AI responses are personalized to the user conducting the search by LLMs leveraging conversational context, previous chat history, and—in the case of Google—access to things like your Gmail, Google Maps, Search + YouTube history and Google Photos. 

Understanding your target audience and their purchase triggers, decision criteria, etc., is now critical to ensure you are best positioned for personalized results targeting your different target personas. 

Google is now taking this one step further. They aren’t just changing how answers are surfaced, but they are changing who surfaces them and putting more of that decision in the user’s hands. If you’ve been treating search visibility as a single, static goal, you need to rethink the problem.

Google Announces Preferred Sources

In late May, Google announced Preferred Sources — a feature that lets users manually select which sites get prioritized in their AI search results. You log into your Google account, nominate the outlets you trust, and Google’s AI Mode weights those sources more heavily when it’s constructing your answers.

Google says any website that publishes fresh content is eligible. This isn’t a publisher-only play. It’s open to anyone actively producing content. If you have a blog/resource center and publish content on a somewhat frequent basis, you are eligible to be a preferred source.

Google’s CEO, Sundar Pichai, also added something that most people glossed over. When asked how subscriptions fit into AI search, he said: “If you’ve subscribed to something, we reflect that as a preferred source for you as a user.”

This last part is specific to paid subscriptions (like The New York Times or local sources), rather than any email newsletter you’ve subscribed to. Since many users often use the “Sign in with Google” option, Google has a “Linked Accounts” feature that allows them to see the connection and automatically add the website to their preferred sources list.

Paying to subscribe to a publication or service now carries algorithmic weight in how AI search answers are built for that person.

However, as previously mentioned, through Google’s Personal Intelligence update, Google’s LLMs can tap into context from your Gmail to deliver tailored responses. With direct access to users’ Gmail and what they’ve subscribed to, it’s not hard to imagine a future update that automatically adds free subscriptions (beyond publisher business models) to your preferred sources.

What This Means

The conventional model of SEO is built around ranking. You optimize content, build authority, earn links, and compete for position on a universal SERP. Everyone sees the same battlefield.

Preferred Sources breaks that model. Now there’s a version of search where being chosen by a user is more valuable than being ranked by an algorithm.

That’s a fundamentally different game. One where brand trust, content quality, and audience relationships matter as much as technical optimization, maybe more.

The brands that win in this environment won’t just be the ones with the best keyword strategies. They’ll be the ones people actually want to hear from.

The Subscription Signal Is the Tell

The subscription piece isn’t just interesting from a publisher business model perspective. It’s interesting because it tells you how Google is thinking about trust signals more broadly.

Historically, Google inferred trust from external signals — links, domain authority, engagement patterns. Now they’re starting to surface something more direct: the fact that a real person chose to give a brand their email address, their credit card, and their ongoing attention.

That’s a hard signal. Harder than a backlink. Harder than a click.

If Google is going to use subscription behavior as a preference signal, the implication for non-publisher brands is obvious: building an audience that actively subscribes to your content is no longer just a marketing nice-to-have. It’s a potential ranking input.

What This Means for Brands That Aren’t Publishers

The announcement was framed around news publishers, but Google was explicit: any site that publishes fresh content can be nominated as a preferred source. That’s any brand leveraging a blog to produce new content regularly.

A few things follow from that:

• Consistency matters more than ever. If you’re only publishing sporadically, you’re not a preferred source candidate. Users nominate sites they return to. That requires a publishing cadence people can rely on.
• Brand discovery to decision can be faster than ever. This update is one of many that has the potential to drastically accelerate the decision process of your target audience. Upper funnel content should be leveraged to get your foot in the door and drive users down the funnel. If you can successfully engage users at the top of the funnel, this update has the potential to skew their results further down the funnel.
• Brand recognition is now an SEO input. If someone has to type your site name into a preference selector, they need to know you exist. Awareness-stage investment and search investment are no longer separate conversations.
• Email lists have new strategic value. If subscription behavior is becoming a preference signal, the audience you’ve built off-platform starts feeding your on-platform visibility. The funnel just got a new loop.

The Direction Is Clear

Personalized results are a clear focus, and the addition of preferred sources is Google’s latest step in this direction.

Google’s AI Mode is being built to understand user intent at a deeper level than keyword matching ever allowed. Personalization through chat behavior, subscription history, browsing behavior, and now explicit source preferences is how they’re doing it.

The brands that adapt earliest are the ones that stop thinking of search as a traffic channel and start thinking of it as a trust channel. Because that’s what Google is building toward.

Earn the preference. The visibility and business impact will follow.

The post How Google is Using Trust Signals to Personalize AI Results appeared first on Silverback Strategies.

There’s a phrase that’s been running through paid social conversations for the last couple of years: creative is the new targeting. It’s true. It’s also been repeated enough that most marketers nod along without doing anything different.

The reason it matters is structural. When data privacy legislation cut off the flow of browsing and app behavior into ad platforms, the targeting layer that paid social was built on collapsed. Meta rebuilt its ad serving system around what it could still see: the ad itself and how users responded to it. The algorithm now reads the creative to figure out who should see it. The hook, the format, the person on screen, the message — those are the signals the platform uses to decide who’s likely to convert.

That shift happened years ago. The accounts that have adjusted to it are pulling ahead. The accounts that haven’t are running paid social the way they ran it in 2019 and wondering why performance keeps slipping.

How paid social creative testing has evolved (and where most brands are stuck)

The industry’s response to the algorithm shift has come in stages, and a lot of brands are still operating in the wrong one.

The first stage was running more tests. Find a winner, declare it, move on. The second stage narrowed the focus to the opening seconds of an ad. Swap the hook, hold everything else constant, see what wins. That became the default playbook across most paid social accounts and still is for many of them.

The third stage was real creative diversity. Different formats, different messages, different visual approaches all running at once. That works better than the prior playbooks, because it gives the algorithm more to learn from. But on its own, diversity produces a top-performing ads list. It doesn’t produce understanding of why the top performers are working, which means there’s no clear way to scale what’s winning.

The accounts driving the strongest performance right now have gone one step further. They’ve turned creative into a system. The heart of that system isn’t volume or velocity, though both matter, it’s a tagging structure that turns every ad into a data point in a learning model.

What is a creative system in paid social?

