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.

Executives don’t think in terms of impressions, CPCs, or keyword rankings. They think in terms of profitability, efficiency, pipeline velocity, and the ability to hit revenue targets. That’s why the strongest executive reporting reframes channel metrics into business outcomes.

We’ll show you how to communicate performance in a way that leadership values, trusts, and understands and how to avoid the reporting traps that make updates feel tactical instead of strategic.

Why Vanity Metrics Fall Flat With Executives

Marketing teams often default to channel metrics because they’re accessible, familiar, and easy to quantify. Impressions, clicks, CTR, average position, CPC, organic sessions, and so many more.

While these matter for optimization, they rarely mean anything to the C-suite.

Executives aren’t asking: “How many clicks did we get?”

They’re asking: “Did we generate high-intent pipeline efficiently?”

Vanity metrics describe what happened. Leadership wants to understand why it matters.

To earn trust and credibility, your marketing performance report should lead with impact, not activity.

Connect SEO and Paid Media to Revenue, Efficiency, and Pipeline

The key to effective executive reporting is connecting channel performance to business outcomes. That requires moving from channel KPIs to the metrics that matter most to CEOs, CFOs, and CMOs.

For SEO, that means emphasizing:

• Pipeline generated from organic search
• Organic market share
• Revenue impact of priority pages
• Efficiency gains from non-paid traffic (blended cost per lead/sale)

For paid media, that means focusing on:

• Cost per lead/sale
• ROAS and ROI
• Velocity improvements (shorter time to conversion)
• Revenue influenced by paid campaigns

When you connect performance to pipeline and revenue, your reporting shifts from “Here’s what we did” to “Here’s how we’re driving the business.”

Translate Marketing KPIs Into Language That Resonates With Executives

Executives think in financial and strategic terms. Marketers think in channel terms. Bridging that gap is where strong reporting shines.

Leadership cares about:

• CAC (Customer Acquisition Cost)
• LTV (Lifetime Value)
• ROI (Return on Investment)
• Pipeline Contribution
• Forecast Accuracy

Your monthly reporting in marketing should draw a clear line between channel performance and these business priorities.

Build a Reporting Narrative Leadership Actually Wants to Read

Executives don’t want a data dump. They want a clear, strategic narrative.

A strong reporting narrative includes:

1. What happened: A concise summary of performance across SEO and paid media.
2. Why it matters: The business impact of those changes — positive or negative.
3. What you’re doing next: The plan for optimizing, capitalizing on opportunities, or correcting issues.
4. What leadership needs to know: Budget considerations, resourcing needs, or potential risks.

This narrative builds trust because it proves you understand the business, not just a channel.

Common Mistakes Teams Make When Reporting to Leadership

Even skilled marketers fall into familiar traps. The most common mistakes include:

• Focusing too much on volume, not value. Executives care more about pipeline quality than traffic quantity.
• Reporting without interpretation. Metrics with no explanation shifts the burden of analysis onto leadership.
• Burying strategic insights under tactical details. Executives want clarity, not channel complexity.
• Treating the report as a recap instead of a roadmap. Your report should guide decision-making, not just summarize last month.
• Using marketing jargon. Terms like “assisted conversion,” “broad match,” or “crawl depth” mean nothing without translation.

Avoiding these pitfalls helps teams build credibility and secure buy-in for future marketing initiatives.

Best Practices for Executive-Ready Monthly Reporting

To create more effective marketing reporting, follow these principles:

• Lead with outcomes, not actions. Highlight revenue, efficiency, and pipeline contributions before channel performance.
• Use visuals to simplify complexity. Executive reporting is stronger when data is scannable and easy to compare.
• Tailor the report to your audience. CMOs want trends, CFOs want efficiency, CEOs want growth. One size does not fit all.
• Be proactive, not reactive. State clearly what’s working, what’s at risk, and what decisions need to be made.
• Connect dots clearly. Tie every data point to a business goal or decision.

When your report feels less like a recap and more like a decision-making tool, leadership will actually use it.

