But here’s what I’ve learned after years of evaluating these announcements—not every Google update deserves equal attention. Some are genuine game-changers, others are incremental improvements, and a few are just repackaged existing features with shinier interfaces.

Let me break down what’s actually worth your time and budget this holiday season.

The Lead Gen Updates That Could Actually Move the Needle

Enhanced Lead Form Ads

Lead Form ads have always felt like missed opportunities. You could collect information, sure, but the forms were static and treated every lead the same way. That’s changing.

The new qualifying questions and conditional responses mean you can now:

Pre-qualify leads in real-time instead of sorting through unqualified submissions later
Adapt the form experience based on how people answer your questions
Identify high-value prospects before they even hit your CRM

If you’re not already testing Lead Form ads, this is your reason to start. If you are using them, these features should be live-tested immediately. The potential impact on lead quality—not just quantity—could be significant.

Google Analytics Gets Serious About Lead Intelligence

The three new Analytics features address real problems most lead-gen marketers face:

Lead Acquisition Reports let you trace back to a lead’s first touchpoint with your brand. This isn’t just attribution theater—it’s actionable intel about which early-stage content and channels actually influence your best leads.

Lead Disqualification Reports surface why prospects didn’t convert. Most advertisers obsess over what works but ignore what doesn’t. Understanding drop-off patterns can be more valuable than celebrating wins.

Eight new audience templates for lead nurturing. Here’s the reality: most businesses are terrible at nurturing leads through the middle of the funnel. These templates give you segmentation starting points based on where leads are in their journey.

The combination of these three features creates a complete lead intelligence system. That’s rare from Google.

The Retail Updates: Practical vs. Promotional

Campaign Total Budgets

Managing budgets during promotional periods has always been painful. You want to maximize opportunity during peak times, but you also don’t want to blow your monthly budget in the first week of Black Friday.

Campaign Total Budgets solves this by letting you set spending limits across defined timeframes (3-90 days) while still allowing Google’s AI to optimize day-by-day within those constraints.

This is immediately actionable for any retailer planning holiday campaigns. Set your promotional period budgets now and let the system balance spending while maintaining optimization.

Demand Gen Gets Local: The Mid-Funnel Meets In-Store

If you’re not running Demand Gen campaigns, you should be. They’re exceptionally effective at reaching users in the consideration phase—that crucial middle-funnel space between awareness and purchase intent.

The local offers integration means these campaigns can now surface nearby store inventory and drive in-store visits alongside online conversions. For omnichannel retailers, this bridges a gap that’s existed for years.

The key requirement: your product feeds and location data need to be current and comprehensive. If they’re not, fix that before you try to leverage these features.

Merchant Center Insights: The Update That Deserves Immediate Attention

This is the most immediately valuable update for retailers. Google is using AI to surface actionable insights about your product catalog, including:

Popular product identification based on search and shopping trends
Competitive pricing analysis against similar products
Audience trend data showing who’s engaging with your products

The difference between data and insights is analysis. Google is finally providing the analysis layer that most retailers lack the resources to build themselves.

Every retailer should be reviewing these insights weekly as we move into holiday season. This isn’t just reporting—it’s competitive intelligence delivered automatically.

The GenAI Tools: Useful, But Don’t Lead With Them

Product Studio and Asset Studio represent Google’s commitment to generative AI for advertisers. You can now generate product backgrounds, create video content, and enhance imagery directly within Google’s ecosystem.

These tools are legitimately useful, but they’re creative enablers, not performance drivers. Use them to scale content creation and test creative variations, but don’t expect them to fundamentally change your results.

The real value comes from testing multiple creative approaches quickly and affordably, not from the AI generation itself.

What This Really Means for Your Holiday Strategy

Google’s pattern is consistent: they announce features that sound transformative but require strategic implementation to deliver actual value. Here’s how to approach these updates:

Lead Gen Advertisers: Prioritize the Lead Form enhancements and Analytics features. These address real conversion quality issues that impact revenue, not just volume.

Retail Advertisers: Focus on Merchant Center insights and Campaign Total Budgets first. Both solve immediate operational challenges during peak selling periods.

Everyone: The GenAI tools are nice-to-have, not must-have. Implement them after you’ve optimized the fundamentals.

The Real Intelligence Approach: Prove Before You Scale

At Brainlabs, we don’t chase every new feature Google releases. Instead, we test systematically and scale what actually moves business metrics.

That means treating these updates like any other optimization opportunity: hypothesis, test, measure, scale. The features that genuinely improve performance will prove themselves through real results, not just Google’s marketing materials.

The holiday season isn’t the time for experimental tactics—it’s when proven strategies need to perform flawlessly. Use these updates to enhance your existing approach, not rebuild it from scratch.

Your budget and timeline are limited. Choose the features that address your biggest current challenges, test them properly, and scale what works. Everything else can wait until January.

The post Google Just Dropped Ad Updates During Think Week—Here’s What Actually Matters for Your Performance appeared first on Brainlabs.

So What’s the Difference?

MMM (Marketing Mix Modeling) is your big-picture view. It shows how different marketing channels (and non-marketing factors) contribute to outcomes over time. Think long-term trends, full-funnel impact, and strategic planning.

Incrementality testing, on the other hand, is all about proving causality in the short term. You’re running experiments — usually geo-based or audience holdouts — to isolate whether a particular campaign, tactic, or channel actually drove lift.