A creative system is an operating model that combines four things: a tagging taxonomy applied to every ad before launch, a high volume of varied creative produced on a weekly cadence, structured weekly performance reviews against the taxonomy, and a re-briefing process that turns learnings into the next round of variants. The output isn’t a list of top-performing ads. It’s a map of which creative patterns drive performance for which buyers—patterns that can be reproduced, varied, and scaled across the account.

The 5 attributes to tag every paid social ad against

Before any ad goes live, it gets tagged across five attributes.

1. Concept: Whether the ad is leading with a problem, a solution, curiosity, or desire. Two ads can sell the same product but approach the buyer from entirely different psychological angles, and the angle that resonates is rarely obvious until the data is in.
2. Format: Static image, short-form video, carousel. Format isn’t a creative choice; it’s a delivery mechanism that interacts with the algorithm and the user differently for different audiences.
3. Message: Feature and benefit, social proof, objection-handling, education. This is what the ad is actually trying to communicate. Two ads with the same hook can be delivering completely different messages, and tagging that distinction is what lets the data show which messages convert.
4. Persona: Not the broad audience definition the brief was written against, but the specific micro-persona the ad is built for. A home services brand isn’t selling to one buyer. It’s selling to first-time homeowners, families in older homes, retirees downsizing, landlords managing rentals. Each of those buyers responds to different creative. Tagging by persona is what makes that visible in the data.
5. Funnel stage: Whether the ad is being built for someone unaware of the problem, aware of the problem but not the solution, aware of the solution but evaluating products, or aware of the product and choosing between competitors. Funnel stage shapes what an ad needs to do, and conflating stages is one of the most common reasons creative tests come back inconclusive.

Why tagging creative is what turns testing into learning

Without these tags, the output of a creative test is a list of which individual ads won. That’s useful for the next 30 days and then the list is stale. With the tags, the output is something different: a map of which patterns are working. Solution-oriented hooks in carousel format for one persona at one funnel stage. Education-led video for a different persona at a different stage. The performance isn’t tied to the specific ad that won, it’s tied to a pattern that can be reproduced, varied, and scaled.

That’s the difference between testing and learning. Testing tells you which ad worked last month. Learning tells you what to build next month, and the month after, and the month after that.

How much creative volume, variety, and testing velocity does paid social require?

A taxonomy doesn’t work without enough creative running through it. Three operational requirements have to be in place.

1. Volume. The algorithm needs enough variants to find patterns, and the taxonomy needs enough variants tagged in each cell to make patterns statistically meaningful. Teams getting real lift from this approach are launching eight to ten new variants per week, consistently.
2. Variety. Variants have to span the taxonomy—different concepts, different formats, different personas, different funnel stages—or the data only tells you what’s working in the narrow slice you tested.
3. Velocity. Learnings from this week’s data have to inform what’s built next week. A wins-and-losses review on a quarterly cadence doesn’t move fast enough for the algorithm or the market. Weekly review, with re-briefing baked into the same week, is the minimum cadence for a system that compounds.

Creative testing fuels lead growth and efficiency

Silverback rebuilt the creative operating model for a brand whose Meta performance had plateaued. Creative was being produced monthly. The algorithm wasn’t getting enough variety to exit the learning phase. We moved them to 10 new variants per week tagged against the taxonomy, weekly performance reviews, and a re-briefing process that turned learnings into the next week’s creative.

Leads grew 719%. Cost per lead dropped 43%. Nothing about the targeting or bidding changed. The lift came entirely from rebuilding how creative was produced, tagged, and learned from.

The question worth asking

Most teams are asking what they should test next. That question produces a backlog of one-off tests and no compounding insight. The better question is what they’re trying to learn, and whether they have the infrastructure to actually learn it. Brands that build the system are pulling ahead. Brands that don’t are running hook tests and watching performance flatten.

The post What Separates Winning Paid Social Accounts From Stalled Ones: A Creative System appeared first on Silverback Strategies.

Google framed its new AI-powered search box as its biggest update to search in over 25 years. This transition signals a new era for businesses, requiring a fundamental shift in strategy. Google unveiled major changes across media, creative, measurement, and SEO, all designed to make the search experience more dynamic, responsive, and ultimately, more effective. Here are the key announcements and what they mean for your business.

1. AI is Changing Search (and Ads)

What was the announcement: Google Search is fundamentally transitioning into Google AI. Google is introducing the new intelligent Search box, which focuses on bringing easier access to its AI search features. This continues their push of AI overviews, with a more natural transition to AI Mode for more information, all powered by Gemini. Users are asking longer, more complex questions, and Google is adapting search ads to serve as direct, dynamic answers within these new AI search results.

• They also introduced “AI Briefs,” allowing you to build campaign guidelines using everyday language, and new “agentic ads” that can actually answer user questions and pre-fill lead forms right inside the search results.

How does it impact marketers: The traditional control marketers have had over keywords, match types, and ad copy is becoming less relevant as users engage in conversational search.

• Your biggest lever in this new era is feeding Google the right context—through your creative, product feeds, and website content—so its AI can accurately summarize your business and serve it to the exact right people.

2. YouTube Drives Real Revenue

What was the announcement: YouTube now reaches over 90% of US adults and is present in 89% of purchase journeys. Google is expanding Demand Gen campaigns to be more creator-centric, allowing you to easily boost creator partnership videos directly during campaign setup.

• Google claimed YouTube drives more than double the long-term ROAS of TV and paid social. Google Demand Gen campaign placements are also expanding to Google Maps.

How does it impact marketers: YouTube can be a major growth engine for your business when it’s fueled by the right creative strategy and measurement approach.

• In Silverback’s incrementality tests, we’ve seen Demand Gen campaigns drive 6x ROAS and that Google was underreporting conversions by 300% in-platform.
• Google is rolling out new proxy metrics like Engaged View Conversions, Campaign Type Attribution and Attributed Branded Searches which are great leading indicators to track ahead of running a formal incrementality test to understand the impact on revenue.
• In addition to being a widely leveraged platform, YouTube is also one of the most influential websites for AI responses, and the #1 source for Google AI Overviews.

3. Better Creative on a Budget

What was the announcement: Asset Studio is officially becoming Google’s AI creative hub. Google is collapsing creative briefing, generation, versioning, and optimization into a single workflow. This summer, they are bringing AI video creation directly into Google Ads using their Gemini Omni model, alongside a new 1-click A/B testing feature.