Get Your Insights Ready for the Boardroom

Reporting is really about shaping decisions. When marketers translate channel performance into business impact, they earn credibility, secure buy-in, and strengthen their influence within the organization.

The truth is simple: SEO and paid media metrics matter most when they tell a story leadership can act on. And with strong reporting, your team can lead that story.

At Silverback, our reporting philosophy is simple: clarity, impact, alignment. We act as an extension of your marketing team, making sure reporting supports internal goals and leadership expectations. We frame performance in terms of business impact and define the metrics that matter most.

Let’s talk about how our team can support your next executive report with a smarter strategy and clear impact.

The post Reporting to Leadership: Translating SEO & Paid Media Metrics into Business Impact appeared first on Silverback Strategies.

That gap is where marketing loses influence.

To build credibility and justify continued investment, in-house teams must shift from channel-based metrics to strategic measurement. 

We’ll show you how to make that leap, offering best practices for aligning marketing strategies with business objectives and turning your reporting into a decision-making asset, not a dashboard recap.

Why Misaligned Reporting Undermines Marketing’s Value

When reporting focuses heavily on channel-level KPIs, leadership struggles to see how marketing contributes to company-wide goals. This leads to:

• Confusion (“What does this mean for pipeline or revenue?”)
• Misaligned priorities (“Should we invest more or pull back?”)
• Reduced influence (“Marketing isn’t proving ROI.”)
• Budget vulnerability (“If the impact isn’t clear, cuts feel logical.”)

Marketing isn’t lacking impact, just translation. The more your reports mirror business objectives, the clearer your value becomes.

Channel KPIs vs. Strategic Business Objectives

Channel KPIs (like CTR, CPM, impressions, keyword rankings, average position, or bounce rate) reflect performance within a platform. They’re useful for optimization but insufficient for business alignment.

Executives think in terms of:

• Revenue
• Customer acquisition cost (CAC)
• Lifetime value (LTV)
• Pipeline contribution
• Efficiency and scalability
• Market share
• Customer growth and retention

These are strategic business objectives. Not marketing metrics.

To bridge the gap, every KPI you report should help answer one question: “How is this helping us grow?”

A Simple Framework for Mapping Marketing Metrics to Business Goals

To align your reporting with company priorities, shift from a channel-centric view to a multi-stage impact framework.

Stage 1: Brand Visibility → Awareness Metrics

• Examples: organic impressions, share of voice, branded search volume
• Business lens: Are we gaining mindshare in our market?

Stage 2: Traffic Quality → Engagement & Intent Metrics

• Examples: high-value page engagement, session quality, lead magnet conversions
• Business lens: Are we attracting the right audiences?

Stage 3: Pipeline Contribution → Lead & Opportunity Metrics

• Examples: SQLs, MQL-to-SQL conversion, assisted conversions
• Business lens: Are we influencing revenue-generating pipeline?

Stage 4: Revenue Impact → Growth & Efficiency Metrics

• Examples: CAC, ROAS, revenue attribution, revenue influenced
• Business lens: Are we driving growth efficiently?

This simple progression (awareness → intent → pipeline → revenue) turns your SEO and paid media metrics into a strategic narrative leadership understands immediately.

Setting the Right Measurement Priorities for Your Company’s Growth Stage

Not all metrics matter equally at every stage of company maturity. Reporting should reflect where your business is trying to grow next.

• Early-stage or emerging companies: Focus on awareness, demand creation, and early pipeline signals.
• Scaling companies: Focus on pipeline quality, acquisition efficiency, and channel contribution to revenue.
• Mature companies: Focus on margin optimization, retention, customer value, and market share expansion.

The key: Align your measurement priorities with what your leadership is prioritizing right now, not what your channels naturally measure.