Here’s how I like to explain the difference:

MMM:
– Shows how channels perform over time
– Timeframe: Months and years
– Data: Aggregated (e.g. weekly sales + spend)
– Best for: Budgeting, planning, channel mix
– Speed: Slower (but broader)

Incrementality Testing:
– Shows what actually caused lift in the moment
– Timeframe: Days or weeks
– Data: User or geo-level
– Best for: Testing, optimizing, creative/audience strategy
– Speed: Faster (but more focused)

Why You Shouldn’t Pick One Over the Other

Here’s where it gets interesting: MMM and incrementality make each other stronger.

– If you’re only running MMM without calibrating it with real-world test results, you’re building a model based on assumptions. Good ones, maybe — but still assumptions.
– And if you’re only running tests without zooming out to the bigger picture, you might be optimizing in a vacuum — missing how channels work together or how your market’s evolving over time.

MMM gives you historical context. Incrementality gives you causal proof. You need both.

When It Makes Sense to Use Them Separately

There are definitely times when one method makes more sense than the other:

Use MMM on its own when:

• You’re planning budgets across all channels for the next 6–12 months.

• You need to justify brand spend that doesn’t show immediate lift.

• You’re working with limited tracking — MMM is great in cookie-constrained environments.

Use incrementality testing on its own when:

• You want to test a new tactic (like YouTube Shorts or a new audience segment).

• You’re looking to validate a media partner’s claims.

• You need fast feedback to make decisions on in-flight campaigns.

From Analysis to Action: Where Real Intelligence Begins

At Brainlabs, we don’t believe in guessing what works—we prove it before you spend a single dollar. That’s the power of our Real Intelligence approach: combining MMM with incrementality testing before strategy creation, not after.

Most agencies build campaigns first, then scramble to measure what worked. We flip that model completely. Our process starts with analysis—not assumptions—so strategy is built on truth, not retroactive validation.

MMM uncovers strategic patterns. Incrementality testing verifies what truly drives results. Your first-party data tells us what happened, but together, these tools tell us why. When used upfront, they shape strategy, not just measure it. That’s the difference.

This means when we learn that video completion rate is more predictive of purchases than CTR, or that reallocating budget from direct mail to paid search improves ROI—these insights aren’t post-campaign reflections. They’re foundational inputs before a single creative is developed or media plan is built.The Real Intelligence approach fuses machine learning, statistical modeling, and human decision-making to drive smarter action, faster. It’s how we transform your data into competitive advantage from day one.

The post MMM vs. Incrementality Testing? appeared first on Brainlabs.

AI search isn’t just adding new features to existing platforms, it’s altering how search works, how customers interact with information, and how businesses need to think about visibility and attribution. Understanding these shifts is critical for any brand that wants to maintain relevance in the evolving search landscape.

The End of Winner-Takes-All: From Single Position to Ecosystem Dominance

The first major transformation challenges everything SEOs thought they knew about ranking strategy. It’s no longer winner takes all. You used to be able to get by with one page—optimized really well for one keywords—and being able to rank in the first position on Google. Google was largely the only one in the game for two decades.

This fundamental shift means that the traditional focus on achieving the coveted #1 ranking on Google is no longer sufficient. Now at this point, it’s really about owning that entire ecosystem and winning on all searchable platforms because AI search is synthesizing that information from any of those platforms, like Reddit and TikTok and YouTube and others.

AI search engines don’t just pull from one source, they aggregate information from across the web to provide comprehensive answers. This means brands need to establish authority and presence across multiple platforms—from Reddit and YouTube, to TikTok and Shopping Feeds—simultaneously. A single optimized webpage, no matter how well-crafted, can no longer guarantee visibility in AI search results.

The strategic implication is profound: instead of concentrating efforts on ranking for specific keywords on Google, successful AI SEO now requires a distributed approach across all organic platforms where your audience searches and AI platforms source information.

The Personalization Revolution: Direct Answers Replace Content Marathons

The second transformation addresses a long-standing frustration with traditional search results. AI Search provides a more personalized, direct answer to your customers.

The old model was inefficient and, frankly, annoying. In the olden days, if anyone’s a baker, you would go onto a recipe site and there would be this endless story that didn’t really make sense here. Just give me the darn recipe!

AI search has eliminated this friction entirely. Now we’re seeing a much more personalized experience where AI search is actually giving that person the exact answer. If you search for a recipe—with your specific ingredients available—it tells you how many teaspoons, tablespoons, and cups of whatever you have. This eliminates frustration when baking, but also most likely a trip to the grocery store (assuming you have alternatives). 

This shift toward direct, personalized answers means content strategy must evolve. Instead of creating lengthy articles designed to capture organic traffic through keyword stuffing and extended narratives, brands need to focus on providing precise, actionable information that AI platforms can easily extract and present to users. 

The winning approach is to really focus on giving direct answers and building that personalized content for all of those different audiences. This means understanding the specific questions your audience asks and providing clear, immediate value rather than forcing them to wade through unnecessary content. Audience research tools like Sparktoro can make this easier. 

The Attribution Challenge: Business Impact Over Vanity Metrics

The third transformation tackles the measurement and attribution challenges that have always plagued SEO, but are now becoming even more complex. Attribution is getting even more difficult than it already was for SEO.

Traditional SEO metrics are becoming less reliable indicators of success. The reality is a lot of SEOs used to only focus on clicks. And the best SEOs were the ones who always focused on business impact and revenue. And in the world of AI search, that doesn’t change. You’re still focused on business impact and revenue.

The challenge is that AI search often provides answers directly in the search interface, potentially reducing click-through rates to websites. However, this doesn’t mean the SEO effort is ineffective—it means the measurement approach needs to evolve.

