That’s changed. 

“The job of paid media managers really changed from trying to control all of the levers to more focusing on what are the signals I’m sending the platform so it can learn what’s a profitable customer for me and then what are the guardrails that I’m setting.”

It’s tempting to hear “fewer levers” and assume the job got easier. It didn’t. “Maybe fewer levers, but those levers matter more.” The platforms make far more decisions now. What’s left for a human to decide is what to tell the machine before it starts pulling them on its own. 

Skip that step, and the machine doesn’t pause to ask. It just runs.

What Happens When AI Controls Your Ad Creative Without Explicit Settings

The clearest example of what goes wrong sits in creative, an area that seems like it should be the easiest thing to hand over to AI.

“Meta has like 32 settings now that are all related to AI content,” Jordan says, “and you have to set them at the ad level.” The detail that matters most: many aren’t opt-in. They’re set automatically.

What does opting in by inaction look like? “Meta can just put a random song on your ad, and I’ve seen it happen. Rap music with a lot of profanity, for example, on an ad from a super polished brand that would never approve that.”

The platform did exactly what it was configured to do. That’s the point. The configuration is the decision, whether or not anyone consciously made it. 

Why AI-Driven Campaigns Attract Bot Traffic and What It Costs You

If creative automation is the visible risk (the one that shows up as an embarrassing ad, for example), there’s a quieter risk underneath it, in what platforms are told to optimize toward.

“This bot traffic has gotten even worse with AI,” Jordan says. Conversion types like click-to-calls and lead form fills are “really susceptible to bots,” and if a campaign optimizes toward raw lead volume without distinguishing real prospects from bot noise, the platform delivers exactly what it’s told to value. More leads. More clicks. More calls. None of it is necessarily real.

This connects to where ads actually run. Jordan flags Google Display Network, Google Search Network, and Facebook Audience Network as placements that tend to be “bot-heavy,” places where monitoring and explicit settings matter more than a top-line number suggests.

The throughline: AI systems are extremely good at optimizing toward whatever signal they’re given. They can’t ask whether that signal is the right one. That’s still a human job.

How Machine Learning Actually Learns (and What It Needs From You)

There’s a second layer here, and it’s counterintuitive for media managers trained in the “more control, more segmentation” era.

Since iOS tracking changes limited how precisely platforms can target individuals, Meta leans harder on pattern recognition based on behavior and creative response. 

Jordan’s example: “Jordan is active on Reels at 9pm, and spends a lot of time watching videos about dogs. Jordan behaves very similarly to these thousands of other people in the U.S.” The platform builds a behavioral fingerprint and matches ads to people with similar patterns. So,  creative becomes the primary lever, since granular audience targeting is largely gone.

But this kind of pattern recognition needs volume. “For Meta to be good at that, it has to have enough data to build a good pattern.” 

The thresholds: Meta needs roughly 50 conversions a week at the ad set level to exit “learning mode” and perform consistently. Google’s threshold is lower, but the same dynamic exists: roughly 30 conversions over 30 days.

This is where the old instinct toward control can become a liability. A media manager trained to segment heavily — separate campaigns for separate audiences, separate ad sets for separate offers — can end up with ad sets that never hit enough conversions to exit learning mode. 

Each segment might make perfect strategic sense alone. But if no segment can hit 50 conversions a week, none of them get the chance to perform the way the algorithm is designed to perform once it’s learned.

The job now: “finding the middle ground between having enough control over your budget and where you want to spend, but also giving the platform enough data to learn from.” 

Too much segmentation starves the system. Too little structure hands over decisions that should stay with a human. The guardrails sit in that middle ground.

The Paid Media AI Guardrail Checklist: Five Settings Most Accounts Get Wrong

A consistent set of things to check because each is so often left on a default nobody chose deliberately.

Brand Exclusions

Performance Max, left at default, “will automatically serve both branded and non-branded terms,” and because branded terms tend to be the cheapest to win, PMax gravitates toward them fast.

Roughly 90% of accounts audited don’t have this addressed, meaning a campaign meant to reach new prospects often functions as a branded search campaign in disguise.

Platform Optimization Targets

An estimated 80-90% of accounts optimize toward leads and phone calls without feeding back which converted further down the funnel, or which were spam. 

