not outsource your judgment to them.

What This Means for Marketing Teams

For in-house marketers, this is a good time to audit your SEO stack.

Start by looking at the dashboards and reports your team relies on most. Are they helping you understand performance, or are they creating noise? Are recommendations tied to actual business outcomes, or are they based on generic scoring systems?

The best SEO decisions usually start with first-party data: Google Search Console, GA4, CRM data, form submissions, call tracking, and sales activity. Third-party tools can add helpful context, but they should not replace the data that shows how your site is actually performing.

That means before making a major SEO change, ask:

Is this supported by Search Console or analytics data?
Does it affect pages that matter to our audience or business?
Is this a real issue, a hypothesis, or just a tool-generated suggestion?
What outcome are we trying to improve?

If the answer is unclear, slow down before adding another task to the roadmap.

What This Means for Agencies and Vendors

For agencies, Google’s guidance reinforces something strong SEO partners should already be doing: explaining the “why.”

Clients should expect more than exported reports and automated recommendations. They should understand what work is being recommended, what data supports it, how it connects to business goals, and where it fits into the larger strategy.

That does not mean every SEO decision needs to come directly from a Google help doc. SEO still involves testing, experience, pattern recognition, and judgment. When a vendor makes a strong claim about what Google wants, that claim should be easy to explain and verify. No secret formulas. No guaranteed rankings. No hiding behind a dashboard.

Stay Grounded in Your SEO Strategy

Google’s new guidance is not a reason to ditch your SEO tools, but it is a reminder to use them responsibly. SEO is not magic, and it is not guesswork. It is a disciplined process of improving your website so users and search engines can understand, trust, and act on your content.

06/10: Meta Business Agent: Turn Social DMs into 24/7 Sales & Support

For years brands have treated WhatsApp, Messenger, and Instagram inboxes like crowded help desks. Meta Business Agent promises to flip that script. The new first-party AI chatbot invites companies to handle product questions, give recommendations, book appointments, and pass qualified leads to humans without ever leaving a chat thread.

What Is Meta Business Agent?

Meta Business Agent is an opt-in feature that lives inside a business’s existing WhatsApp, Messenger, and Instagram accounts. Set-up takes minutes, or teams can connect deeper through the new Business Agent Platform for CRM and catalog integrations . Meta claims more than one million businesses already rely on an early version, and customers open more than one billion business chat threads every day . That volume explains why Meta is betting on automated conversations.

Why Marketers See Potential

For marketing teams, the appeal makes sense. Customers are already asking questions in social inboxes, so why not enlist some help to answer those questions quickly and in a way that moves the relationship forward.

Conversion lift – Fast answers can keep shoppers engaged before they bounce to a competitor.
Efficiency – A well-trained agent can handle common questions at scale, giving human teams more time for complex or high-value conversations.
Data feedback – Chat interactions can reveal customer intent, which may help improve follow-up, segmentation, retargeting, and look-alike modeling.

Meta frames this as an “infinite team” effect. That promise is compelling, but only if the AI is actually useful. A chatbot that answers quickly but poorly does not create leverage; it creates cleanup work. The real value comes when automation helps customers make progress without making the experience feel generic or careless.

The Risks That Come With Automation

The upside is real, but businesses should be careful not to confuse availability with quality. The more responsibility an AI agent takes on, the more important it becomes to manage risk before the tool reaches customers.

Hallucinations and wrong answers – An AI that invents return policies, pricing details, or product claims can damage trust quickly.
Tone drift – Robotic or off-brand language can make loyal customers feel like they are no longer talking to the company they know.
Privacy and regulation – Customer conversations may include personal or sensitive information, and businesses need to understand how that data is handled, stored, and governed.

These risks grow as companies push AI beyond basic FAQ support and into direct sales, recommendations, lead qualification, and service recovery. The more consequential the conversation, the less room there is for vague answers or weak guardrails.

Why Human Oversight Still Matters

Automation can speed up the front line, but it does not replace judgment. Businesses still need people shaping the strategy, reviewing performance, and deciding where AI belongs in the customer experience.

Humans must:

Curate product data, FAQs, policies, and service information so the agent has reliable source material.
Review chat logs, refine prompts, and update guardrails based on real customer interactions.
Step in when conversations become sensitive, emotional, urgent, or high-value.
Decide when the goal is support, when it is sales, and when a forced response could do more harm than good.

AI can deliver speed and consistency. People protect brand voice, customer trust, and business judgment. The strongest programs will use both intentionally.

What Meta Business Agent Means for Social Messaging

Meta Business Agent could turn busy social inboxes into always-on sales and support channels, especially for resource-constrained teams, but this is not a set-and-forget shortcut. The brands that get the most value will treat AI chat as an operating system that needs monitoring and governance. Start small, measure honestly, and eep humans close to the moments that matter.

06/12: Meta Feed Personalization Now Uses Off‑Site Data

Starting in July, Facebook, Instagram, Reels, and Meta AI will rank content partly on the purchases and website actions people take outside Meta’s apps. That means that if someone buys a tent on an outdoor‑gear site, the feed may soon serve them more camping videos whether or not that brand pays for an ad.

Why does that matter? The same server‑side data streams marketers set up for conversion tracking can now lift (or limit) organic reach. The change blurs the once‑clear wall between paid targeting and "earned" visibility.

What Exactly Changed?

Meta already receives purchase and browsing events through the Conversions API, pixel, and app SDK. As of this rollout, those off‑site events will influence the feed, recommended Reels, and answers from Meta AI in ten initial markets, with the EU, U.K., Brazil, and South Africa excluded for now. Meta is folding multiple settings into one control called Activity from Other Businesses so users can opt out of both ad and feed personalization in a single switch.

Why Meta Is Doing It

Apple’s App Tracking Transparency kneecapped Meta’s ability to see what people do in other apps. By leaning harder on first‑party server events, Meta regains some of that lost context while pitching investors on a feed that feels as "relevant" as TikTok’s. The company also needs to prove its hefty AI spend boosts engagement, not just ad bid prices. Factoring purchase intent into organic ranking is a quick win on both fronts.

Impact on Organic Reach, Ads, and Measurement

Organic lift for signal‑rich brands. Retailers and DTC brands already sending clean purchase events could see incremental reach without extra media spend.
Sharper ad performance, but muddier attribution. Advantage+ campaigns and Lookalikes now train on a richer data set, yet separating paid and organic influence will get harder when both rely on the same external signals.
Creative resonance matters more. If the algorithm knows you bought hiking gear, a how‑to‑pack reel can earn a save faster than a generic lifestyle post.

For brands without reliable off‑site data, the opposite applies: expect to compete against better‑contextualized posts in the same feed real estate.

Privacy and Compliance Watch‑Outs

Meta says it is not collecting new data, just using existing data differently. But to regulators, purpose expansion can be as important as collection. The new single toggle may satisfy simplicity advocates, yet it collapses consent granularity. Consumer‑facing privacy notices and consent flows that mention “ad targeting” but not “content personalization” may now be out of date.

What We’ll Be Watching

Regulator reactions. European privacy laws are strict, so Meta is moving slowly. If the company eventually turns this feature on in the EU, that’s a sign regulators are satisfied.
Opt‑out numbers. If large numbers of users flip the new privacy switch to Off, brands will lose much of the extra reach. Keep an eye on that trend.
How fast it spreads. Meta rarely runs permanent experiments in only ten countries. Once the legal team is happy, expect the feature to hit more markets quickly.

TL;DR

Meta feed personalization is evolving from who you follow to what you do online. For brands, that means the conversion data you already share can now surface your content in front of warm prospects for free. That is, if the signals are clean and the creative is context‑ready. Treat the next few months like a soft launch: tighten your data pipes, refresh your privacy language, and pilot intent‑matched creative before the feed changes for good.