How does it impact marketers: AI isn’t just going to make ads faster; it requires you to be smarter. The brands that win here will be the ones with the strongest inputs: clearer audience insights, sharper offers, and strict brand guardrails. The new 1-click A/B testing is a great tool for turning your creative ideas into a disciplined, high-speed testing system.

4. Smarter Measurement to Prove Your Worth

What was the announcement: Google is productizing advanced measurement tools. They introduced “Qualified Future Conversions” to track signals (like site visits and video views) up to six months out, and they are pushing Meridian GeoX—a tool that offers a more accessible version of geo-based incrementality testing (measuring if an ad actually caused a sale that wouldn’t have happened otherwise).

How does it impact marketers: The measurement methods we’ve recommended for years are finally going mainstream. GeoX signals feeding directly into Meridian media mix modeling (a way to see how all your marketing channels work together) is the right architecture for getting a defensible, CFO-ready view of your media performance.

• More importantly, as Google’s AI learns to optimize based on these true, causal signals instead of outdated last-click metrics, it will completely change performance.

5. Agentic Shopping within Google

What was the announcement: Google introduced the Universal Commerce Protocol (UCP) and the Universal Cart. This acts as an intelligent shopping hub that tracks products across Search, the Gemini app, YouTube, and Gmail.

• Not an eCommerce business? Google also announced that UCP will be expanded into additional verticals. As an example, future experiences will allow users to book hotels directly from AI mode or order food from conversations directly within Google Maps. It’s clear that agentic solutions will be widespread across verticals in no time.

How does it impact marketers: Getting someone to buy shouldn’t be a hassle. UCP connects your live inventory, account info, and loyalty benefits directly to Google automatically.

• Customers can add your product to their cart while watching a YouTube review or reading an email, get alerted if the price drops, and check out instantly using Google Pay. No custom coding is needed on your end.

The Path Forward

These announcements from Google I/O and Google Marketing Live confirm that the rules of engagement for marketers have fundamentally changed. Conversational search, AI-driven creative, and sophisticated measurement are now standard. The winning brands in this new era will not be defined by who can blindly turn on the AI, but by those who approach it with strategy and discipline. Success hinges on providing the sharpest inputs—clear creative, clean product data, and precise brand guardrails—and establishing the discipline to test what actually drives revenue, moving beyond vanity metrics and outdated last-click models. It’s time to build a marketing architecture that doesn’t just survive the AI shift, but dominates it.

The post Google I/O + Google Marketing Live: Key Announcements & What They Mean For Marketers appeared first on Silverback Strategies.

The economics of CTV haven’t fundamentally changed. What changed is the measurement infrastructure around it. Tools that were once enterprise-only—like incrementality testing, marketing mix modeling (MMM), brand lift studies, and household-level attribution—are now accessible to brands spending a fraction of what enterprise advertisers spend. That shift turned CTV from an awareness channel into a performance channel for anyone willing to measure it properly.

Most mid-market brands haven’t caught up. But their largest competitors have.

The attribution problem that hides CTV’s real performance

When a mid-market brand tested CTV three years ago and saw flat results in their conversion reports, the conclusion was almost always: CTV doesn’t drive revenue for us.

The actual conclusion should have been: Our measurement model can’t see what CTV is doing.

Last-click attribution—still the default in most marketing dashboards—assigns 100% of conversion credit to the final ad or link a customer touched before purchasing. It’s clean, it’s easy to report on, and it’s wrong: Last-click attribution systematically under-credits any channel that influences consumers earlier in the journey. Which is most of them. And especially CTV.

The way I think about it: imagine you spend a night out with friends. A glass of wine at the first place. A margarita at the next spot. Champagne at a wine bar. And then you end the night at a pub with a Miller Lite. The next morning, you wake up hungover and tell yourself, “That last beer got me drunk.”

Through the lens of last click measurement, that last beer did get you drunk. Your CRM will confirm it. You and I both know it wasn’t the beer.

CTV is one of those earlier drinks. It almost never gets the final click. Someone sees a CTV ad on Tuesday, searches your brand on Friday, clicks a Google ad, and converts. Last-click hands the win to Google. CTV gets nothing. Multiply that across thousands of conversions, and the channel looks dead in your reporting, even when it’s the reason the search happened.

The shift that matters isn’t a new ad format or a better targeting capability. It’s that mid-market brands now have access to measurement tools that can accurately see past the last click.

How do you measure CTV advertising?

Four tools are doing most of the work and none of them require enterprise budgets anymore.

• Incrementality testing. Split a comparable audience into two groups. One sees your CTV ads. The other doesn’t. Compare conversion rates. The difference is the causal lift your CTV spend produced (i.e. conversions that wouldn’t have happened without the ad). This is the gold standard, and the bar to run a clean test has dropped dramatically. Several DSPs and measurement vendors now support it natively. (We’ve written a deeper primer on how incrementality testing actually works if you want to go further.)

• Marketing mix modeling (MMM). A statistical model that looks at your full marketing mix — every channel, every spend level, every external variable like seasonality or promotions — and estimates how much revenue each channel contributed. MMM used to require six-figure engagements with specialty consultancies. The current generation of MMM platforms has dropped the cost and complexity dramatically, and the model is built to credit channels like CTV that influence revenue without owning the final click. Where incrementality answers “did this specific test produce lift?”, MMM answers “across our entire mix, where is each dollar actually earning its keep?”

• Brand lift studies. Survey-based measurement that asks the simple question: did your CTV campaign change what people think and feel about the brand? Ad recall, brand awareness, purchase intent. Platforms like StackAdapt have this built in. It’s not a substitute for incrementality, but it’s a useful read on whether the creative is doing its job.

• Household-level attribution. Connects the TV that served the ad to the phone or laptop that later visited the site, using shared IP addresses and device graphs. It’s directional, not precise — the assumption that everyone on the home WiFi is the same person breaks down quickly — but it gives you a real signal where you used to have none.

You don’t need all four measurement tools. But you do need at least one, and you need to run it before you write CTV off.

What CTV performance looks like when it’s working

Here’s a pattern worth understanding. One of our clients runs a brick-and-mortar social experience venue. Strong brand, loyal customer base, two years of compounding YoY growth from SEO and paid media that we’ve built and scaled together. The fundamentals of the program are working.