At the end of the day, visibility within AI search can be a leading indicator of what that ultimately means. The key is connecting AI search visibility to actual business outcomes rather than relying solely on traditional metrics like clicks and page views.

The most sophisticated approach involves tying those dots together in a clear way so we can ultimately see what the impact of AI visibility is against your overall business goals. And that is what your CEO and CFO are ultimately going to care about.

The Strategic Response to Transformation

These three transformations require a fundamental rethinking of SEO strategy. Success in the AI search era demands:

Ecosystem thinking: Building authority across all platforms where your audience searches, not just optimizing for Google rankings.

Direct value delivery: Creating content that provides immediate, actionable answers across your entire audience set, rather than extended narratives designed primarily for search engine crawling.

Business-focused measurement: Developing correlation models that connect AI search visibility to revenue and business outcomes rather than relying on traditional click-based metrics.

The brands that adapt to these changes will find themselves well-positioned for the future of search. Those that continue optimizing for yesterday’s search behavior will see their visibility and influence diminish as AI search becomes the dominant way customers find and evaluate information.

The post The Three Ways AI Search is Transforming SEO Forever appeared first on Brainlabs.

The Strategic Dilemma

Picture this scenario: You’re planning a major campaign launch. Your media team presents two paths forward:

Path A (Segment & Target): Select a few carefully curated third-party audiences and target them consistently throughout your campaign.

Path B (Experiment): Test many platform audiences and optimize to the best performing ones during the campaign.

Platform audiences are cheaper and likely more accurate, but they don’t always fit brand needs, like targeting category buyers with Facebook interest audiences. Third-party audiences are more tailored to brand requirements but typically cost more due to CPM premiums.

Most marketers would instinctively choose the precision of Path A. But what if this intuition is wrong?

The Simulation That Settled the Debate

Rather than rely on expensive trial-and-error campaigns, I used Monte Carlo simulation to run 100,000 virtual campaigns comparing these approaches. Here’s exactly how I set it up:

The Experimental Approach

Audiences: Platform-specific (Meta, Google, Amazon)
Cost: $5 CPM
Reach: 0.5M – 5M audience sizes
Performance: 1-3% conversion rates
Strategy: Start with 20 audiences for 2 weeks, optimize to top 10 for next 4 weeks, then top 5 for final 6 weeks

The Segment & Target Approach

Audiences: Third-party data audiences
Cost: $7 CPM ($2 premium)
Reach: 0.65M – 6.5M audience sizes
Performance: 1.2-3.6% conversion rates (20% performance premium)
Strategy: 5 audiences for the full 12 weeks

The Shocking Results

After 100,000 campaign simulations, the first result was almost anticlimactic:

Average Performance: Nearly Identical

Experimental Approach: $1.5M spend, 302M impressions, 0.023638% response rate

Segment & Target: $1.5M spend, 214M impressions, 0.023970% response rate

The difference was so small it’s essentially meaningless.

But when I examined the distribution of results, a stunning pattern emerged that changes everything we think we know about audience strategy.

The Hidden Truth About Risk

While average performance was nearly identical, the risk profiles were dramatically different:

The Segment & Target approach created a dangerous feast-or-famine scenario. The distribution showed it could deliver exceptional results on the high end, but also catastrophic failures on the low end. You’re making a high-stakes bet on just 5 audiences carrying your entire campaign.

The Experimental approach delivered something more valuable: consistent, predictable performance. By optimizing from a broader pool of 20 starting audiences, it virtually eliminated the risk of terrible results while maintaining strong averages.

The Counterintuitive Discovery

Here’s what challenges everything we believe about audience targeting: The approach that seems more controlled and scientific (segment & target) is actually the higher-risk strategy.

The experimental approach reduces risk by selecting from a broad pool of audiences throughout the campaign. You’re unlikely to get exceptional performance, but you’re also unlikely to fail catastrophically.

The segment & target approach offers higher upside potential, but with significantly higher downside risk. All 5 audiences could perform exceptionally well, or they could all underperform drastically.

What This Means for Your Strategy

The simulation reveals a fundamental truth: Both approaches perform similarly on average, but an experimental approach reduces risk while a segment & target approach offers higher upside and downside potential.

This insight forces a critical question: How much do you want to gamble with your audience strategy?

For the experimental approach, you need to be confident that your optimization process can effectively identify and scale the best-performing audiences from your initial test pool.

For segment & target, you need to be confident that your third-party data will deliver at least 20% better performance than platform audiences to justify the cost premium, and you need to be prepared for the possibility of significant campaign underperformance.

The Bottom Line

After 100,000 simulated campaigns, the data tells a clear story: The experimental approach isn’t just a viable alternative, it’s often the superior choice for risk management.

The question isn’t which approach delivers better performance (they’re essentially equal on average). The question is whether you’re optimizing for predictable results or willing to accept higher variance for the chance of exceptional outcomes.

The approach that feels safer might actually be the riskier bet.

Real Intelligence in Action

This simulation study exemplifies what we call Real Intelligence, the approach that sits at the core of our media operating system. We use it to develop a real understanding of real customers, influence real action, and deliver measurable, real business outcomes.

We start from a place of first principles, which means separating signals from noise to draw conclusions from real data, not assumptions. Rather than accepting conventional wisdom about audience targeting, we continuously test hypotheses to inform bigger strategic decisions, in this case, our entire audience strategy.