Without that feedback, the platform can’t tell a real prospect from a bot and has no reason to try.

Where Ads Run

Display Network, Search Network, and Audience Network placements carry higher bot exposure. These placements need active monitoring and explicit settings. 

Left on default, the platform decides where to spend.

Tracking Infrastructure

With up to 50% of Americans using ad blockers that block pixel-based tracking, server-side tracking — a direct connection between the ad platform and a business’s server, bypassing the browser — recovers data that would otherwise be lost. 

Without it, the platform decides based on an increasingly incomplete picture.

Campaign Structure & Learning Thresholds

Instead of maximizing segmentation, build structure around whether each ad set can realistically hit the conversion volume (50/week for Meta, 30/30 days for Google) needed to exit learning mode and perform as designed.

None of these are complicated on their own. What makes them matter: almost none of them are decisions a platform will surface or flag. 

They’re settings, and settings, left untouched, are still choices. Nobody made them on purpose.

What Good Paid Media Management Looks Like in an AI-First World

If the work shifted from pulling levers to setting guardrails and reading signals, what makes someone good at the job shifted, too.

“Previously a successful media manager used to over-index on detail-orientation, because a lot of the job was about control,” Jordan says. “Now we’re really screening for big-picture thinking, connecting what’s happening in a campaign to what it means for the business.”

Client communication matters more, not less, as automation increases. “At the end of the day, businesses are going to need someone to hold accountable, someone to make decisions, and communicate those decisions well.” As more execution becomes invisible, someone still has to explain what’s happening and why.

Deductive reasoning and structured diagnosis matter too: “These AI tools are probably going to spit out thousands of suggestions, but knowing what to do with that is going to be a big one.” The volume of output isn’t the bottleneck anymore. Judgment about what matters is.

And creative strategy and customer research (understanding who the audience is and how to reach them) is “more and more an important job of the media manager,” precisely because creative is now the primary lever left for influencing how these systems perform. The data backs this up — see what $1.3 billion in Meta ad spend reveals about creative performance.

The Bottom Line on AI-Driven Paid Media

This isn’t an argument against AI in paid media. These systems perform well when given what they need. The argument is narrower, and more urgent: these systems need to be given something. Signals. Exclusions. Volume thresholds. Guardrails. 

None of that happens automatically, no matter how automated the rest of the campaign looks.

“Automated” describes what happens once the inputs are set. It says nothing about whether those inputs were ever chosen on purpose. The businesses getting hurt by AI-driven paid media right now usually aren’t the ones using AI badly. 

They’re the ones who never realized there was a decision to make in the first place.

The post Higher Ad Costs, Slower Growth, Wrong Leads: What Happens When You Let Paid Media AI Run Without Guardrails appeared first on Silverback Strategies.

On one side: the marketing reports. Cost per lead, flat or improving. ROAS, climbing. By every metric the marketing team’s been trained to report on, things are working. 

On the other side: the P&L. Revenue, flat or sliding.

“Our business is either flatlining or maybe even declining a little bit, whereas our board expects us to be growing at a 15% year-over-year clip,” Jordan says, describing a version of this conversation she’s had more times than she can count. “And I, as the client, am frustrated and also confused because look at the data the ad platforms are saying. It says CPL is either flat or even getting a little better. ROAS is up.”

This isn’t a story about bad data. The platform numbers are accurate, in a narrow sense. The problem is what those numbers are measuring and what they’re not.

Why Summarizing Data Can Make Real Problems Disappear

It’s not that anyone’s avoiding the data. The team in this scenario is looking at their numbers. Regularly, carefully, in good faith. The problem is what those numbers are doing before anyone sees them.

Every report is a rollup. Daily numbers roll into weekly, weekly into monthly, channel-level into account-level, account-level into the topline summary that lands on an executive’s desk. 

Each layer of rollup is a layer of averaging, and averaging is exactly the operation that can make a real problem disappear.

A decline in one specific, important slice of the business can be small enough, relative to the whole, that it simply doesn’t move the aggregate number. The aggregate looks fine. The report says things are fine. And the slice that’s actually in trouble never gets its own line because nobody asked for one.