Aztek Marketing News Roundup (06/01 - 06/05)

Mon, 01 Jun 2026 08:00:00 Z

This week's topics:

AI Email Summaries Are Becoming the New First Impression
AI Mode Ads Are Changing Google Search
Consistent Brand Messaging for AI Search

06/01: AI Email Summaries Are Becoming the New First Impression

Email marketers have spent years obsessing over subject lines and preview text. Those "first impression" elements were often the deciding factor in whether an email was opened or ignored. Today, they still play an important role in getting attention, but they may no longer be the first thing someone sees when they open their inbox.

Why AI Email Summaries Change the Inbox Experience

Tools like Gemini in Gmail, Copilot in Outlook, and Apple Intelligence can summarize content, surface key details, and highlight next steps. As these features expand across email, messaging, and notifications, AI is increasingly shaping how people experience communications before they read them in full.

For users, this can be helpful. Nobody wants to dig through a messy thread just to find the one sentence that matters. For marketers, it creates a new challenge. Your email may be judged by a machine-generated summary before your actual copy gets a chance to do the work.

That puts more pressure on clarity. If the main point of your email is buried under a fluffy opener, a large hero image, or three paragraphs of setup, the summary may miss what you actually wanted people to know.

What This Means for Email Performance

AI summaries could make email reporting harder to read. Open rates have already become less reliable thanks to privacy changes, bot activity, and platform-level filtering. AI summary features add another wrinkle because people may understand the message without engaging with the email the way your dashboard expects.

A recipient might see the offer in the summary and never open the message. They might click from a surfaced CTA in a way that does not track cleanly. They might get the answer they needed and move on, which could be a good user experience even if it looks like weak engagement in your reporting.

This is where marketers need to be careful. A drop in clicks may not always mean the message failed. A steady open rate may not mean people actually read the email. The gap between what users do and what platforms report is getting wider.

One practical way to combat messy reporting is to focus on a broader set of performance indicators instead of relying on a single metric. Tracking things like assisted conversions, reply rates, website behavior, and overall campaign outcomes alongside opens and clicks can provide a clearer picture of how people are actually engaging with your marketing.

How to Write Emails That Summarize Well

The best defense against a bad AI summary is a clear email. Start with the point. If the email is about a limited-time offer, say that early. If it’s about a service update, put the actual update in the first few lines. If there is one action the reader should take, make it obvious before the email gets too far into background or supporting details.

This is also a good reason to avoid overly clever openings. A warm, human tone is still important, but vague intros do not give AI tools much to work with. “We’ve got some exciting news to share” is less useful than “Registration for our September webinar closes this Friday.”

Structure matters too. Clear headings, simple formatting, descriptive links, and plain-English copy all make it easier for summary tools to understand the message. Over-designed emails with image-heavy hero sections or vague button copy like “Click Here” may still look polished, but they can be harder for AI tools to interpret.

A good rule of thumb is to write the email so a busy person could skim the first few lines and understand the point. Conveniently, that also helps the AI summary get it right.

SMS and Message Threads Are Part of This Shift Too

This change is not limited to email. SMS, RCS, app notifications, and customer service threads are also being condensed by AI tools. A carefully timed three-message SMS flow may appear to a user as one short recap. A long support thread may be reduced to a single suggested action.

That means the first message in a sequence carries more weight. Marketers should avoid saving the main value for message two or three. If the key point is a discount, deadline, appointment reminder, or next step, it should show up right away.

This also applies to links. Long, messy URLs with heavy tracking parameters can get cut off or hidden in some condensed experiences. Tracking matters, but the user-facing message needs to stay clean enough to survive compression.

Do Not Let AI Summaries Flatten the Important Stuff

There are a few areas where marketers need to be especially careful. Legal language, required disclosures, unsubscribe information, and sensitive details often live near the bottom of an email. AI summaries may not surface that information unless the message is structured clearly.

That can become a bigger issue for regulated industries like healthcare, finance, insurance, and legal services. If the summary pulls out the promotional part of a message but misses the necessary context, the email may create confusion. It may also create compliance questions that the original email technically handled, but the summary did not.

Brand voice can take a hit too. AI summaries tend to flatten personality. A clever line may get turned into something generic. A thoughtful explanation may become a dry recap. You cannot fully control how every platform summarizes your message, but you can give it better material to work with by making the top of the email clear, useful, and still recognizably yours.

What Marketers Should Test Next

Create seed accounts in Gmail, Outlook, and Apple Mail. Send your regular campaigns to those accounts and review what the summaries say. Look at whether the main point is accurate, whether the CTA appears clearly, and whether anything important gets lost.

From there, test your lead sentence the same way you would test a subject line. Try one version that starts with a clear benefit and another that uses a softer intro. See which one produces the better summary and the better downstream engagement.

Marketers should also pay closer attention to assisted conversions, reply quality, and traffic patterns that may not line up neatly with clicks. AI summaries may change how people engage, so the measurement needs to get a little more flexible too.

The Inbox Is Still Human, Even When AI Gets There First

AI summaries are not replacing email strategy. They are raising the bar for clarity. The emails that hold up best will be the ones that get to the point quickly, use simple structure, and make the next step easy to understand.

TL;DR: Marketers do not need to write for machines at the expense of people. They need to write emails that are clear enough for the machine to summarize and useful enough for the person to care.

06/02: AI Mode Ads Are Changing Google Search

Google is bringing ads deeper into AI-powered search experiences, including AI Mode. That means paid search is starting to look less like a list of keyword-triggered ads and more like part of a longer, more conversational research process.

As Google rolls out these changes, advertisers need to recognize that weak campaign inputs are becoming harder to hide. If your campaigns and brand messaging don’t clearly explain who you serve and why someone should choose you, Google’s automation has to infer more about your business, audience, and value proposition. The result probably won’t be one dramatic failure. It’s more likely to show up as vague ad copy, odd matches, or leads that look fine in Google Ads but don’t turn into real opportunities.

What Changed With Ads In AI Mode

AI Mode is Google’s AI-powered search experience that lets people search in a more natural, conversational way. Instead of typing a short keyword and scrolling through a list of links, users can ask detailed questions and receive AI-generated responses that help them research a topic step by step. Ads can now appear within those responses and are labeled as “Sponsored.”

Because of that, ads are no longer limited to competing for attention on a traditional search results page. They may appear while someone is actively researching a problem, comparing solutions, or asking follow-up questions. In other words, advertisers are becoming part of a longer decision-making process rather than simply showing up for a single keyword search.

At the same time, Google is encouraging advertisers to use more AI-driven campaign types, including AI Max and Performance Max. Dynamic Search Ads will transition to AI Max beginning in September 2026, signaling Google's continued move toward automation and machine learning.

What does that mean for businesses? Google is taking on a bigger role in deciding when your ads appear, what messaging is shown, and which users are most likely to see them. That can be a good thing when your campaigns provide clear signals about your business, audience, and goals. But when the system is working from generic messaging, unclear objectives, or weak landing pages, it has to make more assumptions, and those assumptions don't always lead to the right results.

Why AI Mode Ads Hit B2B Advertisers Differently

B2B advertisers have less room for generic messaging. A consumer brand selling a straightforward product may be able to get by with broad language for a while. A company selling industrial equipment or specialized software cannot rely on phrases like “custom solutions” and “trusted partner” and expect Google’s AI to magically understand the nuance.

This is where AI Mode raises the stakes. In traditional paid search, teams had more direct control over the exact copy a buyer saw. With AI-driven formats, more of that control moves upstream. The campaign structure, landing pages, creative assets, audience signals, and conversion goals all help shape what Google understands about the business.

That doesn’t mean humans are out of the loop, but it does mean the human work has to happen earlier and more intentionally. Your campaign inputs need to clearly explain what the business does, who it serves, what makes a lead valuable, and which claims the brand should avoid. Weak inputs create more room for weak outputs.