In Q1 of this year, growth stalled. Direct traffic (i.e. returning customers and brand searchers) dropped 9% in sessions and 13% in conversion rate compared to the same period last year. Macro pressure on discretionary spend explains some of it. But the brand search trend pointed to something more structural: fewer people were searching for the brand at all. The top of the funnel was shrinking.

Our competitive analysis showed their largest competitor is doing something about that. This competitor has 400+ locations across three concepts, $100M–$140M in annual revenue, and — here’s the relevant part — is running geographically targeted CTV campaigns by ZIP code. 

The competitor’s head of marketing called CTV a top five revenue-driving advertising source in interviews. And the company is scaling the CTV strategy across all three brands.

Their reported results: 187% blended ROAS above goal. 153% lift in conversion rate.

This doesn’t mean that existing channels like Google have stopped working for the client. But Google Search can’t grow an audience that’s getting smaller; they can only convert the demand that already exists. Closing that gap with proportionally-matched funnel expansion on CTV is a top priority.

The real question: where does the next dollar go?

Most mid-market marketers reading this aren’t deciding between CTV and nothing. They’re deciding between adding more spend to the channels that are already working — Google, Meta, the trackable performance stack — and putting that same spend into a channel like CTV that fills a different part of the funnel.

For a long time, the trackable channels won that argument by default. The reporting was cleaner. The attribution was tidier. The ROAS numbers looked better in the deck.

But every channel hits a point of diminishing returns, and on the trackable channels that point often arrives silently. Your dashboard keeps reporting conversions. Your ROAS holds. But a growing share of those conversions are people who would have bought from you anyway: brand searchers, returning customers, retargeted visitors already deep in the funnel. You’re paying to take credit for revenue you would have gotten for free. The click-based dashboard can’t tell you that, because it wasn’t built to.

That’s the moment when the next dollar stops belonging in Google and starts belonging in a channel that’s actually expanding the audience. CTV is one of the most credible candidates for mid-market brands right now — addressable, measurable, and proven in market by the enterprise versions of the same business models. The brands that move on it early get to grow the top of the funnel before their competitors close the window.

If you don’t know which side of that diminishing returns line your current spend is on, that’s where to focus. Run an incrementality test on the channels you’re already investing in. The answer will tell you where the next dollar should go.

The post CTV Just Became a Performance Channel for the Mid-Market. Most Brands Haven’t Noticed Yet. appeared first on Silverback Strategies.

This insight reflects a fundamental shift in how paid social works. Platforms like Meta have become increasingly dependent on machine learning systems that rely heavily on creative signals. Instead of marketers deciding which ads will work best for specific audiences, algorithms are constantly testing and learning which creative combinations resonate most with users.

But for the algorithm to learn, it needs options. And that’s where many brands fall short.

How Do Paid Social Algorithms Use Creative Signals?

Modern paid social advertising platforms are designed to identify patterns between audiences and creative. When campaigns launch with a wide range of messaging, visual styles, and formats, the platform can quickly determine which combinations generate engagement and conversions.

However, when brands launch campaigns with only a handful of ads, paid social algorithms (like Meta Andromeda) have very little information to work with. It becomes much harder for the platform to discover what actually resonates with different segments of the audience.

This is why creative has become the most important lever in paid social performance.

It’s not just the message being delivered to users. Creative is now the primary signal the algorithm uses to decide who sees your ads and how often they’re shown. When marketers increase creative diversity, they effectively give the platform more opportunities to match the right message with the right person.

And when that happens, performance improves.

Why Do Most Paid Social Ads Underperform?

One of the most important lessons from large-scale creative performance data is that winning ads are surprisingly rare.

Across millions of ads analyzed in studies like the recent Meta dataset, a small percentage of creative variations tend to drive a disproportionate share of results. The majority of ads perform average or below average.

For marketers accustomed to traditional advertising, this can feel counterintuitive. Historically, campaigns were built around a single big creative idea that was expected to carry performance.

But performance marketing works differently.

Rather than trying to predict the winning ad in advance, successful teams build systems that allow them to discover winners through testing. The process becomes less about creative intuition and more about structured experimentation.

In other words, the goal isn’t to guess the right ad. The goal is to find it faster than your competitors.

Creative Diversity Is Where the Real Insights Come From

Increasing creative volume alone isn’t enough. The most effective testing programs also emphasize creative diversity.

Too often, brands interpret creative testing as producing slightly different versions of the same ad. A new color treatment. A different headline. A slightly modified video edit.

But these minor variations rarely generate meaningful insights.

Real performance breakthroughs tend to happen when brands explore fundamentally different ways of communicating their value. One creative angle might focus on a customer pain point, while another highlights social proof. A third might rely on humor or storytelling. Another may use UGC-style ads — user-generated content that feels native to the feed and builds trust through authenticity rather than production value.

Each of these approaches speaks to different motivations, emotional triggers and creative tastes within the audience. Because those motivations vary widely across consumers, it’s nearly impossible to predict which angle will resonate most strongly without testing.

Creative diversity allows marketers to explore those differences at scale.

From Individual Ads to Scalable Creative Systems

Another shift happening in high-performing paid social programs is the move away from chasing individual winning ads.

Even the most successful ad will eventually hit ad creative fatigue. Audiences stop responding. Platforms change. New competitors enter the market.

Instead of focusing on one-off creative hits, leading teams focus on identifying creative systems—repeatable combinations of messaging, visuals, and formats that consistently perform well.

“A creative system is a repeatable combination of a proven audience insight, a clear messaging angle, a recognizable visual pattern, and an ad format that consistently drives performance.”

For example, a strong insight about customer motivation might evolve into multiple ads that explore the same concept through different hooks, visual treatments, or storytelling styles. Each variation reinforces the same core idea while giving the algorithm additional signals to optimize against.

Over time, this approach creates a scalable framework for creative production. Instead of constantly reinventing campaigns from scratch, brands can expand on what they already know works.

What Makes a High-Performing Paid Social Creative Strategy?

While creative testing velocity is critical, it shouldn’t be mistaken for random experimentation. The most successful creative programs begin with research designed to uncover the motivations behind customer behavior.