By running 100,000 simulations, we discovered that our industry’s assumptions about risk and precision were backwards. This is Real Intelligence at work: using data to challenge what we think we know, then building strategies based on what we actually know to be proven.

The post The Great Audience Targeting Paradox: What 100,000 Simulations Revealed About Risk vs. Performance appeared first on Brainlabs.

Customers are now demonstrating search intent in more diverse places than they used too, as discovery-driven commerce grows, creating new competitive advantages for brands that can seamlessly blend entertainment, validation, and transaction in social ecosystems.

The trend is only heading in one direction, with the 2025 GWI Social Media Report revealing that nearly half of 16-34 year olds cited searching for products as one of the main reasons they use social media. They’re not just changing platforms they use for search, they’re rejecting the premise that discovery and purchase should be separate experiences. 

The traditional funnel stages haven’t vanished; they’ve collapsed into micro-moments of social discovery. Brands must act fast to adapt to this fundamental shift.

So, what is Social Search?

Prior to this evolution, if a consumer was looking for answers such as ‘best restaurant in London’ or ‘healthiest dog food,’ there was only one place they turned, Google (sorry Bing). But users are now turning to their social media platforms to answer these questions, using the platform search functionalities to find answers.

But why the shift?

Anyone that has worked in traditional Paid Search knows that the greatest signal a user can give you is their intent. The unique power of this channel is an immediate, in moment response to direct requests from consumers. This is not the place for educating users about a brand’s product or generating demand, but social platforms can unlock this opportunity. Here’s why:  

The Authenticity Economy – when on TikTok or Reddit, consumers know their queries are being answered by a real person on the other side of the screen, not a business with a big SEM budget or a carefully crafted article full of keywords. There’s a level of trust that comes from this. These public forums invite criticism and discussion so the recommendations and answers are perceived as genuine – whether they are or not…

A Visual Medium – Most platforms are natively built around images and videos, so when looking for answers on ‘best gochujang recipes’ or ‘where to stay on the Amalfi coast,’ why wouldn’t you prefer to see what the final recipe looks like or what the view is like on a beach in Italy? 

Speed and Seamlessness – a couple of years ago a study was conducted that found Brits scrolled more than 3x of the length of the Eiffel Tower per day. Platforms like TikTok, Reddit and Instagram are easy to navigate, and serve multiple functions such as entertainment, inspiration, networking, commerce or shopping, and search capabilities. Because of this, users are less likely than ever to leave the app. 

Forward-thinking brands are already reorganizing around integrated teams that think in terms of social ecosystems rather than channel silos. These teams don’t just optimise campaigns; they cultivate communities that blend entertainment, education, and commerce. Tech platforms are supporting this by adapting their Search functionality to be simple and ingrained in the user experience.

How are big tech platforms adapting to this change ?

TikTok is on the front foot here, developing their Search Ads campaigns, with keyword search term targeting expected to roll out widely in H2 2025. Reddit and Pinterest likewise are gaining momentum – as their platforms have always had search principles integrated into their audience and placement offering. 

Interestingly the dominant forces in the industry are slower to move in actionable ways, Meta has not identified this as a priority within their ad buying tools. Instead they are focusing on automation of existing functionalities and monetising Threads/Whatsapp over investment in keyword bidding functionalities. Similarly, it will be important to keep an eye on how Google reacts over the coming months… will the traditional engine be adapted to meet changing consumer demands? We will certainly be watching this space. 

Social users behave so differently on each platform that they may also evolve to capture category-specific search behaviors. Pinterest owns lifestyle discovery. TikTok dominates product performance. Reddit controls opinion validation. In addition to adding search features—they’re becoming the definitive authority for different types of purchase decisions. The winners won’t just be platforms with the best search algorithms, but those that create the most seamless path from discovery to transaction within their ecosystem. Amazon understood this early with one-click purchasing. Social platforms are now building the same frictionless commerce experience around content consumption

So, what can advertisers do to capitalise on this?

Ecosystem Thinking Over Channel Optimization – This has never been more important, with the principles of Search starting to be integrated into ad buying functionality across Social platforms. Ensuring a streamlined and efficient workflow between Search, Social and Content teams is going to be essential to maintain flexibility as the platforms develop and landscape changes.

Authority Through Community 

1. Your competitive edge increasingly comes from the communities that advocate for your brand across platforms, so a creator strategy is essential. Authentic content is key, and brands will need to be strategic with who they partner with, as well as deliberate in the type of content they develop. Ensuring they are present to answer the questions their customers are asking, without jeopardising trust.

2. Branded content also needs to maintain the creative “red thread” that exists across other marketing channels – driving authenticity not only in messaging but in brand identity. 

Test, test, test! – With functionality across the different Social Ads Platforms varying and ever-changing, having a structured test & learn strategy (and budget) is a must to make sense of the new landscape. Working with media partners on understanding platform nuances, planning tool availability, and shifting best practices will help brands start to shape their strategy for 2026 and beyond.

It’s an exciting time for the industry and this ongoing behaviour shift represents a great opportunity for marketing teams focused on future-proofing their strategies. As the roles of channels are beginning to blur together, it is essential to have these conversations now, to build for an integrated future. 

The post Social Search Is Changing Consumer Behavior appeared first on Brainlabs.

The way that people find information online is changing.

Strides forward in LLM capabilities over the last few years have transformed the types of questions that people can ask online. Rather than having to learn to “speak search engine” and enter truncated queries into a search bar, AI can provide effective responses to far more multi-faceted queries – whether those queries are grounded in natural language, or visual & multi-modal search.