For B2B companies, this can become a real performance problem. A campaign might drive form fills that technically count as conversions, but sales may quickly realize those leads are the wrong fit. Or the ads may sound polished enough on the surface while still missing the specifics that make the company worth choosing. In an AI-shaped search experience, clarity becomes part of performance.

What Businesses Should Review Now

Before AI Mode ads become another confusing performance variable, businesses should take a closer look at the pieces feeding their paid search campaigns. The goal is to make sure Google has enough useful information to represent the business accurately.

Start with the brand brief. Can your team clearly explain what you do, who you help, what makes a good lead, and what your brand should never say? If the answer takes four meetings and a whiteboard, your campaigns may not have enough clarity.
Look at your creative assets. Headlines, descriptions, images, and landing page copy should sound specific to your business. If a competitor could copy the same language and use it without changing much, the raw material probably is not strong enough for AI-assisted ad formats.
Review your conversion signals. If campaigns are optimizing toward clicks, basic form fills, or low-quality leads, Google may learn how to find more of the wrong people. For B2B companies, performance should be tied as closely as possible to qualified leads, pipeline, or another meaningful business outcome.
Check the landing page experience. AI Mode searches are often more specific than traditional keyword searches, so the destination page has to do more than repeat a broad service pitch. If someone asks a detailed question, the page they land on should help answer it.

The common thread is clarity. Google’s automation can do a lot, but it still needs good direction. The more specific your campaign inputs are, the less room there is for the system to guess.

Better Inputs Make AI-Powered Ads Easier To Trust

AI Mode ads are another reminder that automation is only as good as the strategy behind it. Google can assemble, match, and test more than ever, but it still needs clear direction from the business.

For B2B teams, now is a good time to review the setup before performance gets harder to diagnose. If your current paid strategy was built around short keywords, generic copy, and loose conversion goals, AI Mode is likely to expose those gaps.

06/05: Consistent Brand Messaging for AI Search

AI‑powered search experiences like Google’s AI Overviews, ChatGPT answers, Perplexity summaries, and Gemini snapshots no longer rank a tidy list of pages. They pull answers from whatever places they trust. When your website, product listings, LinkedIn page, and PR releases describe you differently, those systems get confused. The result? Fewer brand citations, more competitor mentions, and descriptions you never approved.

Why AI Search Rewards Consistency

Large language models piece together entities by matching facts across the open web. If your signals line up, same company name, tagline, product lineup, value prop, confidence goes up and you get cited. If they conflict, the model either ignores you or fills the gap with someone else’s information.

A recent SEMrush survey found that 37% of marketers see competitors quoted in AI answers more often than their own brands because of fragmented messaging. That matters because AI answers now give people everything they need without requiring a click. Every brand mention inside those answers is earned exposure, credibility, and assisted conversion, but only if the model can recognize you.

Analyze Your Brand Signal Gaps

Before you can fix anything, you need a clear map of what the world sees. Gather every public touchpoint and compare them side‑by‑side.

Quick Signal Audit Checklist

Website vs. LinkedIn “About” copy – Are the elevator pitches identical?
Product descriptions on marketplaces – Do they match the specs on your site?
Author bios across guest posts – Same titles and expertise statements?
Review‑site blurbs (G2, Capterra, Glassdoor) – Consistent naming and positioning?
Knowledge Panel / Wikidata / Wikipedia entries – Accurate founding year, HQ, leadership?

Run a few live tests while you audit. Ask Google’s AI Overview “What does [Your Brand] do?” or prompt ChatGPT with the same. Note any wording that feels off, and trace it back to the source.

Unify Your Brand Entity Across Platforms

Start by locking down the non‑negotiables: company name, tagline, one‑sentence elevator pitch, product names, founders, headquarters, and founding year. Write them down in a shared doc and an internal style guide.

Next, roll those facts out everywhere:

On‑site schema – Use Organization, Product, and Person markup so crawlers see structured clarity, not just prose.
Social bios and link hubs – Keep taglines identical, refresh outdated banners.
Press release boilerplates – Replace legacy copy in PR templates.

When Google’s AI Features documentation says, “clear author and organization information helps us surface the right content,” this is what they mean.

Reinforce Authority with Supporting Signals

Consistency alone won’t make you the chosen citation. You still need authority:

High‑quality backlinks from relevant publications and partners
Credible third‑party reviews that echo your positioning
Expert commentary in industry round‑ups, podcasts, and webinars
Topical depth on your own site, e.g., pillar pages that prove expertise

Brands with authoritative link profiles get a 15% higher share of AI citations. Authority and coherence work together: one earns trust, the other removes doubt.

Governance: Keeping Coherence as Teams and Channels Evolve

Signal coherence isn’t a one‑off sprint; it’s a habit. Put guardrails in place:

Living brand guidelines that spell out core facts and approved phrasing
Cross‑functional content calendars so product, marketing, and PR stay in sync
Pre‑launch checklists for new channels to apply the guidelines before content goes live

Without governance, every new intern, agency, or platform reset can unravel months of careful alignment.

Practical Next Steps

Run the audit checklist this week and list every inconsistency.
Patch the big gaps first—name, tagline, schema, social bios.
Align your upcoming content calendar so new posts reinforce the unified story.
Monitor AI citations quarterly, and refresh guidelines whenever you launch a new product or channel.

Stay Visible with Consistent Messaging

Maintaining consistent brand messaging won’t trend on LinkedIn or win a marketing award, but it will help determine whether AI search tells your story or forgets you altogether. Get it right today and you’ll own the narrative even when no one clicks through to read the details.

Aztek Marketing News Roundup (05/25 - 05/29)

Tue, 26 May 2026 11:27:01 Z

This week's topics:

Google Killed FAQ Rich Results. Here's What to Do About It.
Your Buyers Are Using AI To Research Vendors. Are You Showing Up?

05/26: Google Killed FAQ Rich Results

If you've noticed some chatter in SEO circles this month about FAQ schema, structured data, and whether rich results are "dead," here's what actually happened and what it means for your website.

On May 7, Google permanently removed FAQ rich results from search. Those expandable question-and-answer dropdowns that used to appear beneath some organic listings, the ones that let a page take up more real estate on the results page and potentially pull in extra clicks, are gone. No exceptions, no phased approach, Google just updated the Search Central documentation and killed it off.

The reporting infrastructure will follow in stages:

May 7, 2026: FAQ rich results stop appearing in Google Search
June 2026: FAQ rich result report and Rich Results Test support removed from Search Console
August 2026: FAQ rich result support removed from the Search Console API

If you have automated dashboards or reports pulling FAQ data through the API, those need to be updated before August or they'll start returning silent errors.

What You Don't Need To Do

Before getting into what this means for your strategy, it's worth clearing up the overreaction that's been circulating. You don't need to strip FAQ structured data from your pages. Google's own documentation confirms that unused structured data doesn't cause search problems, and the company explicitly stated it will continue using FAQPage markup to help understand page content.

The display feature may be gone, but the underlying machine-readable signal isn't. If your development team is asking whether to do a full audit and removal, the answer is you likely may not have to at all. You also don't need to remove FAQ sections from your service pages or product pages. Google didn't say question-and-answer content is unhelpful, it just retired the visual SERP enhancement.

Where That Effort Should Go Instead

FAQ rich results were attractive because they created a shortcut to structure your content a certain way, apply the right markup, and earn more visible real estate in search results. That formula worked for a while, but the feature was always a wrapper around something that matters more: is your content actually answering the specific questions buyers have when they're trying to figure out if your company is the right fit?

AI Overviews, Google's AI Mode, ChatGPT, and Perplexity are all pulling from content that clearly and specifically answers real questions. The pages earning citations in AI-generated answers aren't the ones with a generic FAQ block bolted on at the bottom. They're the ones where the content itself is built around answering something precise and useful. That could be anything from the kind of questions your sales team hears on the first call to the thing your buyer types into a search bar at 9pm before deciding whether to request a demo.