This research often includes analyzing customer sentiment, identifying purchase triggers and barriers, studying competitive messaging, and understanding broader economic or cultural trends influencing buying decisions.

Insights are sourced rapidly—through review-mining, AI-assisted audience surveys, and competitive & market analysis—and allow marketers to build creative around specific audience motivations, not generic messaging.

From there, those ideas can be translated into multiple visual and messaging directions. Each concept becomes a hypothesis that can be tested against others in market. Over time, the results of those experiments reveal which narratives resonate most strongly.

This process transforms creative development from a subjective exercise into a data-informed system.

What’s Holding Back Your Paid Social Creative Performance?

Ironically, the biggest obstacle to this approach isn’t budget or platform complexity.

It’s approval process.

Many organizations still operate with creative workflows designed for traditional advertising. Campaigns move through lengthy approval cycles, with teams striving to perfect every detail before launch.

But this mindset can be counterproductive in performance marketing environments where success depends on experimentation and speed.

If winning ads only emerge from a small percentage of creative, then scale and testing velocity become critical advantages. When approval processes slow down production, brands dramatically reduce the number of ideas they can test. Meanwhile, competitors who embrace faster, more iterative creative workflows are constantly discovering new performance insights.

In this environment, perfection can become the enemy of performance.

Interestingly, some of the highest-performing ads today are not the most polished ones. They’re often lo-fi, creator-style videos or simple concepts that feel authentic and native to the platform. These formats can be produced quickly and tested rapidly, allowing teams to learn far faster than traditional production cycles allow.

The Competitive Advantage of Creative Velocity

As paid social platforms continue evolving, the role of creative will only grow more important.

But the advantage will not come from predicting the perfect campaign. It will come from building systems that generate ideas quickly, test them aggressively, and scale the insights that emerge.

Brands that embrace creative diversity and testing velocity will create a learning engine that compounds over time. Those that remain stuck in slow approval cycles and low creative output may never uncover the messages that truly resonate — and in an environment where creative is the difference between average and exceptional performance, that gap can grow quickly.

Building a scalable performance creative system requires four things:

• Research-driven creative strategy grounded in real audience motivations, not generic messaging assumptions
• Creative diversity that explores fundamentally different angles, not just minor variations of the same idea
• High-volume testing that gives the algorithm enough signals to identify winning patterns
• Rapid iteration cycles that prioritize speed and learning over perfection

The post What a Study of $1.3B in Meta Ad Spend Reveals About Paid Social Creative Strategy appeared first on Silverback Strategies.

Google’s ecosystem is everywhere:  search, email, maps, photos, YouTube… By connecting the dots between them, Google is creating a hyper-personalized AI search experience that no competitor can easily replicate.

While OpenAI builds great models, Google owns the infrastructure of daily life. That gives it a huge edge in adoption and influence.

Google is building the future of AI-powered search, and it’s happening inside products your customers already use.

Google’s Building the Better AI Search — Here’s Why That Matters

For marketers, this raises the bar.

Search results have always been personalized based on things like your location and search history, but this is another example of AI Search Experiences being personalized based on far more data than before.

AI Mode isn’t just pulling from public search data. It’s pulling from private, personal behavior — email receipts, what’s in your photos, and habits.

If you’ve been treating SEO, Content and Media like isolated channels, this should be a wake-up call. The next wave of visibility will depend on how well your brand fits into someone’s entire digital life, not just how well your site ranks.

Your Emails Are Now Search Inputs

If Gmail is now feeding data into search experiences, your email content is part of the AI training loop.

That means:

• Clarity and structure in emails matter more than ever.
• Brand interactions that happen in the inbox aren’t just for retention — they now influence future discovery.
• If someone’s purchased from you before, how you communicate post-sale might decide whether you show up again in AI-generated results.

Make sure your emails speak in plain, relevant language — and that they reflect the value you deliver. AI will be watching.

Broad Personas Are Dead. Deep Customer Insight Wins.

AI personalization doesn’t care about vague personas like “Working Mom” or “Tech-Savvy Millennial.” It’s looking at real people, real behaviors, real interests to serve the best result for that individual.

Imagine 2 different people in the same location searching for a coffee shop and being served different results with different text based on what Google knows about them. 

• Jill has 2 dogs and Google knows she loves taking them everywhere she can based on her photos, search history, location history, and her emails. Jill’s first result is a dog-friendly coffee shop featuring a photo of a dog eating a pup cup — she’s in.
• Jack’s history shows that he prefers food with his coffee. Google decides to show him a coffee shop that’s slightly further away but has a more robust menu. It specifically calls out food that it knows he likes in the description.

If you want to win in AI-driven search, you need to:

• Dig into first-party data
• Understand customer habits, lifestyle patterns, and motivations through customer interviews and qualitative research
• Create assets and copy that reflect who they are and how your brand fits their world

Forget chasing everyone. Focus on becoming essential to the few customer profiles who matter most.

Don’t Just Rank — Show You’re the Right Fit

With AI Mode, visibility won’t just be about content volume or backlinks.

It’ll be about relevance.

Relevance to the user’s past behavior. Relevance to the way they talk, shop, and think. Relevance to what their inbox and activity suggest they need next.

That means:

• Shift from keyword-heavy content to value-driven content
• Use language that mirrors your customers’ voice
• Build trust signals across every channel — web, email, product, support

Your goal isn’t to win a search result. It’s to show up when it matters most — when AI is making the decision on their behalf.

This Isn’t Just Google — Meta’s Already Moving This Way

If this shift feels familiar, it should. Meta’s been on this path for a while.

We’ve already seen how Meta’s Andromeda AI engine changed paid social — pushing the platform to favor creative diversity and lean into micropersonas. Instead of chasing one “ideal” customer, brands now need to create for multiple variations of the same audience, based on interests, behavior, and context.

That same trend is hitting search.

Google’s AI Mode is the search equivalent of what Meta’s AI already does in ads: learn who your customer is by watching how they engage, then serve content tailored to their unique signals. Different users, different experiences — even if the intent looks similar on the surface.The takeaway is clear:

Hyper-personalization is no longer a paid media trend. It’s becoming the foundation of organic discovery too.

And just like with Meta, brands who create with one-size-fits-all messaging will get left behind.

Here’s What to Do Next

This isn’t a wait-and-see moment. It’s a move-fast-or-fall-behind moment.