This shows up in a variety of data points. Firstly, on Google search, we’re seeing consistent growth in more open-ended queries – for instance, queries containing “what”, “how” & “best”. These are subjective questions that require complex comparison skills, which search engines of old would have struggled to address. Compare this to the more closed, “command”-style queries, such as “buy” or “cheap”, which are trending either flat or in marginal decline.

We also know that the queries people are asking are growing in length. Whilst Google are seeing growth across query strings across all lengths, nowhere is that growth greater than in the longer-tail. This is happening even more in AI environments, with users of Google’s AI Mode asking queries that are on average 2-3x longer than those on regular Google search.

All of this change has drummed up a lot of interest in the search industry, and is leading clients to ask one question time and time again: How can we ensure our brand is present within AI responses to queries that are relevant to our brand?

Until now, the answer has mainly been confined to the realm of SEO practitioners, because paid advertising opportunities have been incredibly limited. When we look across the AI answer engines from a paid advertising perspective, it’s only Microsoft’s Copilot that today has an operational advertising unit. 

Perplexity has launched its “Follow Up Questions” format, but so far only in a very limited testing phase to some US brands.

Sam Altman, CEO of OpenAI, has historically taken a very anti-advertising stance to ChatGPT. Whilst his view may be softening, ads in ChatGPT are certainly still not on the immediate horizon.

And finally, in Google’s sphere, whilst we’ve known for some time now that Ads in AI Overviews have been in testing, the launch had always looked like being a fair way away. Google had been very tight-lipped around any potential timelines.
All that changed last week. At the beginning of August, Google revealed that Ads in AI Overviews could be landing in English-speaking markets in as soon as the next 5 months – ie, before the end of 2025.

It’s hard to overstate just how big a change this will represent for Google and for clients.

Why is this such a big deal?

The short answer is that this will open up the floodgates on a huge amount of previously un-monetised Google search inventory.

To understand why this is the case, and why this won’t just cannibalise existing advertising opportunities on Google, it’s helpful to build a segmentation of all of the different types of text-based queries that Google receives today.

A 2024 report by SparkToro revealed that only 15% of such queries demonstrate a clear commercial intent. A further 32% of queries are “navigational”; this encompasses brand searches, as well as more generally any queries where the user has a known destination in mind that they are looking to reach.

And then, finally, making up over half of all traffic on Google, are the “informational” queries. In these queries, users are asking a question that doesn’t – at least, on the face of it – demonstrate an obvious intent to purchase a product or service. Questions like the ones users are asking even more often within AI experiences, as discussed above.

With our segmentation in place, it’s then helpful to think about what the SERP looks like for each of the three query types. Whilst there are some exceptions, by and large it is the case that ads are bought against commercial & navigational queries, whereas AI Overviews are the most suitable response for Informational queries.

The crucial callout here is that there is very little overlap between these two result formats. When ads do arrive within AI Overviews, they will appear on queries which have largely been un-monetised – and so will genuinely present a net new, incremental opportunity for marketers to access their customers.

Of course, it would be a mistake & an exaggeration to propose that every single one of those informational queries is going to be monetised. However, in even our most conservative scenarios, we’d expect this to unlock a 20% expansion in ad inventory on Google search.

Why can Google put ads on these queries all of a sudden?

There is a crucial difference in how ads will work within AI Overviews compared to how they work today within “regular” search.

In a regular search context, by far and away the most important signal to determine whether an auction will be initiated is the search query itself. If one or more advertisers are deemed to have an ad that could be relevant for the query, then an auction will be triggered.

However, Ads in AIOs can also be triggered based on the content that is included within the gen AI response itself. Think of the AI Overview as a bridge: first of all, it will answer the query that the user asked of it, and then it will suggest “oh and by the way, if you did happen to be thinking of buying a product or service to help you out with your question, then here are some suppliers you could consider.”

It’s a simple enough idea, but it’s hard to overstate just how significant a conceptual leap this is.

What do marketers need to do about it?

Firstly, it’s worth being clear that there will be no “AI Overviews” campaign type appearing in your ad account any time soon. In keeping with their general direction of travel over the past few years, Google will be consolidating all advertising inventory in AIOs into existing campaign types.

However, that’s not to say that we can afford to stand still and have the +20% uplift land in our laps. These auctions are materially different from the ones we’ve played in before, and trying to access this new inventory with your existing search tactics will see you quickly run into some hurdles:

The good news though, is that everyone else is in the same boat. This opens up the potential for a huge first-mover advantage – but what do we need to do to capture it?

From the three challenges above, it’s not difficult to see the direction we need to move in for our Google Ads campaigns:

Targeting: Adopt intent-based targeting solutions that assess ad relevance based not just on the query, but on Google’s understanding of your business

Messaging: Lean into creative assets generated in real-time that allow you to show up in the most relevant possible way

Bidding: Provide bidding strategies with more flexibility to target never-before-seen queries

Specifically, the tactics that Brainlabs is recommending its clients to test & implement between now and the end of the year are:

Somewhat understandably, many advertisers have historically been wary about some of these features: in particular, when it comes to sharing control over assets & messaging with Google. And up until now, there hasn’t been a pressing performance need to overcome this reticence. But with Ads in AIOs just down the line, we’ll very soon reach a point where this hesitance will put the brakes on advertisers achieving the visibility and performance that they desire from AI environments.