If a buyer asks an AI tool what to look for when choosing a company in your category, and your website can't answer that question clearly enough to be cited, you're not just losing a SERP feature. You're losing a consideration-stage conversation before it ever starts.

Three Practical Next Steps

Don't rush to remove FAQ schema. There's no SEO penalty for leaving it in place, and it continues to function as a content comprehension signal.
Do audit whether your existing FAQ content is actually answering real buyer questions, or whether it's structured around keywords rather than decisions.
Redirect the effort toward your service and solution pages. The questions your sales team fields every week, about process, pricing, timelines, and what makes you different, belong on those pages in plain language. That's what earns visibility in AI answers. It's also what converts the visitors who do make it to your site.

The businesses that handle this transition well won't be the ones who found a new schema trick. They'll be the ones who finally got around to making their website answer the questions their buyers actually have.

05/27: Your Buyers Are Using AI To Research Vendors. Are You Showing Up?

B2B buyers aren’t waiting for your sales team to explain who you are anymore. A growing number of them (73% to be exact) are using AI tools like ChatGPT and Perplexity to research vendors, build shortlists, compare services, check reviews, and get a feel for pricing before they ever land on a company website.

The research process that used to require bouncing between a dozen websites can now happen inside one AI conversation. A buyer can ask, “Who are the best agencies for this?” or “How does Company A compare to Company B?” and get a summarized answer in seconds. By the time they click through to your site, they may already have an opinion about whether you’re a good fit.

The Gap Between How Buyers Research And How Marketers Measure

Here’s where things get messy. Buyers are already using AI as part of the vendor research process, but most marketing teams still aren’t tracking whether they show up in those answers. Instead, they are measuring success through the usual suspects: Google rankings, website traffic, and form fills. Those metrics matter, but they don’t tell the full story anymore. They miss what’s happening earlier in the buying process, especially when an AI tool is shaping the buyer’s shortlist before that person ever visits your website.

If your analytics setup doesn’t separate AI-referred traffic from other channels, you’re probably missing useful context. Someone who finds you through ChatGPT, Perplexity, Gemini, or another AI tool may arrive with more intent than a typical search visitor. They’ve already asked questions and seen some kind of comparison, so they’re not starting from zero when they reach your site.

What AI Tools Are Actually Doing With Your Content

When a buyer asks an AI tool to compare vendors or explain different service options, the tool has to build its answer from whatever it can find online. That might include your website, blog posts, case studies, third-party mentions, reviews, directories, and any other credible source connected to your business.

Then it decides whether you belong in the answer. There’s no ad buy for that placement, and there’s no quick bid adjustment that gets you into the recommendation. AI tools are looking for clear, specific, consistent information they can understand and summarize. If your online presence is vague, outdated, or thin, the tool has less to work with.

That means your content has a new job. It still needs to help people, and it still needs to rank. Now it also needs to give AI tools enough context to accurately describe what you do, who you help, and why you’re different from the other companies in your space.

What This Means For B2B Marketing Teams

The B2B buying journey is getting a little strange. Buyers may contact fewer vendors directly, but they’re doing more independent research before that contact happens. By the time someone fills out your form, they may already have compared you against competitors and formed a pretty clear opinion.

Your service pages play a big role in that process. They’re not just SEO pages or sales enablement copy. They’re source material. If those pages only say broad things like “custom solutions” or “full-service support,” an AI tool won’t have much substance to pull from. Specific service pages that explain who you work with, what problems you solve, how your process works, and what makes your approach credible give both people and AI tools something useful to go on.

Tracking is part of this too. If you’re not already looking at traffic from ChatGPT, Perplexity, Gemini, and similar tools, it’s a good time to start. It won’t show you every AI-influenced touchpoint, but it can help you understand whether this channel is already sending people your way and what those visitors do once they arrive.

Don’t Throw Out What Already Works

This doesn’t mean SEO is dead. It also doesn’t mean you need to chase a brand-new acronym every time someone on LinkedIn invents one.

Strong SEO, useful content, clear service pages, case studies, reviews, and a well-structured website all still matter. In fact, those are the same inputs AI tools rely on when they’re building answers. Good marketing fundamentals are not becoming less important. They’re becoming useful in more places.

The real question is whether your content is specific enough to hold up when someone else summarizes it. Can an AI tool clearly understand what you do? Can it tell who you’re best for? Can it find proof that backs up your claims? Can it separate you from a generic description of your industry?

Those have always been good marketing questions. The difference now is that buyers may be asking them through an AI tool before they ever ask you.

Aztek Marketing News Roundup (05/18 - 05/22)

Mon, 18 May 2026 08:00:00 Z

This week's topics:

Google’s First AI Search Guide Confirms What Good SEOs Already Knew
LinkedIn’s Algorithm Is Punishing the Old B2B Playbook
Google’s Biggest Search Redesign in 25 Years Means Fewer Clicks for Websites

05/18: Google’s First AI Search Guide Confirms What Good SEOs Already Knew

Google just gave marketers a much-needed reality check on AI search. The company released its first official guide to optimizing websites for generative AI features in Google Search, including AI Overviews and AI Mode. For business owners and marketing managers, the timing is helpful. Over the past year and a half, plenty of vendors, consultants, and LinkedIn experts have treated AI search like a brand-new discipline with new rules and new acronyms. From Google’s perspective, optimizing for generative AI search is still good ol' SEO.

AI Search Still Depends on SEO Fundamentals

Search has changed, just not in the way a lot of AI SEO sales pitches make it sound. AI Overviews and AI Mode can give people a summarized answer instead of sending them straight to a list of links, but Google is still pulling from the web pages it can access, understand, and trust.

That means the basics still have to be in place. If your site is hard for Google to crawl, your service pages are thin, or your content sounds like it could belong to any company in your industry, an AI-focused file or a few “LLM-friendly” rewrites are not going to fix the real issue.

Good SEO still starts with a healthy website, clear page structure, and content that helps people make a better decision. The packaging around search may be changing, but the shortcuts are not suddenly more useful just because AI is involved.

Google Called Out the AI SEO Noise

Google’s guide is especially helpful because it clears up what site owners don’t need to chase. It calls out several tactics that have been floating around the AI SEO world, including creating llms.txt files, “chunking” content into smaller pieces for AI systems, rewriting content with AI-specific phrasing, chasing inauthentic brand mentions, and adding special schema just for generative AI search.

AEO and GEO may be useful labels for talking about how search is changing, but they are not separate magic systems with their own totally different rules. Showing up in AI-powered Search still depends on the same SEO foundation that helps a site show up in traditional results.

That doesn’t mean every conversation about AI visibility is useless. It does mean businesses should be careful when someone says they need a completely separate strategy to show up in AI search. A lot of what’s being sold as “AI SEO” is just SEO with a new label, and some of it is busywork Google has now directly said does not matter.

The Real Shift Is Content Quality

The bigger change is not that businesses need to chase a new algorithm. It’s that generic content has even less room to hide. Google’s guide puts a heavy emphasis on creating valuable, non-commodity content, which means content with a real point of view, firsthand experience, useful examples, and information your audience can’t get from any other website saying the same basic thing.

Google specifically warns against recycling what already exists online or publishing content that a generative AI tool could easily produce. For a business, that means a basic “5 Tips for Choosing a Contractor” post probably is not enough. A better piece of content might explain what your team looks for during a site visit, which project issues create the biggest delays, or what questions customers should ask before signing a contract.

That kind of content is harder to fake because it comes from actual experience. It’s also where AI search raises the bar. It rewards the work businesses should have been doing anyway: answering real customer questions, showing expertise, making pages easier to understand, and giving Google something worth citing.

Stop Chasing Acronyms. Start Improving the Website.

There is no magic AI search playbook hiding behind a new acronym. Businesses that want to show up in AI-powered search results should focus on the same core areas that have always separated strong sites from weak ones.