Here’s where to start:

1. Audit your email content. Is it clear, relevant, and aligned with how your audience talks?
2. Invest in customer research. Go beyond demographics — understand behavior, intent, and context.
3. Reframe your content strategy. Create assets that help AI understand when your brand is the right answer.

Google just raised the stakes on personalization. If you’re not ready, your competitors will be.

Need a partner who knows how to adapt fast?

We help marketing teams build strategies that cut through the noise and align with where search is going — not where it’s been. Let’s talk.

The post AI Is Rewriting the Rules of Personalization. Are You Ready? appeared first on Silverback Strategies.

For ten years, scrolling and clicking was how people shopped online. Now, they’re looking for something easier. AI tools are changing how people think about shopping; from doing it themselves to having AI do it for them. This raises a big question for business leaders: Will AI agents eventually do all our shopping?

How the Universal Commerce Protocol (UCP) Enables AI-to-AI Shopping

At the big retail conference in January 2026, Google made a major announcement with Shopify and Walmart. They introduced something called the Universal Commerce Protocol (UCP). This is their attempt to create a universal system that lets AI shop and buy things across any website.

Image Description: Google CEO Sundar Pichai at NRF’s Retail’s Big Show 2026 announcing the Universal Commerce Protocol (UCP), a pivotal development in agentic commerce and AI-to-AI shopping analyzed by Silverback Strategies.

Some experts think AI shopping agents won’t work because the incentives, tech, or consumers aren’t ready yet. But the money being invested says otherwise. Google, Microsoft, and Amazon have committed over $100 billion to build the technology needed for computers to buy from computers. People are moving away from traditional search because AI systems can process information and make decisions more efficiently than humans browsing and clicking.

Is agentic commerce a structural shift or a high-stakes mirage?

We don’t know yet if AI shopping will last. People want convenience, but they’re also skeptical about letting AI make their choices. Big companies are building systems for AI to handle purchases, but success depends on whether shoppers and stores are willing to give up the experience of browsing and discovering products themselves in exchange for speed and efficiency.

Convenience vs. Trust and Control

The biggest question facing retail is whether people will trade control for convenience. Recent data from 2025 and 2026 shows what researchers call the “Privacy Paradox”: people say they want control, but their actual behavior reveals they’ll give it up for efficiency as long as there’s not much at risk.

Here’s what the data shows about how consumers are navigating this tradeoff.

Research Yes, Buying No

People are happy to use AI for shopping research, but hesitant to let it complete purchases.

• Research adoption is high: About 70% of shoppers use AI tools to help with planning, comparing products, or reading reviews.
• The trust gap: Only 12% to 17% trust AI recommendations without double-checking, and just 4% are willing to let AI complete a purchase on its own.
• Room for growth: While only 24% feel comfortable with AI completing purchases today, 64% say they’d be open to it with the right protections in place.

What Makes People Hand Over Control?

Convenience alone isn’t enough. People need a compelling reason to let AI shop for them:

Key finding: People see AI shopping as a financial tool, not just a time-saver. They’re more willing to give up control if they think the AI can get better deals than they can.

It Depends What You’re Buying

People’s willingness varies dramatically by product type:

• High comfort (low stakes): 40% are comfortable with AI handling groceries and household basics.
• Low comfort (high stakes): Only 16% would trust AI with health and wellness purchases, and even fewer with major financial decisions.

Age and Gender Differences

Younger people are adopting AI shopping much faster:

• Gen Z & Millennials: Nearly 31% of their regular monthly purchases could be handled by AI within a few years.
• Gender gap: 67% of men are somewhat comfortable letting AI handle purchases, compared to 47% of women, who have higher concerns about security and privacy.

What People Need Before They’ll Let AI Shop

For consumers to move from browsing to buying with AI, three things must happen:

1. Trust the AI works for them: People must believe the AI is finding the best option for them, not just promoting high-profit items for retailers.
2. Set spending limits: 28% say they’d only use AI shopping if they could set budget caps and “kill switches.”
3. Easy returns: 30% need a simple refund and return process before they’ll give up control.

Will Retailers Meet in the Middle?

Retailers are the second big obstacle. Many don’t want AI shopping agents because these agents eliminate profitable impulse purchases and advertising revenue. When AI automates shopping, it skips past all the ads and browsing that make retailers money. But recent announcements at the big retail conference suggest a compromise is emerging. Google, Shopify, and Walmart are creating systems that let AI handle transactions while retailers still control the sale and keep customer data.

What Does it Add Up To?

This will split consumers into two groups. AI agents will likely take over routine, boring purchases (the stuff people buy regularly without much thought). By the time company leaders start asking abo ut AI shopping, customers may already be usingit for a significant portion of their household purchases. But for products people care about (like fashion, luxury items, or big decisions) humans will stay in control. In these categories, strong brands become more important than ever. When AI handles the mechanics of shopping, brands need to give people compelling reasons to choose them.

How Signal Density and the “Invisible Shelf” Impact Brand Visibility for Bots

Business leaders should think of AI shopping readiness as upgrading how they organize product information, not replacing their brand or customer experience. By using standard formats like UCP, brands can make sure AI can find them while still creating the emotional connections with customers that AI can’t replicate.

The goal isn’t to stop investing in your brand or website (in fact CMOs are listing “branding” as their number 1 priority in 2026, even over things like gen ai, and data-driven marketing); it’s to make sure your information is accessible. If an AI agent can’t easily verify your return policy or check your inventory through organized data, you’re invisible to that bot. As brands move onto what Silverback Strategies terms the ‘Invisible Shelf,’ the battle for visibility shifts from eye-level retail placement to data-level manifest optimization. You succeed by increasing your “Signal Density,” providing more organized, verifiable information that AI systems can read and understand on the open web.

Strategic Implications

If you’re not preparing for AI shopping, you’re betting that people prefer the hassle of shopping themselves over convenience. But going all-in on an unproven AI-only future is just as risky. The smart approach is the middle path: organize your data in ways that help AI while also improving your current marketing, so you’re ready for whatever comes next.

1. Brand Recognition: Your brand’s reputation is what makes people tell their AI: “Only buy from [Brand Name].”
2. Multiple Systems: As different AI shopping systems compete, your product information needs to work with all of them.
3. Standing Out on More Than Price: Well-organized data lets you show AI shoppers why you’re worth it (like faster shipping or better sustainability) not just a lower price.