As a practitioner, what’s most exciting about these tactics is that, with AI Max in particular, Google have learned from the feedback they received throughout the launch of PMax a few years prior. Almost right out of the gate, Google launched AI Max with an array of reporting and experimentation features that will provide clients with greater levels of transparency & influence over how the campaign is behaving and performing. A full breakdown of these features is for another post – but four particularly notable callouts would be:

1. Cookie-based A/B testing functionality for AI Max campaigns vs. regular Search campaigns;

2. An “AI Max expanded searches” row within Search Term Reports, to indicate what performance has been driven through AI Max’s targeting capabilities in addition to your existing keyword targeting;

3. Asset reports that allow for a comparison of performance between advertiser-uploaded and automatically-created assets;

4. Asset control functionalities, such as Asset Exclusions & Asset Removals. You can mentally liken these to negative keywords, but for assets rather than search terms.

Call to arms

Ads in AI Overviews will almost certainly represent the largest overnight expansion of search inventory that we have seen within the last decade.

The growth of mobile devices & Broad Match keywords are the two other events that had a comparable impact – but both instances required gradual adoption from users and advertisers, respectively. Contrast that to AI Overviews, which already reach 1.5 billion people globally. When Ads in AIOs & AI Mode do arrive, it’s hard to see Google opting for anything other than a wide-scale “turn on the tap” approach. The whitespace ahead for brands will be huge.

However, by committing to a sprint test & learn roadmap between now and the end of the year, marketers have a chance to tip the scales even further in their favour and to build distance between themselves and their competitors. The five-month countdown has begun.

The post Winning the New AI Paid Search Game: How to Capture 20% More Ad Inventory Before Your Competitors Do appeared first on Brainlabs.

The prevailing narrative suggests a seismic shift in consumer search behavior, where the dominance of Google and Amazon is being eroded by a new ecosystem of Social and AI-driven platforms. To move beyond speculation, we built a proprietary dataset, analyzing the detailed purchase journeys of 3,000 UK and US consumers. This data allows us to map the real-world behaviors that define the modern search research journey. Our analysis reveals that while the landscape is diversifying, the story is not one of simple replacement. Instead, the market is fragmenting into three distinct behavioral personas, each with a unique research DNA:

The Traditionalist: A significantly older demographic that sticks exclusively to the foundational giants of Google and Amazon, representing the most direct path to purchase.

The Augmenter: Our data reveals this is the largest segment, representing the mainstream consumer (25-44), who begins with Google or Amazon but then adds multiple other platforms like YouTube and AI chatbots.

The Dissenter: Our analysis identified a younger demographic that bypasses the duopoly altogether, discovering products organically on social and video platforms like TikTok and Instagram.

Understanding these three distinct journeys is critical for marketers, as each requires a unique channel strategy to ensure a brand’s message is coherent across the consumer’s bespoke path to purchase.

The Evolving World of Search: Beyond a Two-Platform Model

The prevailing wisdom is that we’re at a major inflection point for the world’s search habits. Search—the act of looking for information using text, images, or voice—has transformed . What was once a market dominated by the default choices of Google and Amazon has being fractured into a diverse ecosystem of platforms.

We’re told we’re living in a new era of ‘Social Search’ and ‘AI Search,’ where users are supplementing the traditional duopoly for a world of searching on platforms like TikTok, Reddit, and ChatGPT. As marketers, we’ve dutifully coined the new terms and accepted the premise that the old habits are dead, cynically perhaps because the new thing is always what sells to clients or internal stakeholders.

This diversification of platforms has been accompanied by a fundamental change in user behaviour. We’ve moved from a model where users relied on one or two primary platforms into a far messier world of touchpoints, with people using multiple channels in their search journeys. 

And so the story goes: Google’s role is evolving, its habitual dominance being complemented by the rise of social and AI search, creating a more fragmented world for users and advertisers. The narrative is set. All that needs to be considered now is the critical question: is it actually true?

Answering this question is remarkably hard. It’s hard for two main reasons:

1. Traditional measures of share of search focus on traditional search engines, they don’t include how often a user is using social or AI search. For example, see stat counter’s view of share of search.
2. More cynically, none of these companies want to share data on their search volumes. Google and Amazon don’t want to suggest that their volumes are shrinking and the social and ai search platforms are benefiting from the prevailing narrative that they’re growing, why disavow the market of the belief that they’re claiming some of Google’s $175 billion¹ in search revenues.

However, there is some evidence that Google might not be shrinking, implying that talk of their demise may at least be a little premature. Google searches are growing. Spend on Google isn’t dropping. Nor is their share price. And the “winners” seem a little quiet on the topic…

Google searches are growing.

Google searches are growing. Spend on Google isn’t dropping.

Google searches are growing. Spend on Google isn’t dropping. Nor is their share price.

Google searches are growing. Spend on Google isn’t dropping. Nor is their share price. And the “winners” seem a little quiet on the topic…

We set out to get a real representation of the search landscape today

To cut through the noise of unsubstantiated claims, we commissioned an in-depth survey of 3,000 representative consumers across the UK and the US. We focused on a key battleground in the search landscape: researching a recent product purchase.

Our survey was designed to capture the full complexity of a modern research journey by asking users about three key areas:

1. The Purchase Context: We first grounded each response in a real transaction by having users select from a comprehensive list of categories, from ‘Automotive’ to ‘Fashion & Accessories’.

2. The Platform Toolkit: To understand the full ecosystem, we then asked: ‘To do your research, which platforms did you use to search for information?’ This multi-select question included everything from Search Engines and Amazon to YouTube, TikTok, and AI Chatbots, revealing the complete ‘platform toolkit’ for each user.