Make sure important pages can be crawled and indexed
Clean up thin or repetitive content
Write from real expertise
Organize pages so people can actually use them
Add helpful visuals when they make the content stronger

AI search did not make SEO obsolete. It made weak SEO easier to spot. So no, you probably don’t need to panic-buy an AEO strategy. You need a better website, better content, and a clearer reason for Google to trust what you have to say.

05/19: LinkedIn’s Algorithm Is Punishing the Old B2B Playbook

The standard B2B LinkedIn routine has been pretty predictable. You publish a blog post, share the link from the company page, maybe ask people to comment if they agree, and measure success by likes, clicks, and impressions. Plenty of companies still run that same playbook today.

The problem is that LinkedIn has moved on. Recent 2026 algorithm analysis from Digital Applied, DSMN8, Whitehat SEO, and other LinkedIn-focused sources points to the same larger shift: LinkedIn is rewarding content that keeps people engaged inside the platform and penalizing tactics that feel like shortcuts. External links, engagement bait, generic company updates, and copy-paste employee posts are all becoming weaker ways to earn reach.

For B2B companies, this is more than a small platform tweak. LinkedIn is changing what gets seen and who gets trusted, which means the old habit of pushing brand content from the company page is starting to work against the companies still relying on it.

LinkedIn Is Rewarding Attention, Not Quick Clicks

LinkedIn used to be easier to game. If a post got enough early reactions, it had a better chance of spreading. That encouraged a lot of shallow content: quick polls, vague questions, “comment YES” prompts, and posts written mainly to trigger activity instead of saying anything useful.

The newer version of LinkedIn’s algorithm appears to care much more about whether people actually spend time with content. Several 2026 analyses describe a shift toward what is often called a “Depth Score,” which looks beyond likes and quick clicks. LinkedIn wants to know whether someone paused, read, saved, shared, or left a thoughtful comment because the post was genuinely useful.

That means the easy engagement tricks are losing value. A dozen one-word comments may look active, but they do not signal the same level of quality as a discussion where people are adding context, asking follow-up questions, or sharing the post privately with someone who might care. LinkedIn is trying to separate real interest from manufactured activity, which means brands need to stop writing posts like they are trying to trick the feed.

External Links Are Getting in the Way

The classic company-page post usually has one job: send people somewhere else. That might be a blog post, landing page, press release, webinar registration page, or case study. The content may be useful, but from LinkedIn’s perspective, the post is asking users to leave the platform.

That creates a problem for organic reach. Reports suggest that posts with off-platform links are seeing reduced distribution, especially when the link is the main value of the post. LinkedIn wants people to stay in the feed longer, so native content has a better chance of earning attention. That does not mean links are useless, but it does mean the link can no longer do all the work.

A stronger approach is to turn the best part of the asset into a complete LinkedIn post first. Instead of posting “Check out our latest blog,” a company can share the main takeaway, a useful chart, a short point of view, or a practical lesson from the article. The link can still exist, but the post itself needs to deliver value before asking for a click.

Company Pages Are No Longer the Whole LinkedIn Strategy

Company pages help prospects validate your business and give people a place to understand what you do. They are not, however, where most organic visibility seems to be happening anymore.

Recent reports point to a major imbalance between company-page reach and personal-profile reach. Even when the exact numbers vary by source, the pattern is clear enough: LinkedIn is giving more visibility to real people than brand logos. That creates a challenge for B2B companies that rely on the company page to carry the full content strategy.

A founder’s take on an industry shift or a subject matter expert’s practical lesson from the field can feel much more useful than a polished company update that sounds like it went through three approval layers. That does not mean every employee needs to become a LinkedIn influencer, but companies need a better way to help their experts share what they know in a way that actually sounds like them.

This is especially important for B2B companies with longer sales cycles. Buyers may not be ready to fill out a form the first time they see your post, but they may start recognizing your team’s perspective over time. That kind of trust rarely comes from a logo posting a link and hoping the algorithm does the rest.

What B2B Companies Should Do Now

The fix is not to abandon LinkedIn or stop sharing content from your website. The better move is to rethink how LinkedIn fits into the broader content strategy. Blog posts, case studies, webinars, and guides can still fuel your LinkedIn presence, but they need to be repackaged for the feed instead of dropped in as links.

A stronger LinkedIn strategy should focus on a few practical shifts:

Turn blog posts into native insights before sharing the link.
Use company pages for credibility, but build reach through personal profiles.
Replace engagement bait with better questions and stronger points of view.
Track saves, comments, shares, and profile-driven conversations instead of only likes.
Give internal experts a repeatable way to contribute without making content creation feel like a second job.

These changes are not about chasing the algorithm for the sake of it. They are about matching the way LinkedIn now rewards content. If the platform is prioritizing depth, discussion, and trusted individual voices, then B2B companies need to build around those signals.

The Lazy Version of LinkedIn Stopped Working

LinkedIn still has plenty of value for B2B companies, but the old “post the blog link on the company page” strategy is no longer enough. In some cases, it may actively hold organic reach back. The companies adapting fastest are the ones treating LinkedIn as a place to share expertise, not just distribute links.

If your LinkedIn reach is slipping, the issue may not be that your audience disappeared. Your strategy may still be built for a version of the platform that no longer exists. The brands that adjust now will have a better chance of staying visible as LinkedIn continues to reward useful, human content over link drops and empty engagement.

05/21: Google’s Biggest Search Redesign in 25 Years Means Fewer Clicks for Websites

Google has had a busy week in search, and marketers should be paying attention. Earlier this week, the company released its first official guidance on optimizing websites for AI-powered search features like AI Overviews and AI Mode. Then Google followed that guidance with a much bigger announcement at I/O 2026.

The company introduced what it described as the biggest redesign of Search in more than 25 years, moving the experience further away from a simple search bar and closer to an AI-powered interface built around Gemini. Users will be able to ask more complex questions, interact with AI-generated tools, create mini-apps in natural language, and rely on information agents that monitor the web for them.

Google Search Is Becoming More Self-Contained

For years, Google Search followed a pretty familiar pattern. Someone typed in a question, scanned the results, clicked a link, and landed on a website that hopefully gave them what they needed. That path still exists, but Google’s latest redesign makes it clear the company wants more of the experience to happen inside Google itself.

AI Mode gives users a more conversational way to explore a topic. Generative UI can build custom tools or interactive experiences based on what someone asks. Information agents can monitor the web in the background and surface updates when something changes. For users, that's convenient, but for businesses that rely on organic traffic, it creates a real challenge.

More Search Activity May Not Mean More Website Traffic

AI Overviews now reach 2.5 billion monthly users, and AI Mode has crossed 1 billion monthly users. AI-powered search is quickly becoming part of the normal Google experience.

That changes how businesses should think about organic search performance. More people may be searching, and your brand may still appear somewhere in the search experience, but website traffic can still drop anyway.

That feels backward because marketers have spent years connecting search demand with website visits. If more people searched for a topic and your site ranked well, you had a better chance of earning traffic. AI Overviews and AI Mode weaken that connection because Google can answer more questions directly on the results page.

This doesn't mean every search becomes zero-click. People will still visit websites when they need to compare vendors, request a quote, book an appointment, make a purchase, or dig deeper before making a decision. The bigger risk is that basic informational traffic will keep getting squeezed.

The Shift Is From Ranking to Being Referenced

Traditional SEO has often focused on earning the click. That still matters, especially for high-intent searches where someone is ready to take action. A strong ranking can still bring in qualified visitors, and a well-built page can still turn that visitor into a lead.

The new layer is visibility before the click. If Google is summarizing answers and comparing options inside Search, your business needs to earn enough trust to be included in that answer. That means content has to do more than target a keyword. It has to give Google something specific and credible to work with.

A strong service page should explain what the service includes and who it is right for. A useful FAQ should answer the questions your team actually hears from customers. A good blog post should share a real point of view instead of repeating the same surface-level advice already sitting on dozens of other websites.