3 Strategies to Optimize Your Brand for Agentic Commerce and AI Trust Scores (ATS)

• Step 1: Check Your Data Quality. Make sure all your product details (inventory, warranties, return policies) use standard formats that AI can read. If a machine can’t find it, it might as well not exist.
• Step 2: Invest in “Contextual Brand.” Focus your creative investments on platforms where humans still “discover” (CTV, social) to ensure your brand is the “default” choice in the agent’s prompt. Make sure to measure these channels based on incremental impact, not just last-click.
• Step 3: Stay Informed on New Systems. Keep track of emerging shopping protocols like UCP and Agent Payments Protocol. Being able to work across different systems will be crucial for reaching customers.

The post What is Agentic Commerce? How the Universal Commerce Protocol (UCP) is Changing Commerce in 2026 appeared first on Silverback Strategies.

If the search landscape is evolving, the way we measure it needs to evolve, too.

The Problem with Old-School SEO Reporting

For years, SEO success was measured using a familiar set of metrics:

• Keyword Rankings
• Organic Traffic
• Organic Conversions

These metrics aren’t wrong, they’re just incomplete in today’s search landscape. Traditional SEO reporting was built for when search results were mostly just ten results on a page, rankings directly correlated with organic traffic, and consumer journeys were straightforward. 

In that environment, success was easy to justify. Today, that model breaks down.

Why Today’s Landscape is More Complicated

There are several realities that legacy SEO reporting fails to account for:

• Increase in places to search: Google is still the priority, but with the introduction of AI Search (ChatGPT, Gemini, etc.) and increased usage of social media platforms (YouTube, TikTok), there are far more places where consumers are searching for brands and information.
• Search behavior varies by platform: How consumers interact with traditional search is often far different from how they interact with AI search. For example, there was a study that was done that found the average traditional Google search was 3 words long, and the average ChatGPT search was 42 words long. This makes it difficult to rely on historical target keywords, given how search behavior varies by platform.
• Zero-click searches are growing: With LLMs & AI Overviews, consumers are finding information without having to click into a website. As a result, many websites are seeing a decrease in organic traffic, further proving that, at times, traffic has been more of a vanity metric.
• Conversions rarely happen on first-touch: Consumers leverage multiple channels in their journey, and attribution reports under credit brand discovery channels.

The Modern SEO Measurement Playbook

A strong measurement playbook blends traditional SEO metrics, AI Search visibility, and business outcomes.

1. Search Visibility (Not Just Rankings)

You should continue to leverage keyword ranking platforms, but it’s only part of the picture.

When it comes to visibility, you now have to consider your presence in AI search. However, this is increasingly difficult with how personalized AI searches are.  To effectively measure AI visibility, you need two different types of tools in your playbook:

• Estimated Overall Visibility: Again, with how personalized AI search is, you’ll never have a tool that provides a complete, accurate picture of your visibility. However, there are tools (ex. Ahrefs Brand Radar) out there that can provide you with an estimate to use as a benchmark or a comparison against your competitors. 
• Priority Prompt Visibility: In addition to overall visibility, you should also hone in on prompts you know your target audience is using to either discover brands, compare options, or make a decision. Tools like Athena, Profound, Ottery, etc., will then give you much more granular reporting specifically for those prompts. Not only can this help you determine where you have visibility, but also help you determine whether or not you are mentioned with positive or negative traits.

Between these two tools, you’ll be able to pinpoint where you have strengths and weaknesses from a visibility standpoint.

2. Traffic (Not Just Organic)

Traffic is still an important metric to keep an eye on, but the way in which we measure it has shifted. 

With the zero-click landscape, it’s not uncommon for websites to have seen a traffic decline over the past year or so. The decline is most often coming from upper funnel blog traffic that’s now easily answered without a user ever having to get to your website. As a result, traffic isn’t the impactful metric many once thought it was. Traffic is still incredibly valuable to measure, but you should do so by measuring the traffic to the pages most likely to drive down-funnel business impact.

With the introduction of AI search, it’s also important to broaden how you filter for traffic. Traffic coming from AI models (ChatGPT, Gemini, etc.) is typically attributed as referral or direct traffic in most reporting platforms (GA4, Adobe Analytics, Hubspot, etc.). So, as a starting point, broadening your reporting to include referral and direct will give you a better understanding of how AI search is playing into the equation. 

In most reporting platforms, you can dive a layer deeper by looking at the referral source to understand which AI models are not only driving traffic, but which ones you should prioritize from a measurement standpoint.

3. Conversions/Revenue (Not Just Organic)

The ultimate measure of SEO success hasn’t changed. At the end of the day, it still comes down to whether or not your SEO efforts are driving the growth of your business. Celebrating traffic improvements means nothing if you don’t have the down-funnel performance to show for it.

Similar to how traffic is measured, it’s important to filter beyond organic conversions/revenue in this new landscape, and begin including both direct and referral sources to account for the impact from AI models.

Bringing It All Together

The modern SEO playbook isn’t about abandoning traditional metrics, it’s evolving them.

Strong SEO measurement helps connect visibility to business outcomes. As search continues to evolve, the brands that win won’t be the ones chasing rankings and traffic. They’ll be the ones measuring what truly matters.

The post Modern SEO Measurement Playbook appeared first on Silverback Strategies.

That’s becoming increasingly challenging, given how often LLMs are sourcing third-party websites, such as Reddit and now YouTube.

In addition to being the second most visited website in the world, behind only Google, it’s also now the #1 most sourced website within Google’s AI overviews.

YouTube’s Dominance of AI Overviews

According to recent BrightEdge data, YouTube has become the most commonly cited website within Google’s AI Overviews. The data found that AI Overviews cited YouTube up to 30% of the time, compared to Vimeo’s 0.1%. When you factor in how often AI Overviews appear, which is now over 50% of queries, this presents quite the opportunity.

It’s not just how often YouTube is being cited; it’s also the shift in the types of searches that YouTube is being cited for. YouTube is often seen as upper funnel, but it’s being cited for searches throughout the funnel, including high intent terms.