3. Additional details – we asked users to identify where their journey started (‘Which platform did you use to start your search?’) and where it ended (‘On which platform did you make the final purchase?’). We also asked them to estimate how long it took to go from research to purchase.

Modern share of search platforms

The data below illustrates the modern distribution of search platforms. The chart shows the “share of search platforms”— the proportion of all the platform touchpoints identified which were each of the platforms. E.g. if we counted up all platform touchpoints identified by users and had 10 in total, Google being 2 of these would give Google a share of 20%.

It’s important to note that our survey focused on which platforms were used, not the frequency of use. Our goal is to understand how the habit of turning to incumbents like Google and Amazon is evolving in this new, diversified landscape.

Insight #1 – The incumbents are bruised, not broken

Our survey results confirm that consumers are using a wide array of platforms for search. This in itself is noteworthy – AI platform search share is 6.3% across the dataset – impressive growth by any measure. We also see social search with high adoption as well. However, our data shows that the ingrained habits of using Google and Amazon remain formidable, as they command a significant share of search platform touchpoints (27%).

Another interesting finding is that the adoption of AI platform search share is more than four times higher in the US than in the UK (7.9% vs. 1.8%), with Social Search also showing higher adoption rates. This aligns with the common observation that the UK often lags behind the US in the adoption of new technology.

Insight #2 – Brand websites show a stronger foothold in the UK.

In our cross-country analysis, a key difference emerges in the role of brand-owned websites. In the UK, they represent a significant 17% of search touchpoints, more than double the 8% share in the US. This may be because lower adoption of social and AI search platforms like TikTok and ChatGPT in the UK means the traditional journey—which directs users out to third-party sites—remains more prevalent, directly benefiting brands. 

Insight #3: The story is one of curation, not chaos

Contrary to the narrative of a messy, endless journey, the average research path uses surprisingly few platforms,  involving just 1.9 platforms in the UK and 2.5 in the US. 

The real story is one of curation, not chaos.

Users are developing new, category-specific habits. For example, our data highlights the heavy use of AI when users consider financial services. This implies that instead of simply adding more steps, users are building efficient, personal “platform toolkits”—instinctively choosing the best platform for the specific task and their own search preferences.

Do note importantly that the combinations of platforms used in journeys was very variable – the implication being that you can’t really predict the journey, you have to plan for them all.

Patterns in the chaos – the traditionalist, the augmenter and the dissenter

The data provided by our survey paints a clear picture of a changing search landscape. We can assume 5-10 years ago it was just Google and Amazon, but now we have a much more diverse platform set in use. Understanding how this transition is happening, by examining not the aggregate but by looking at individual customer journeys, is essential for building robust communication and channel plans – ensuring we meet our customers where they are with a message which is coherent alongside those they’ve encountered on other search platforms.

Analyzing the nearly 3,000 individual journeys in our dataset, we have identified, we have identified three clear groups of users by looking at each research journey in our survey. 

Deeper insight #1: Google and Amazon are still a part of 81% of journeys in total
Actionable takeaway: Audit you current media spends in search, if you’re spending >80% on Google and Amazon, think about trialing a recalibration to other platforms.

So whilst Google and Amazon combined make up only 45% of touchpoints across the dataset as we saw in Modern search’s platform share, they occur in 81% of user journeys, meaning that they are probably your best bet to manage to reach as many of your customers as possible on a very reduced platform set.

Deeper insight #2: Men are nearly twice as likely to use AI to augment their searches as women
Actionable takeaway: Pilot an “Answer Engine Optimization” (AEO) strategy, especially if your brand targets men and involves a complex, information-heavy purchase (like finance, auto, or tech)

Deeper insight #3: Social search adoption is high across genders, with 69% of men and 66% of women using a social platform.
Actionable takeaway: Treat your social channels as mid-funnel research platforms, not just top-of-funnel brand channels. Audit your content calendar to ensure your posts directly support product research.

Final Reflections

Our analysis of 3,000 consumer journeys reveals a search landscape that can be simplified into three core personas: the Traditionalist, who relies on the efficiency of the Google/Amazon duopoly; the Augmenter, who validates choices across a wide ecosystem; and the Dissenter, who bypasses the giants altogether. While these profiles provide a clear framework, they are generalizations. The Traditionalist journey is highly predictable, but our data shows the Augmenter and Dissenter groups contain hundreds of unique, individual platform combinations, revealing a vast “long tail” of personal research behaviors.

This complexity is best illustrated by the Dissenter. Our data shows this is not one group but two, with Dissenters in the US favoring a new ecosystem of social and AI discovery, while those in the UK navigate directly to brand and retail sites. This critical divergence proves that any effective marketing strategy must be local, modular, and built for the specific, nuanced ways different consumers are now finding their answers.

A Note from the Author

The method of dissent is local, which is key if this is the future of search

The “Dissenter” segment—those who bypass Google and Amazon—is not a single global persona. Our data reveals that Dissenters in the US and UK follow fundamentally different research paths, requiring distinct marketing strategies.

• When a US consumer bypasses the duopoly, they migrate to a new ecosystem of discovery platforms. The data shows:
  – 46% use YouTube
  – 28% use an AI Chatbot

• In contrast, when a UK consumer dissents, they follow a more traditional, direct path, favoring brand and retail websites they already know. The data shows:
  – 50% visit a Brand’s Website
  – 31% go directly to Other Retailers

This presents a fascinating paradox: the UK has a proportionally larger population of Dissenters, yet they use the “next-generation” social and AI platforms less than their US counterparts. This raises a critical question about the nature of the transition we are witnessing.