What Businesses Should Do Next

If your website traffic strategy relies heavily on people clicking basic informational blog posts from Google, that model is under pressure. If your organic strategy is built around helpful service pages, strong local visibility, useful comparison content, and clear conversion paths, you are in a better position.

Start by looking at which organic pages actually drive qualified leads. Some traffic loss may not hurt much if it comes from broad searches that rarely turn into business. Traffic loss becomes a bigger issue when it affects pages that bring in serious prospects or support the buying process.

This is also a good time to strengthen the pages closest to revenue. Service pages, product pages, location pages, case studies, FAQs, and comparison content all deserve attention. These are the pages that help people understand whether your business is the right fit, and they give Google more reliable information to pull from when it builds AI-powered answers.

The Click Is No Longer the Whole Story

Organic visibility is changing, not disappearing. Businesses should still care about rankings and traffic, but those metrics no longer tell the full story. A brand can be visible in search without earning the same number of clicks it might have earned a few years ago. Strong SEO still gives your business the best chance of being found, trusted, and chosen, even when the first interaction happens directly inside Google.

Aztek Marketing News Roundup (05/11 - 05/15)

Mon, 11 May 2026 08:00:00 Z

This week's topics:

Ground Control to Claude: What the AI Compute Arms Race Means for Your Marketing Stack
AI Agents 101: What They Are, How They Work, and Why They Matter
TikTok’s Pay-Or-Consent Test Puts A Price On Privacy: What Marketers Need To Know

05/11: Ground Control to Claude: What the AI Compute Arms Race Means for Your Marketing Stack

Last week, Anthropic signed a deal to grab every watt and GPU inside SpaceX’s new Colossus 1 data-center cluster. The ink was barely dry before Anthropic doubled Claude Code’s usage caps and removed peak-hour throttling for Pro and Max users.

A day later OpenAI ended its years-long Azure lock-in and landed GPT-5.5 on Amazon Bedrock less than 24 hours after the exclusivity clause expired. This was an equally loud signal that top-tier models now chase whichever cloud can feed their appetite fastest.

If the “space race” metaphor ever felt tongue-in-cheek, it’s looking pretty literal right now. Compute capacity has become rocket fuel, and the launch window is always now.

Why Compute Is the New Battleground

Wall Street analysts think U.S. data centers will need about 74 gigawatts of power by 2028, but our current grid can only spare roughly 24 gigawatts. In other words, demand may run 50 gigawatts ahead of what’s available. The companies that lock down those chips will decide how quickly the rest of us get new AI features.

What does that mean for marketers? Two things you’ll notice right away:

Usage limits can change overnight. Claude might raise your cap today, but another platform could slam on the brakes tomorrow if it runs out of capacity.
Prices won’t sit still. When power and chips get tight, vendors pass the extra cost along likely as a surprise “fuel-surcharge” on your bill.

What the AI Compute Arms Race Means for Marketing Teams

Latency & Reliability: SEO research, AI-assisted content drafts, even paid-media modeling all slow down when a model starts rate-limiting. Build workflows that can switch over to a second provider when your main one slows down or goes offline.
Budget Volatility: When GPU spot prices jump, vendors pass it through. If your 2026 plan assumes flat per-token costs, rewrite it now. Treat AI spend like cloud hosting: monitor, forecast, and cap.
Vendor Lock-In Risk: The bigger the arms race, the less incentive any one provider has to stay cheap and open. Contracts should spell out minimum throughput, notice periods for price hikes, and clear exit ramps.
Sustainability & Reputation: Using an “in-space” data center sounds futuristic; it also invites scrutiny about energy footprints. Clients will ask. Have an answer.

Your Flight Plan

Audit Your Dependencies
List every place ChatGPT, Claude, or any other model touches content, analytics, or dev workflows. Highlight single-points-of-failure.
Budget for Turbulence
Add a 30% swing factor to AI line items. If the market loosens, great, you’ve found money. If it tightens, you’re not scrambling.
Build a Multi-Model Stack
Most teams already mix Google, Meta, and LinkedIn ads. Treat generative tools the same way. Claude for code, GPT for ideation, open-source for quick lookups. AKA, whatever combination keeps you moving when one engine sputters.
Negotiate SLAs Like Your Uptime Depends on Them (Because It Does)
Ask for guaranteed token throughput, rate-limit notice windows, and price-change caps. If a vendor won’t put it on paper, that’s a data point.
Keep an Eye on Power Politics
New regulations around energy use and recycling credits are coming. A future RFP might require your AI vendor to prove its renewable mix.

Chips & Power: The Real AI Bottleneck

The AI compute arms race isn’t just a spectacle of billionaire handshake deals and gargantuan data centers. It directly affects how fast your content team can ship, how steady your ad-modeling stays, and how predictable your budget really is. Treat it less like sci-fi and more like cloud infrastructure: plan for failure, diversify providers, lock in terms, and stay alert.

In other words, keep your feet on the ground…even when your GPUs are headed for orbit.

05/12: AI Agents 101: What They Are, How They Work, and Why They Matter

An AI agent is software that uses a large-language model (or several models) to pursue a goal on your behalf, not just answer a question. It can reason through multi-step tasks, call external tools or APIs, learn from feedback, and decide what to do next without waiting for the next prompt.

Many people still assume an AI agent is just a fancy chatbot, but that's not the case. A classic chatbot reacts only to the words you type, while an agent uses its autonomy to pursue a goal, decide the next step, and finish the job for you.

Levels of Autonomy: From Reactive Bots to Goal-Seeking Agents

Think of autonomy on a sliding scale, from rigid scripts to self-directed digital coworkers.

Level	What It Does	Practical Example
0 — Scripted Bot	Follows a rigid decision tree with no real AI. Delivers fixed answers and cannot adapt.	“Track my package” support widget that returns status based on a tracking number.
1 — Conversational Assistant	Generates human like responses with an LLM but needs constant prompts and has no tool access or long-term memory.	A GPT style writing aide that drafts paragraphs when asked.
2 — Task Agent	Accepts a single objective, plans a handful of steps, uses one or two tools, then stops when done.	Pulls last week’s analytics data, builds a chart, emails it to the team.
3 — Workflow Agent	Orchestrates a multistep process across several apps, loops on errors, and stores short term context to improve results.	Cleans a CRM list, drafts personalized outreach emails, schedules the sends, then logs performance metrics.
4 — Continuous Agent	Runs around the clock, monitors the environment, triggers workflows when conditions change, and refines its plan over time.	Google’s experimental “Remy,” which is currently being tested to plan errands, update schedules, and message contacts on your behalf.

Most business use cases today fall around Level 2 or 3. They are powerful enough to eliminate grunt work while still operating within clear guardrails.

Under the Hood: Core Components of an AI Agent

Brain (LLM or multi-model stack): Generates plans, interprets results.
Memory: Stores context so the agent doesn’t start from scratch each time. Anthropic’s new “dreaming” technique literally lets agents review prior runs and self-improve.
Toolbox: Secure APIs, databases, and apps the agent can call (e.g., Google Ads, HubSpot).
Orchestrator / Framework: Code that decides when to ask the model, when to call a tool, and how to loop until the job is done (popular options include LangChain, Vertex AI Agent Engine).
Guardrails: Permissions, rate limits, and audit logs so the agent can’t go rogue.

Real-World Examples Already at Work

Paid Media Automation: Google Ads’ new journey-aware bidding draws on agent-like logic to adjust spend toward pipeline, not clicks.
Personal Productivity: Google’s forthcoming Remy watches your inbox, drafts replies, and even orders supplies when it notices the recurring task.
Compliance Monitoring: Finance firms are spinning up niche agents that scan transactions for policy breaches in real time.