Tailoring Reports to the Audience: What Executives Actually Care About

Different stakeholders need different levels of detail:

CMOs care about:

• Market share and visibility trends
• Channel contribution to pipeline
• Budget efficiency
• Strategic opportunities and risks

CFOs care about:

• CAC, ROI, and cost efficiency
• Forecast accuracy
• Spend justification
• Predictable growth metrics

CEOs care about:

• Revenue impact
• Competitive advantage
• Strategic clarity
• Big swings—not channel detail

Your reporting should flex based on who’s reading it, surfacing the narrative and metrics they value most.

Common Reporting Traps to Avoid

Too many teams fall into familiar pitfalls that weaken their reporting:

• Data dumping instead of storytelling
• Chasing vanity metrics that don’t affect business goals
• Ignoring cross-channel context
• Over-relying on dashboards instead of providing interpretation
• Reporting without recommendations

Avoiding these traps keeps reporting strategic, digestible, and aligned with leadership expectations.

Align Your Marketing Efforts, Drive Smarter Decisions

Aligning marketing metrics with business objectives is a mindset shift. When in-house teams connect channel performance to outcomes like pipeline, revenue, efficiency, and market share, they strengthen credibility, accelerate decision-making, and make a clear case for ongoing investment.

This alignment turns marketing from a cost center into a strategic growth driver.

At Silverback, we specialize in supporting in-house teams. Our monthly reporting prioritizes business impact as we help you translate performance into executive-ready narratives. Leadership gets the “so what,” not a slideshow of charts.

When your reporting reflects business goals, marketing goes from “nice to have” to “critical to growth.”

Let’s build a reporting framework that aligns your efforts with what leadership wants to see and drives smarter, faster decisions across your team. Get in touch.

The post Aligning Marketing Metrics with Business Goals: A Reporting Framework for In-House Teams appeared first on Silverback Strategies.

The death of Google was supposed to be a foregone conclusion. Since OpenAI launched ChatGPT 3.5 back in 2022, and as recently as early 2025, the prevailing wisdom held that large language models would commoditize search, stripping Google of its trillion-dollar moat. Venture capitalists salivated. Tech journalists penned obituaries. Marketing leaders panicked about where to reallocate their budgets.

None of it happened.

What we are witnessing is the emergence of a complementary consumer journey. Google absorbed the threat and weaponized its infrastructure advantages, and posted its first-ever $100 billion revenue quarter in late 2025. Meanwhile, OpenAI created a parallel way for consumers to engage, reaching 700 million weekly active users by September 2025 (expected to exceed 800 million by the year’s end) and fundamentally altering how people interact with information online.

According to analysis by Eric Seufert of Similarweb data, there is a significant asymmetry in platform adoption. 95% of ChatGPT users (orange, below) visited Google (blue, below), while only 14% of Google users visited ChatGPT:

The question for marketing leaders entering 2026 is no longer “Which platform wins?” It’s “How do we operate effectively in a world governed by two empires with fundamentally different economics, user behaviors, and strategic advantages?”

How Has Google Survived the AI Threat?

Google’s resilience was structural. While competitors like Microsoft and a parade of well-funded startups scrambled to bolt generative AI onto their products using third-party GPUs (with all the costs that entails), Google leveraged something far more valuable: vertical integration. Their custom Tensor Processing Units (TPUs) allowed them to slash AI inference costs at scale, maintaining profitability while others burned cash trying to keep pace.

As a result, prediction markets that had given Google just a 7% chance of having the superior AI model a year ago now place those odds at 96%. This demonstrates how deeply entrenched infrastructure advantages compound over time. Google didn’t need to be first to generative AI. Google needed to be efficient and sustain the economics of running these models at planetary scale.

But here’s where the story gets interesting: OpenAI didn’t lose. ChatGPT’s growth trajectory from 400 million to 800 million weekly active users in under ten months represents one of the fastest consumer adoption curves in digital history. More importantly, the nature of that usage fundamentally differs from traditional search. The average ChatGPT session lasts 14 minutes and 9 seconds, compared to just 5 minutes and 12 seconds on Google. Users are staying, working, and iterating. ChatGPT has evolved from a novelty into something closer to an operating system for thought work.