Why is this the case? The data suggests two competing hypotheses.

Hypothesis A: The US is a Time Machine

It’s possible that the transition to social and AI search is happening universally, drawing users away from all traditional touchpoints—not just Google and Amazon, but also from brand and retail sites. The difference we see today would simply be a consequence of this transition happening more quickly in the US.

Hypothesis B: The Cultural Divide is Real

An alternative hypothesis is that UK customers are on a different trajectory entirely. Our analysis consistently shows a higher UK preference for brand and specialist sites across all traditionalists, augmenters and dissenters. This may be a fundamental consumer trait, possibly driven by a greater mistrust of “big tech” aggregators or a cultural desire for more direct, controlled journeys.

Only future data, tracked over time, can definitively tell us whether the UK will follow the US path or if these two forms of “dissent” will continue to evolve in parallel. For now, marketers must acknowledge that the future of post-duopoly consumerism is not one-size-fits-all; it’s local.

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TL;DR

Brainlabs tested integrating Broadcaster YouTube content—specifically Sky Sports’ Women’s Euros coverage—into a Unified CTV campaign alongside Netflix and YouTube. The aim was to capitalise on high-attention broadcast moments beyond the live event, tapping into highlights, interviews, and analysis that keep audiences engaged. By using cross-publisher frequency capping and Programmatic Guaranteed buys, the campaign:

• Delivered +1.4M added unique reach without extra budget
• Achieved 142% of planned reach for the same cost
• Drove 77% product search lift and 61% brand search lift
• Captured demand-led spikes in engagement after key matches

Broadcaster YouTube offers a brand-safe, high-attention channel to extend reach, build effective frequency, and align with cultural moments—without undermining broadcaster or SVOD revenue—making it a powerful lever in Unified CTV strategy.

Insights provided by:

Alex Glover
Managing Director,
Programmatic

Paddy Peel-Barnard
Associate Director,
Programmatic

Nathan Ridout
Senior Account Manager,
Programmatic

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AI-powered search is here to stay. With 59% of searches now ending without clicks and AI Overviews appearing in over 100 countries, the rules of SEO have fundamentally changed. This comprehensive guide reveals which industries are most affected (Healthcare: 87%, Education: 87%, B2B Tech: 70%), why panic isn’t the answer, and exactly how to adapt your strategy. Learn to optimize for AI citations, rethink your KPIs beyond traffic, and build content that wins visibility in the age of zero-click searches. The future isn’t about ranking #1—it’s about being cited, synthesized, and trusted by AI systems.

Lead author: Robin Greer, Director of SEO, APAC

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Real Intelligence in Action

This partnership represents the evolution of what we can achieve with Real Intelligence – the optimal blend of artificial and human intelligence – enabling brands to connect directly with consumers. Our combined approach applies AI-driven media strategies to enable deep understanding of customers and the ability to influence action, driving real business outcomes across all channels.

Why This Matters for Our Industry

The future of media isn’t about choosing between digital or traditional channels – Real Intelligence is about mastering both to create real results. Today’s brands need partners who can masterfully orchestrate cohesive campaigns across every screen and surface, from connected TV and programmatic to retail media and paid social. The fragmented approach of working with multiple specialists simply doesn’t deliver the breakthrough results brands demand in today’s media environment.

Exverus has built an exceptional reputation for exactly this kind of cross-channel integration. As a three-time Ad Age Small Media Agency of the Year winner with eight Adweek Media Plan of the Year awards and three Cannes LIONS finalist honors, they’ve proven their approach works. Managing over $100M in annual media spend, they’ve demonstrated that full-funnel media planning across traditional, programmatic advertising, retail media, e-commerce, paid search, paid social and analytics delivers exceptional outcomes for ambitious brands like Premier Protein, Dymatize, and New Belgium and Bell’s Brewing.

Expanding Our Capabilities and Coast-to-Coast Reach

This acquisition significantly enhances our full-funnel media capabilities while establishing a strong West Coast presence, completing our coast-to-coast footprint alongside our existing East and Midwest operations. The Exverus team brings deep expertise in traditional media, planning, creative strategy, execution, and cross-channel measurement that perfectly complements our AI-driven digital capabilities.

Together, we can now serve as a true single agency of record, seamlessly connecting brand strategy to performance across all media platforms. This integrated approach enables us to maximize return on ad spend while reducing the complexity that comes from managing multiple agency relationships. We’re uniquely positioned to help brands navigate the mounting pressure to break through increasingly fragmented media landscapes.

What This Means for Our Clients

For our existing clients, this acquisition unlocks new capabilities in full-funnel media planning, traditional media, enhanced creative strategy, and deeper cross-channel expertise. For Exverus clients, they gain access to our international scale, AI-powered technology, and cutting-edge digital capabilities—all while continuing to work with the same trusted Exverus team that has delivered exceptional results.

Looking Forward

Brainlabs is an AI-powered media agency that pioneered the use of AI in advertising. Our early adoption of AI has propelled us to become one of the fastest-growing independent media agencies globally.

As we welcome the talented Exverus team to the Brainlabs group, we’re not just expanding our capabilities; we’re continuing on this path of innovation and growth. Together, we’re reimagining what’s possible when world-class talent, data-driven methodologies, and cutting-edge AI technology come together under one roof to serve growth-focused advertisers—from emerging brands with $10M in media spend to established enterprises investing up to $250M annually.

This acquisition represents the next chapter in our mission to push the boundaries of what an independent media agency can achieve through Real Intelligence.

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