Watchouts: Security, Governance, and Brand Safety

Autonomy cuts both ways. An agent that can post on social or move budget can also go off the rails. Before going live, consider:

Limiting scopes: Grant the smallest set of permissions that still lets the agent work.
Logging everything: Keep a trail of decisions for audits and post‑mortems.
Setting human escalation rules: Define when the agent must pause and ask.
Starting in the sandbox: Run simulated data or low‑risk tasks first.

Why AI Agents Matter for Your Business

AI agents are not just chatbots with a few more tricks. They’re goal‑oriented coworkers that can plan, act, and learn within clear boundaries. For most businesses, the smartest move today isn’t to chase sci‑fi aspirations; it’s to pilot a modest agent that frees humans for higher‑value work, measure the results, and scale from there. Get the fundamentals right, and the hype turns into hard numbers.

05/13: TikTok’s Pay-Or-Consent Test Puts A Price On Privacy: What Marketers Need To Know

TikTok is testing a £3.99-per-month ad-free tier for UK users who are at least 18 years old. Paying users won’t see ads and TikTok will no longer use their data for advertising. Users who stay on the free version are treated as having agreed to personalized ads by default.

TikTok is calling this a small-scale subscription test, but the setup looks a lot like the pay-or-consent pilots the company ran in other markets last year. The price point is also telling. At about the same level as TikTok’s annual ad revenue per UK user, the model doesn’t need mass adoption to make financial sense.

TikTok usually doesn’t experiment with its ad business for fun. If the numbers work, the same basic model could show up wherever privacy pressure gets harder to ignore, including the United States. For marketers, that makes this less of a “UK-only platform update” and more of a preview of how privacy pressure could reshape the way paid social campaigns are planned and measured.

Why U.S. Marketers Should Pay Attention

This trend is bigger than TikTok. Meta already moved in this direction across the EU in 2023, offering paid ad-free versions of Facebook and Instagram. TikTok’s test adds another major platform to the pay-or-consent pile, which suggests this may become a go-to response when regulators push platforms to give users a clearer choice.

The regulatory pressure is not staying neatly contained overseas, either. Federal privacy proposals like the SECURE Data Act are still moving through the political process, while state laws are already creating new obligations for businesses. Oregon’s 2026 updates, for example, will require companies to honor universal opt-out signals. That kind of rule can quickly shrink the data pool marketers have relied on for targeting and retargeting.

The economics matter, too. At £3.99 per month, TikTok is putting an actual price on a user’s advertising data. It’s not an abstract privacy debate anymore; it’s a revenue model. If the UK pilot protects revenue without hurting engagement, TikTok or another platform could decide the same trade-off is worth testing in the U.S.

Even if this exact fee never makes it across the Atlantic, the ripple effects still matter. Cross-border brands may see smaller retargeting pools in the UK and EU, lookalike models could get less reliable, and attribution may get a little fuzzier. None of that means paid social stops working, but it does mean marketers need to plan for a world where platform data is less complete than it used to be.

What To Pressure-Test In Your 2026 Plan

Start with reach and CPMs. Model what happens if 10% of your UK or EU audience disappears from TikTok and Meta targeting pools. Then look at how that affects budget, frequency, and performance expectations. This does not need to be perfect forecasting. It just needs to show where your plan gets shaky.

First-party data also becomes more important. When platforms lose visibility into certain users, brands with stronger customer data have more ways to understand demand, build audiences, and measure what’s working. That doesn’t mean collecting everything you possibly can, but it does mean collecting what you can actually use, with a clear value exchange that makes sense to the person handing over the information.

Creative deserves another look, too. If privacy-minded users are the ones most likely to pay for an ad-free experience, the remaining ad-supported audience may be more tolerant of ads, but not more forgiving of bad ones. Stronger hooks, clearer offers, and more useful creative will matter more as targeting gets less precise.

Policy calendars should be part of your planning rhythm. Watch state bills that mention universal opt-out signals, keep an eye on federal privacy proposals, and pay attention when major platforms test new consent models. The next change that affects your addressable audience may show up as a compliance deadline before it shows up as a platform announcement.

Paid Social Needs A Privacy Backup Plan

TikTok’s pay-or-consent test is another sign that ad-funded targeting is getting more expensive, more regulated, and less automatic. Marketers who treat privacy as a box to check will spend a lot of time reacting while marketers who treat privacy as a planning constraint will be in a better spot.

That means building around stronger first-party data, clearer measurement, and creative that can work without perfect targeting. Not exactly glamorous work, but very much the kind of work that keeps paid media from getting knocked sideways every time a platform or regulator changes the rules.

Aztek Marketing News Roundup (05/04 - 05/08)

Tue, 05 May 2026 08:00:00 Z

This week's topics:

When Convenience Hurts: The Hidden SEO Costs of Drag-and-Drop Site Builders
TikTok Search Ads Campaign: What Marketers Should Know
Google AI Overviews Links Get a Boost: What Marketers Need to Know

05/05: When Convenience Hurts: The Hidden SEO Costs of Drag-and-Drop Site Builders

DIY site builders like Wix, Squarespace, and Weebly all promise slick templates and launch-in-a-day convenience. That’s great...until you need search traffic. Beneath the drag-and-drop polish lurks a cocktail of SEO limitations that can stall growth the moment you outgrow “just having a website.”

Core Web Vitals: Speed You Can’t Fix

Google’s page-experience system puts real weight on how fast your pages load. In recent tests, Squarespace sites averaged a painful 8.79 seconds until the main content finished loading, while Wix landed at 5.2 seconds. Both of these are far slower than leaner custom builds.

Google’s own documentation makes it clear: slow vitals lower the odds of ranking well. Since most of these drag-and-drop builder themes bundle hefty JavaScript, third-party fonts, and video backgrounds you can’t easily trim, you inherit performance debt you can’t pay down.

Structured-Data Roadblocks

When you search Google and see things like recipe cards, star ratings, or handy “How-To” steps right in the results, that’s because the website added special labels in its code called structured data. Think of them as little sticky notes that tell Google, “This part is a product price,” or “This is a customer review.”

Here’s the catch:

Sites like Wix and Squarespace only let you use a short menu of those labels. If you need anything beyond the basics, you’re out of luck.
Without the right labels, your page shows up as a plain blue link while competitors have stars, images, or FAQ drop-downs.
As Google rolls out more AI-powered search features, those extra visual bells and whistles matter even more for clicks.

Drag-and-drop builders make it easy to get online, but their label limits keep you from claiming the actual search results that drive traffic.

Slow, Heavy Pages Make Google Visit Less Often

Picture Google’s crawler as a delivery driver with a tight schedule.

Light pages = quick drop-off. The driver scans your page, grabs what’s needed, and moves on.
Heavy pages = time drain. Drag-and-drop builders pack each page with extra scripts and widgets you never see. The driver now has to open every box to find the real content. That slows everything down.

Because the driver (Googlebot) has millions of stops, it spends less time on slow houses. Fewer new pages discovered, slower updates indexed, weaker rankings, purely because your site is weighed down by code you can’t remove.

Technical Levers You Can’t Reach

When you first launch a website, “publish and go” feels fine. The moment you compete for tougher keywords, though, search engines expect clearer signals you can’t always send from a drag-and-drop dashboard.

Setting	What It Does	Why You’ll Want It
Canonical tag	Declares which near-duplicate URL is the one Google should rank.	Prevents your own pages from stealing traffic from each other when you reuse content or add tracking parameters.
robots.txt rules	Blocks crawlers from low-value areas (e.g., staging folders, endless filter URLs).	Keeps Google focused on pages that deserve authority instead of wasting crawl budget on junk.
hreflang attribute	Directs visitors (and Google) to the correct language or regional version.	Essential once you translate pages or target multiple countries.

Most drag-and-drop builders hide these options behind paywalls or guardrails. Wix, for example, allows robots.txt edits but disclaims responsibility if changes tank visibility. On small, local sites that may never matter. Once you expand, these limits create real friction:

Duplicate pages fight for the same query; rankings drop.
Googlebot spends its crawl time on parameter URLs you never intended to rank.
International visitors land on the wrong language version and bounce.