This is different from the goal-setting and attribution issues that create the initial gap between platform metrics and business reality. This is what happens after that gap exists: once a misleading number is baked into the reporting, the normal process of summarizing data for an executive audience doesn’t surface it. 

If anything, summarizing makes it less visible because summarizing is, by design, the process of making smaller things invisible inside bigger things.

The result is a report that isn’t lying, exactly. It’s just operating at a level of resolution where the thing that matters most doesn’t show up.

An Example: How Healthy Aggregate Numbers Can Hide a Declining Business

Let’s dive into a real client example.

Organic search performance — in aggregate — looked steady, even improving. Most teams would read that as reassuring. But aggregate organic is a blend of branded and non-branded traffic. And here, the company had built up non-branded organic visibility.

That growth was masking the fact that organic branded search clicks (people searching for the company by name, no ad involved) were “rapidly declining.” 

Nobody was looking at that number in isolation, because the aggregate looked healthy.

The business had a strong brand and great reviews. People were searching for it by name. But paid spend kept growing in branded search and Performance Max, so increasingly, the business was paying for those searches instead of capturing them for free. 

The decline in free organic brand visibility was the signal, hidden inside a number that looked fine.

Why the Warning Sign Came From Outside the Marketing Function

The most telling detail in this story is who raised the alarm.

By the time Jordan’s team got involved, the business had already let go of its in-house marketer. For six months, the reports told a consistent story: paid media performing well, path to growth is more budget. The business followed that recommendation, but revenue didn’t follow the increase in spend.

It was the company’s private equity firm — looking at the business from the outside, focused on the P&L — that called it. And, Jordan says, “They were right that that budget wasn’t being spent in the right way.”

This is the core of why this matters: the gap between “marketing looks profitable” and “revenue disagrees” can be completely invisible from inside the marketing function. 

Every number that function watches can look fine at the same time the business is struggling.

How to Test a Budget Hypothesis Without Risking the Whole Budget

Jordan’s team had a clear hypothesis: too much budget flowing to channels capturing existing demand instead of creating new demand. But moving a large share of a client’s budget on a theory carries real risk if you’re wrong.

So before changing anything, they tested it. Split the business’s markets into two groups. In one, Performance Max spend stayed put. In the other, it was turned down. Nothing else in the marketing mix changed during the window. Then they watched revenue in each group, focused on measuring the real-world outcome.

The result confirmed the hypothesis on both fronts. Revenue in the reduced-spend markets didn’t decline, which meant that spend hadn’t been generating revenue that wouldn’t have come in anyway. 

And organic brand search activity, tracked through GA4 and Google Search Console, picked back up in those same markets, just as predicted: when paid stopped capturing those branded searches, the free version of those same searches reappeared.

What Happened Next: 14% Increase in Revenue

With the test confirming the diagnosis, Jordan’s team reallocated roughly a third of the client’s budget out of Performance Max and branded search and into Meta and YouTube, channels better positioned to reach people who weren’t already looking for the business by name.

The result: media spend stayed flat. The business didn’t need to spend more to fix this. The money was already there, just pointed at the wrong thing. 

Top-line revenue increased 14% year-over-year.

Read the Full Case Study

The Question Every Marketing Budget Should Be Able to Answer

This isn’t “Performance Max is bad” or “branded search is wasted spend.” Both are too simple, and the right multiplier for a channel depends entirely on the business. Sometimes branded search really is driving incremental revenue, and defunding it would be a mistake.

The real question is underneath the whole story: is this spend creating revenue the business wouldn’t otherwise have, or is it capturing revenue that was coming in regardless? 

Platform-level reporting can’t answer that. It can only tell you whether the platform thinks it deserves credit. 

Answering the real question means looking somewhere the platform can’t see: the business’s own revenue, measured against itself, with and without the spend in question.

When marketing looks profitable and revenue disagrees, revenue is usually right. The work is figuring out why the marketing numbers didn’t see it coming, and increasingly, that work is testable. Not something a business has to hope someone notices before it’s too late.

The post Your Marketing Looks Profitable. Your Revenue Disagrees. Here’s Why. appeared first on Silverback Strategies.