We’re not watching a zero-sum competition.

Google vs. ChatGPT: Transactional Intent vs. Consideration

User behavior has split into two distinct utilities. Marketing leaders must understand the specific role of each.

Google remains the undisputed king of transactional intent. With nearly 90% of global search market share, it’s still the venue where high-velocity commercial queries get resolved. If someone searches “emergency plumber near me” or “low cost auto insurance,” Google is where that transaction happens. This is not changing in 2026.

OpenAI’s advantages are inverse. They own attention. That 14-minute average session signals deep engagement. Users arrive with complex problems and iterate through solutions. This is where brand authority gets built, where consideration happens, where the messy middle of the customer journey actually unfolds.

But OpenAI has a glaring vulnerability: unproven monetization. ChatGPT’s advertising product is nascent compared to Google’s 20-year-old, algorithmically optimized cash machine. OpenAI projects $1 billion in ad revenue for 2026, scaling to $25 billion by 2029. Those are impressive hockey-stick projections, but they’re mandatory ones. The company must rapidly operationalize this revenue to service the staggering debt load and compute costs they’ve accumulated to fuel their growth. The ad product is expected to launch broadly in Q1 or Q2 of 2026. Nobody knows what formats will look like, how targeting will work, or whether OpenAI can thread the needle between user experience and advertiser demand.

The 2026 Playbook: Strategic Imperatives for Marketing Leaders

For pragmatic marketing leaders, the 2026 landscape requires moving beyond hype to execute on three specific strategic shifts: adopting Generative Engine Optimization (GEO), strictly managing AI ad campaigns, and preparing for the ChatGPT ad ecosystem.

1. Optimize for LLMs with Generative Engine Optimization (GEO)

The Core Shift: Transition from keyword stuffing to becoming a cited “source of truth.”

The SEO playbook of 2010–2023 remains foundational but insufficient. WIth roughly 60% of Google queries now ending with a click, marketing leaders must integrate Generative Engine Optimization (GEO) into their organic strategy to ensure brand visibility within the conversational journeys now occurring on AI platforms.

• Structure for Ingestion: Content must be formatted with direct answers, clear hierarchical headings, and structured data tables that LLMs can easily parse.
• Optimization Goal: The objective now includes securing citations and mentions in AI-generated answers.. Brands must provide authoritative data, unique insights, and 3rd-party validation that models prioritize as factual evidence.

2. Test New AI-Ready Ad Campaigns (with Strict Guardrails)

The Core Shift: Leverage Google’s AI automation without ceding financial control.

Do not abandon Google; it remains the world’s most sophisticated ad delivery system, particularly when measuring incrementality. However, budgets should shift toward testing AI-driven formats, such as Google’s AI Max, to capture high-intent demand.

Success requires a rigorous, hands-on approach rather than “set it and forget it” automation. To prevent algorithmic campaigns from wasting capital on irrelevant placements or bot traffic, you must implement strict guardrails:

• Precise Audience Data: Feed the algorithm high-fidelity first-party data.
• Deliberate Bid Strategies: Set caps to control cost-per-acquisition, and select the appropriate KPI to optimize towards.
• Negative Keyword Hygiene: Aggressively filter irrelevant terms to maintain traffic quality.

3. Establish a Sandbox Budget for ChatGPT Ads

The Core Shift: Prepare for early-adopter arbitrage in conversational advertising.

With OpenAI launching ad-supported tiers in the first half of 2026, brands need a dedicated sandbox budget allocated immediately. The goal is to test these formats the moment they become available.

• The Arbitrage Opportunity: Early inventory is historically underpriced because advertiser demand lags behind user supply.
• The Targeting Paradigm: OpenAI will likely leverage deep contextual data to target users based on conversation history and active thought processes rather than static keywords. Brands that test early will master this intent-based targeting before the auction marketplace matures.

The post Google vs. OpenAI: The Myth of Fragmentation and the Reality of 2026 appeared first on Silverback Strategies.