Marketing’s Next Shift: Search, Measurement, and AI Are Getting Rewired
FAQ Strategy Isn’t Dead. FAQ Rich Results Are.
Google is removing a Search feature, not the value of answering customer questions.
What's the News:
Google is no longer supporting FAQ rich results in Search, ending a feature many brands used to earn extra SERP visibility through FAQ schema. According to Google, FAQ rich results stopped appearing in Search on May 7, 2026, with related Search Console reporting and Rich Results Test support being phased out in June, and API support ending in August.
Why It Matters:
For years, FAQ pages and FAQ schema gave marketers a relatively straightforward way to expand search visibility, support People Also Ask-style discovery, and answer high-intent user questions directly in the SERP.
That specific rich result is going away. But the behavior behind it is not.
People still search in questions. Users still need clear answers before they convert. And LLMs still rely on structured, useful content to understand what a brand knows, where it has authority, and what it should be recommended for.
This update is not the death of FAQ strategy. It is the death of one Google SERP feature.
The SEO conversation has over-rotated on the loss of FAQ rich results, but FAQs were never valuable only because they created additional markup-driven visibility. They are valuable because they reduce friction, improve the user experience, support conversion paths, and help both search engines and AI systems understand the depth of a brand’s expertise.
Marketers Should:
Keep FAQs where they improve the customer journey. Questions that help users compare, qualify, troubleshoot, or convert still belong in the content strategy.
Build around real audience questions. Prioritize what prospects are actually asking, not just what has historically generated rich-result visibility.
Use LLM query fanout research to expand content coverage. Look at what users ask first, what they ask next, and what AI systems may infer as related follow-up questions.
Evaluate performance before making changes. Do not rip out FAQ sections because of a platform update. Review traffic, engagement, assisted conversions, and AI visibility before deciding what stays or goes.
MMM Has an Action Problem
The model only matters if the organization can act on it.
What's the News:
Google-sponsored research from Harvard Business Review Analytic Services is putting a spotlight on the “actionability gap” in marketing mix modeling. While 87% of respondents say MMM is important to their organization, only 28% say they are very effective at turning MMM insights into timely action.
The research also found that renewed interest in MMM is being driven by two forces: a greater focus on ROI, cited by 68% of organizations, and increasing channel complexity, cited by 54%.
Why It Matters:
As customers move across search, social, streaming, retail media, and other touchpoints, marketers are realizing that click-based attribution alone is not enough to explain performance.
MMM gives brands a more privacy-safe, cross-channel view of media impact. But the research makes clear that adoption is not the same as action. The biggest barriers are operational: data quality issues, cited by 47% of respondents, and integrating siloed data from multiple sources, cited by 46%.
The biggest MMM challenge is not whether marketers believe in the model. They do. The challenge is whether organizations are built to act on what the model says.
This is something we see often: brands invest in measurement, but the insights arrive too late, live too far away from the media team, or rely on inputs no one fully trusts. That is how MMM becomes a reporting exercise instead of a decision-making tool.
The brands that get value from MMM are not always the ones with the most sophisticated models. They are the ones doing the operational work: cleaning the data, aligning teams, shortening planning cycles, and connecting measurement outputs to real budget decisions.
Marketers Should:
Treat MMM as a practice, not a project. The value comes from consistent use, not a one-time readout.
Fix the inputs before debating the model. Clean data, consistent naming, and reliable source integration are what make the outputs actionable.
Shorten the feedback loop. If insights arrive after budget decisions are already made, the model is not influencing strategy.
Bring analytics and media teams closer together. Measurement should not sit in a silo. The people interpreting performance and the people managing spend need to operate from the same set of truths.
Start with triangulation. Most mid-market advertisers do not need to jump straight into a full MMM build. They can make smarter decisions by combining platform data, incrementality tests, revenue trends, and business context more deliberately.
Google Tag Manager Is Getting Its Biggest Update in Years
Cleaner tagging is coming, but migration still needs a plan.
What's the News:
At Google Marketing Live, Google is expected to announce a major update to Google Tag Manager that brings GTM and Google Tag closer together. GTM containers will effectively become Google Tags, legacy Google Tags can migrate into “Destinations,” and GTM will no longer need to load separate gtag.js files for each Google destination.
Google is also introducing a visual event builder that lets marketers walk through a website and generate tags, triggers, and variables by clicking on page elements. The upgrade is expected to be opt-in, with preview, workspace testing, and rollback options.
Why It Matters:
For marketers running Google Ads, GA4, Floodlight, or other Google tracking, this could simplify a setup that has historically been redundant and brittle.