Why YouTube

Similar to optimizing for YouTube search, AI Search is influenced by a video’s title, description, tags/categories, playlists, etc. In addition, AI models can “watch” and “listen” to video transcripts to understand the content within the video itself.

As a result, this type of content makes it easier for Google to confidently source YouTube videos within AI Overviews because they can easily answer queries like “how to” or “how much” as an example.

What Does This Mean For Your SEO Strategy

In today’s AI Search landscape, it’s easier than ever to start your journey with brand discovery and make a quick decision, so being part of the conversation is everything. With YouTube’s dominance of AI Overviews, in order to be part of that conversation, you need to consider the role of YouTube.
If video isn’t a part of your current strategy, determine whether or not it should be based on where your audience spends most of their time. Using an audience research tool like SparkToro, you can get a sense of what search and social platforms your audience typically uses.

Between search engines, LLMs, and social platforms, organic search is everywhere. In order to ensure your brand is as well, you need a modern SEO playbook.

1. Audience Research: It all starts with understanding your target audience. Where is your audience searching for a solution? What social platforms are they using to research their options? What type of content are they looking for at each stage of their journey?
2. Content Strategy: With an understanding of what platforms your audience is on and the type of content they are looking for, the content strategy becomes clear. Consumer journeys are complex, typically across multiple channels, so your content strategy needs to meet your audience where they are.
3. Measurement: A modern SEO playbook requires modern SEO measurement. Organic Traffic & rankings are no longer sufficient on their own. In today’s AI Search landscape, you also need to measure how often you are mentioned and cited. How does your AI Search share of voice compare to your competitors? As your visibility improves, keep a close eye on direct and referral traffic and conversions.

Bottom line, the search landscape is evolving, and your SEO strategy needs to keep up, shifting from chasing rankings and organic traffic to understanding visibility across channels and the impact on driving real business growth.

The post YouTube is the #1 Source for Google AI Overviews: Is Your SEO Strategy Ready? appeared first on Silverback Strategies.

The dashboard shows a 10x ROAS. The bank account shows flat revenue. This gap, call it the CFO Gap, is the difference between what platforms claim to deliver and what actually shows up in your business.

Since the late 1990’s, when digital advertising and web analytics began to boom, we papered over this gap with attribution models. Multi-touch attribution. Last-click attribution. Weighted attribution. These weren’t measurement tools. They were credit-claiming mechanisms dressed up as science.

In the early 2020’s, something shifted. Google and Meta did something that would have seemed suicidal five years ago: they’ve been steadily democratizing incrementality testing and open-sourced their Marketing Mix Models.

The platforms are handing you the tools to measure whether they’re actually working.

The Enterprise Tax on The Truth Just Disappeared

Historically, “truth” was expensive.

Running a conversion lift study? You needed a six-figure monthly ad budget just to get Meta or Google to flip the switch. Building a Marketing Mix Model? Budget $500K, clear six months on your calendar, and hire a team of statisticians to manage the black-box software.

For mid-market brands, the price of knowing what actually worked was prohibitive. So most settled for the “good enough” lie of click-based attribution.

That barrier is gone.

Incrementality Testing: Google slashed the minimum budget for incrementality tests from $100,000 to $5,000. Meta integrated “always-on” lift testing directly into Ads Manager. Causal measurement isn’t a six-month project anymore. It’s a button click.

Open-Source MMM: Google’s Meridian and Meta’s Robyn moved Marketing Mix Modeling from proprietary software to transparent, open-source code. You can now incorporate non-media variables (pricing, promotions, inflation, seasonality) to isolate true marginal ROI. No PhD required. No half-million-dollar invoice.

This isn’t a feature update. It’s a strategic acknowledgment that in an AI-driven ad market, platforms need to prove causality to justify continued investment.

Why Are Google and Meta Giving Away Measurement Tools?

Simple. They hit a ceiling.

Traditional attribution models are biased toward harvesting existing demand. Last-click overvalues the final touchpoint (the branded search, the retargeting ad) because it’s the easiest to measure. The result? Marketers keep dumping money into saturated bottom-funnel channels while starving the demand generation engines.

By lowering the barrier to lift testing and modeling, Google and Meta are showing you the halo effect. That YouTube video in October that drives the organic search in December. The awareness or prospecting ad that makes your branded search 40% more efficient. The “unmeasurable” parts of the funnel that are often your most incremental levers.

Don’t mistake this defensive move for altruism. If marketers can’t prove incremental value, they stop spending. If they stop spending, the platforms lose.

How Do Incrementality Tools Improve AI Campaign Performance?

In the GEM Era (Meta’s Generative Engine Model), AI bidding algorithms like Performance Max and Advantage+ are only as effective as the truth you feed them.

Optimize for last-click conversions, and the AI hunts for people who were already going to buy (or bots that pretend to). You pay for credit, not growth.

Use incrementality testing to isolate the causal signal, and you can feed a more accurate value back into the platform. You’re not asking the AI to find “conversions.” You’re asking it to find incremental revenue.

In our experience at Silverback Strategies, brands that simply shift budgets based on their open-source MMM and incrementality test results improve performance by 20% compared to relying solely on platform-reported ROAS.

This is the competitive edge for mid-market brands: low-cost paths to use enterprise-grade measurement to train your AI campaigns better than your competitors can.

What Should Marketing Leaders Do With These New Tools?

As these tools go mainstream, the role of the performance marketer shifts from “proving you didn’t waste money” to “identifying where the next dollar delivers the highest marginal return.”

For CMOs and CEOs, this represents moving from defensive reporting to offensive capital allocation. Instead of spending your time justifying last quarter’s spend, you’re confidently reallocating budget from saturated search terms into high-growth video, social, and demand channels.

The 2026 Measurement Stack:

1. Causal Truth: Run conversion lift studies and incrementality tests on your largest channels to measure true incremental lift
2. Strategic Budgeting: Build or deploy an open-source MMM (Meridian or Robyn) to understand cross-channel effects and optimal budget allocation
3. AI Optimization: Feed lift-adjusted conversion values back into automated bidding to train algorithms on incremental outcomes

The tools are accessible. The cost barrier is eliminated. The only gap left is expertise, knowing how to navigate the new physics of the ad market.

The post The Causal Pivot: Why Google and Meta Are Giving You the Tools to Grade Their Homework appeared first on Silverback Strategies.