The problem: “working” and “working according to ad platforms” aren’t the same thing. Jordan Crawford has seen this play out across enough accounts to know that a channel can look efficient on every metric a team is tracking and still be the wrong place for the next dollar. Money keeps flowing where it’s always flowed because nobody’s stopped to ask.

Jordan has also seen what it takes to ask that question and why most teams don’t, even when they suspect the answer might not be what they expect. 

“A lot of [the CMOs we work with] have had 10 plus years of running budgets a certain way, with real results to point to.” The challenge is showing them a better way to see something they’ve been looking at for a decade before the gap between “reported as working” and “actually working” gets expensive.

The Real Obstacle to Better Budget Decisions Isn’t the Math

When Jordan thinks about what stands between a marketing team and a data-backed budget, the technical pieces (incrementality benchmarks, testing frameworks, marketing mix models) aren’t where she starts.

“I think there’s an education element to it,” Jordan says. “It’s a different way of looking at things.”

She’s watched media managers leave agencies entirely rather than accept that the platform reporting they’ve relied on for their entire career might be misleading. “A lot of marketers have come up in this digital marketing environment, and if you’re told, hey, no, the platform’s lying to you, it’s a big shift.”

That education gap runs both ways. The marketing team needs to understand why the old framework misses the full picture. And the CEO and CFO need to be brought into that same understanding because the budget conversation that follows won’t land otherwise.

The second obstacle is more concrete, but Jordan calls it “a close number two”: even with agreement on the new approach, the data infrastructure often isn’t ready. 

“Your CRM has to be set up in a certain way to be able to pull the data that you need,” Jordan says, and she’s seen this hit hard in industries like senior living or higher education, where CRM systems “are not built to easily pull data out and share with the marketing team.”

Education comes first, not because the technical work doesn’t matter, but because without it, the technical work has nowhere to land.

How to Audit Where Your Marketing Budget Is Actually Going

Once a team is ready to look at their budget differently, start with an audit. It breaks into three layers.

Settings

What are the channels actually configured to do? 

Performance Max is the clearest example. 

“Out of the box, if you turn on PMax, it will automatically serve both branded and non-branded terms,” Jordan says, and because PMax is built to find the cheapest path to its goal, it gravitates toward branded terms fast, since they’re usually the cheapest to win. 

Without a brand exclusion, a campaign meant to reach new prospects can quietly become “a branded search campaign that’s hiding as a new prospecting campaign.” 

Jordan’s number on how often this goes unaddressed: “I would say, like, 90% of the sales audits we do, people don’t have that on.”

Signals

What data is being fed back to the platform to define success? 

Jordan estimates that 80-90% of the time, accounts optimize toward leads and phone calls without distinguishing real leads from noise. 

“If you’re not also feeding back information about how many of those leads were spam or how many turned into appointments or applications, the platform has no way to learn what good looks like.” And bot traffic, which has “gotten even worse with AI,” gets treated the same as a real prospect.

Tracking Infrastructure

Is the platform seeing all of its conversions?

With up to 50% of Americans using ad blockers, a huge share of activity never reaches the ad platform through normal tracking. 

Server-side tracking — “a direct line between the ad platform and your server,” bypassing the browser — recovers some of that lost data, giving platforms a fuller picture to optimize against.

How to Calculate What Your Business Can Actually Afford to Pay for a Customer

Underneath all three audit layers sits one question that’s almost always missing: what can this business actually afford to pay for a customer?

“I find that often we either don’t have a clear goal going in, or there’s a goal that nobody can explain where it came from,” Jordan says.

Here’s a real example from a retail entertainment brand offering public events and private parties. They didn’t have a target CAC. When asked, the honest answer was “I have no idea.” 

So Jordan’s team worked from the business’s own economics. They pulled the cost of goods and services for each event type, calculated it per seat (since that’s how customers buy), and looked at revenue per seat, including the food and drinks customers buy once they’re there.

That gave a break-even number: the most the business could ever pay to acquire a customer before losing money was $48. Apply the business’s target margin, roughly 50%, and the target CAC drops to $24.

This number becomes the anchor for everything that follows. Every channel, every campaign, every reallocation gets measured against it.

Why Ad Platform Numbers and Real Performance Numbers Almost Never Match And How to Find the Difference

Once you have a real target CAC, the next step is understanding what each channel is actually contributing.