If growth is on your roadmap, confirm that your platform lets you control canonical tags, robots.txt, and hreflang without work-arounds. Otherwise, plan for a move before those missing settings start costing organic traffic.

Invest Early, Save Later: Key Takeaways

Drag-and-drop builders earn their keep when the goal is “get online fast,” but the trade-offs add up:

Speed matters. Heavy code and widgets drag Core Web Vitals below Google’s comfort zone.
Visibility matters. Limited structured-data options and missing advanced settings keep you out of rich results and slow growth.
Control matters. Once you need duplicate-page fixes, robots.txt rules, or multilingual targeting, the dashboard stops short.

If long-term search traffic is on your roadmap, sometimes the smartest move is to invest in a flexible, performance-focused build from day one so that quick start turns into an expensive redo.

05/06: TikTok Search Ads Campaign: What Marketers Should Know

TikTok has quickly become a place where people search for answers, product ideas, recommendations, reviews, tutorials, and inspiration. For advertisers, that opens up a different kind of opportunity than the typical “show up in the feed and hope someone stops scrolling” approach.

With TikTok Search Ads Campaigns, brands can now reach people closer to the moment they’re actively looking for something. Someone searching “best running shoes for beginners” or “easy weeknight dinner ideas” is showing a different level of interest than someone casually watching whatever the algorithm serves next. They may not be ready to buy right away, but they’re raising their hand.

What Changed With TikTok Search Ads?

Before its recent update, TikTok search ads were more limited. Advertisers could opt into search placement, but they didn’t have the same level of control that paid search advertisers are used to seeing in platforms like Google Ads.

Now, TikTok Search Ads Campaigns work more like a true search campaign. Instead of only relying on TikTok’s broader delivery system, advertisers can build campaigns around the words and phrases people are actually typing into the app.

Some of the biggest changes include:

Keyword targeting, so advertisers can choose the searches they want to show up for
Negative keywords, so they can avoid irrelevant or wasteful searches
Search-focused reporting, so teams can see which searches are driving activity
Dedicated campaign setup, so search is treated as its own strategy instead of a side placement
Creative options that still fit the TikTok experience, including video and carousel assets

Why TikTok Search Ads Are Worth Paying Attention To

Most TikTok advertising is built around discovery. Someone is watching videos, your ad appears in the feed, and the creative has to earn attention quickly. That can work pretty well, especially when the ad feels native to the platform and gives people a reason to keep watching.

Search ads work a little differently. They let you show up when someone is already looking for information. The user has typed in a question, need, product category, or problem. Your ad can meet them in that moment with something useful.

This does not mean TikTok Search Ads should replace Google Ads, Meta campaigns, or other paid media channels. A more realistic way to think about them is as another layer in your paid strategy. If your audience is already using TikTok to research products or ideas, search campaigns can help you capture some of that demand before they move elsewhere.

Who Should Test TikTok Search Ads First?

TikTok Search Ads will not be the perfect fit for every brand right away. Search behavior varies a lot by industry, audience, product type, and buying journey. Some categories naturally lend themselves to TikTok search, while others may still perform better on more traditional platforms.

Brands that may be strong early fits include:

Ecommerce brands with visually interesting products
Beauty, skincare, fashion, fitness, food, and lifestyle brands
Companies already getting engagement from TikTok content
Brands with products people compare, research, or review before buying

More niche B2B companies, highly regulated industries, or brands with very long sales cycles may still be able to use TikTok Search Ads, but they should start carefully. A small test can help show whether people are actually searching for relevant topics on TikTok before the brand invests more heavily.

A useful gut-check question is: would someone realistically search for this topic on TikTok? If the answer is yes, there may be enough intent to justify a pilot. If the answer is “probably not,” the brand may be better off focusing its budget elsewhere first.

How To Measure Whether TikTok Search Ads Are Working

Clicks are easy to look at, but they do not tell the whole story. A campaign can get a lot of clicks and still fail to drive meaningful business results. For TikTok Search Ads, you will want to look at both performance metrics and the quality of the search activity behind them.

The most useful metrics to review include:

Conversions
Cost per acquisition
Return on ad spend
Search terms
Assisted conversions
Performance compared to other paid channels

Search terms are especially valuable because they show what people actually searched before seeing or clicking your ad. That information can help you improve the campaign, find new keyword opportunities, and spot searches that should be excluded.

After a few weeks, compare TikTok Search Ads against your existing campaigns. The goal is not just to prove that search can spend money. The goal is to understand whether it is bringing in efficient traffic, useful conversions, or insights you can use across your broader marketing strategy.

The Bottom Line On TikTok Search Ads

TikTok Search Ads Campaigns give advertisers a more intentional way to show up inside a platform people already use for discovery and research. They combine TikTok’s visual, creator-style environment with some of the control marketers expect from paid search.

The smartest approach is to test before you scale. Start with a focused keyword group, use creative that actually answers the search, review performance regularly, and keep refining based on what people are typing into TikTok. If the results look strong, you can expand from there.

05/08: Google AI Overviews Links Get a Boost: What Marketers Need to Know

When Google rolled out AI Overviews last year, publishers watched organic clicks fall by nearly 40% while zero-click searches jumped from 54% to 72%. Searchers were getting their answers and never leaving the SERPs.

This week, Google signaled it has heard the backlash. Five updates, ranging from inline citations and hover previews to “Where to go next” article lists, re-surface real links and point users back to the open web. It’s a direct attempt to ease zero-click concerns and to showcase content that carries clear authorship and firsthand expertise.

The five new touches are:

“Where to go next” suggestions – A mini SERP of deeper articles appears after many AI answers.
Subscribed-label links – If a user pays for a publication, its articles carry a “Subscribed” badge.
First-person perspectives preview – Quotes from forums, social posts, and other firsthand sources get attribution and direct links.
Inline contextual links – Links now sit next to the exact text the AI is citing, not in a footnote pile.
Hover previews (desktop) – Mousing over a link pops up the page title and site name so searchers know where they’ll land.

Why Marketers Should Care

Clicks aren’t dead yet. Early tests cited by Google show higher engagement when users can see and preview links inline. That means “Google AI Overviews links” may start sending meaningful referral traffic instead of siphoning it.

Original expertise is the new currency. Google frames these changes as helping people “connect with authentic voices.” AKA, pages that demonstrate firsthand insight, clear bylines, schema, and community credibility have a better shot at being surfaced.

Brand perception happens before the click. With hover cards, vague or click-baity page titles can now repel users immediately. Tighten up titles and metadata so the preview inspires confidence.

Retention meets acquisition. The Subscribed label turns existing paying readers into a ranking signal. Publishers that nurture newsletter or membership programs could see a double benefit: recurring revenue and prominent placement.

No measurement yet. Google Search Console still doesn’t break out AI Overview traffic, so expect to rely on blended analytics and directional trend checks for now.

Quick actions to stay visible

Audit titles for clarity and brand. If you wouldn’t click the title in a hover card, rewrite it.
Layer in author & entity markup. Use Person schema, author.url, and social profile links so Google can display names in perspective snippets.
Surface subscription feeds. Publishers with paywalls should connect their identity feed to Google to unlock the Subscribed badge.
Publish firsthand content weekly. Case studies, original data, and expert Q&As align with Google’s push toward “authentic voices.”
Engage (selectively) in forums. Helpful participation in niche communities can earn organic mentions that now show inside AI answers.

What's Next for AI Overviews

The latest enhancements don’t end the zero-click debate, but they do prove Google is willing to share more real estate with credible sources. Treat Google AI Overviews links like any other SERP feature: Focus on original expertise, crystal-clear titles, and structured data that proves who you are. If your content is truly helpful and easy to trust, AI Overviews can become a fresh traffic stream rather than a black hole.