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

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:

• 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.”