Fewer duplicate scripts can mean faster pages, cleaner configuration, and one more centralized place to manage Google measurement settings. The visual event builder also lowers the barrier for basic conversion setup, especially for teams that do not have constant developer access.
Cleaner tagging is a good thing. Blindly upgrading your tracking infrastructure is not.
The GTM and Google Tag consolidation could reduce duplication, simplify setup, and make event creation more accessible for marketers. That is meaningful. But any change to a tagging environment carries risk, especially when conversion tracking, remarketing, consent settings, and analytics reporting depend on that infrastructure.
The visual event builder is useful for simple use cases, but it should not replace a real measurement strategy. Easier setup does not replace clear event definitions, naming conventions, QA processes, and documentation.
Marketers Should:
Audit before opting in. Know exactly what is in the current GTM container before making changes.
Map legacy tags carefully. Identify what should migrate, what should stay in place, and what may need to be retired.
Test in preview before publishing. Use workspaces, preview mode, and rollback options to reduce risk.
Validate priority conversions first. Lead forms, purchases, quote requests, calls, and other business-critical events need extra scrutiny.
Use the visual event builder selectively. It can speed up simple tracking needs, but it should not become a substitute for thoughtful measurement planning.
Google Is Making Causal Measurement More Accessible
Meridian GeoX and Meridian Studio bring incrementality and MMM closer to the mainstream.
What's the News:
Google is expanding its Meridian measurement ecosystem with new tools designed to make MMM and incrementality testing more accessible. Meridian, Google’s open-source media mix modeling tool, is getting a user interface, reducing the need for Python knowledge that previously limited use to more technical teams.
Google is also launching Meridian GeoX, an open-source geo-based incrementality testing tool with a UI built into the platform. GeoX is designed to run geo-matched market tests, with the resulting causal signals feeding directly into Meridian’s MMM. Google is also introducing Meridian Studio, an enterprise platform on Google Cloud that allows teams to build, customize, and manage high-volume MMM models with less technical lift.
Meridian GeoX is expected to begin testing later this year.
Relevant article: https://blog.google/products/ads-commerce/google-marketing-live-2026-turn-your-data-into-decisions/
Why It Matters:
This is a meaningful shift in where measurement is headed. By lowering the technical barriers to MMM and geo-based experimentation, Google is making probabilistic, incrementality-based measurement more accessible to marketing teams that may not have dedicated data science resources.
Google is productizing the measurement approach smart marketers have been moving toward for years: incrementality, MMM, and multiple signals working together.
Meridian GeoX is especially worth watching because geo-based incrementality testing feeding directly into MMM is the right architecture. It creates a stronger path to understanding not just where conversions were reported, but what media actually caused growth.
Marketers Should:
Use this as a case for better measurement internally. Google’s own investment in MMM and incrementality reinforces that last-click attribution is not enough.
Build testing discipline now. The tools may get easier, but good incrementality testing still requires clean markets, clear hypotheses, controlled execution, and stakeholder buy-in.
Connect measurement to budget decisions. A test that does not change investment strategy is just a research exercise.
Prepare the data foundation. Spend, revenue, geo, campaign, and channel data need to be clean enough to support causal measurement.
ChatGPT Ads Are Showing Early Promise
High-intent conversations could become the next performance channel, but scale is still the question.
What's the News:
Early ChatGPT ad tests are reportedly showing stronger click-through rates than traditional display and podcast benchmarks, particularly around high-intent prompts. Similarweb’s early analysis found strong engagement from ChatGPT ad placements, while inventory and scale remain limited.
The bigger takeaway is not just that users are clicking. It is that conversational AI may create a new kind of ad environment where users are actively asking questions, researching options, and looking for recommendations in real time.
Why It Matters:
Conversational AI is different from passive media. Users are not scrolling past an ad. They are often trying to solve a problem, compare products, evaluate vendors, or learn what to do next.
That makes AI-generated answers a potentially high-intent environment for advertisers.
ChatGPT users are actively asking questions, researching options, and looking for recommendations. That creates a more intent-rich context than passive display environments.
Marketers should stay disciplined, though. Inventory is still limited, scale is unproven, and CTR does not tell the full story. The brands that benefit most will be the ones that test early while staying focused on conversion quality, message fit, and incrementality.
Marketers Should:
Test early, but do not overcommit too quickly. Strong engagement is a signal, not a guarantee of scalable performance.
Strengthen brand positioning for recommendation-based environments. AI-driven discovery rewards brands that are clear, differentiated, and easy to understand.
Adapt messaging for conversational UX. Traditional display or search copy may not translate cleanly into AI-generated answers.
Measure beyond the click. Lead quality, assisted conversions, pipeline impact, and incrementality will matter more than CTR alone.
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