This is where platform reporting breaks down most visibly. Ad platforms measure success by tracking clicks and attributing conversions to them. But not every conversion a platform takes credit for was actually caused by that channel. Some of those customers would have converted anyway. When a platform counts those conversions, it’s inflating its own results.

To find the true number, Jordan’s team uses incrementality testing: a structured way to measure what would have happened without a given channel, and compare it to what actually happened with it. The gap between those two outcomes is the channel’s real contribution.

The result of that testing is an incrementality multiplier—a ratio that adjusts what the platform reports down to what the channel actually drove. A multiplier of 20% means only 20% of the conversions a platform reported were genuinely caused by it; the other 80% would have happened regardless.

Here’s what that looks like in practice

A contribution report showed Google branded search drove 1,280 leads. Testing revealed the real number was 256—about 20% of what was reported. The other 1,024 leads, in Jordan’s words, “would have come in organically and converted anyway.”

Once CPL is recalculated against the true incremental number, the budget math often flips. A channel that looked efficient—and like an obvious place to add spend—can become the most expensive channel in the mix when measured against what it actually drove.

Incrementality multipliers come from two sources

Silverback maintains a database of thousands of incrementality tests—its own and from published industry research—and uses category averages as a starting baseline. Branded search, on average, comes in around 20% incremental.

But averages can mislead just as easily as platform numbers. Jordan has often seen branded search incrementality closer to 5%. For one client in a competitive DC-area market, however, testing showed branded search was 100% incremental—when they pulled back spend, they lost revenue that organic didn’t recover. 

“They lost a lot of money, and organic didn’t make it up. They needed that branded search budget.”

Every business gets its own number, built from its own test.

How Incrementality Testing Actually Works

Three approaches, ordered roughly by how much data you need.

1. Matched Market Test

The most rigorous. Using historical data, Silverback’s team finds pairs of markets that have historically moved together. For example, when Charlottesville, Virginia revenue goes up, Dayton, Ohio revenue also goes up.

Once enough pairs are identified, one group becomes a test, the other control. A change happens only in the test group — launching a new channel, scaling spend up or down, turning a channel off — while the control stays unchanged and nothing else shifts during the window. 

Attribution gets set aside completely. The only thing measured: how revenue moved differently between the two groups.

2. Directional Lift Test

Used when there’s not enough data for full statistical significance. For one Silverback client, Jordan’s team cut branded search paid spend in half and watched organic clicks. 

If organic rose by roughly the amount paid fell, that’s a strong signal, even without a full test, that the paid spend was largely redundant with organic traffic that would’ve happened regardless.

3. Media Mix Modeling (MMM)

A statistical model built from roughly two years of historical spend across all channels, including offline, and the business’s revenue. These models come from approaches open-sourced by companies like Uber, Google, and Facebook after years of internal data science investment. 

The output: a prediction of how revenue responds if spend in a given channel changes. MMMs work especially well alongside testing. Results can feed back into the model to improve it over time.

Learn More About Silverback’s Media Mix Modeling Approach

From Insight to Reallocation: What Happens After the Tests

The retail entertainment client example and the branded search multiplier example point to the same kind of decision: once a business knows its real target CAC and its real incremental cost per channel, budget moves stop being arguments about gut feel and start being simple math.

That doesn’t make the conversation easy. A CMO who’s spent ten years optimizing toward a platform’s reported ROAS needs real time to trust a multiplier-adjusted number instead. 

But once that trust is there, reallocation follows the numbers: cut what’s expensive once incrementality is accounted for, fund what testing shows is working, and keep testing as the business and competitors change.

This shift is about giving intuition something real to stand on: a target CAC grounded in the business’s own economics and multipliers grounded in what actually happened when the business tested its assumptions.

The post Which Dollars Are Actually Working Across Your Marketing Mix? Shifting Marketing Budget Allocation From Gut Feel to Data-Backed appeared first on Silverback Strategies.

The marketing team walks in with a report that looks like good news. Cost per lead is flat or improving. ROAS is climbing. Across the table, the board asks why revenue is flat (or even declining) when the plan called for 15% year-over-year growth.

Nobody’s lying. That’s what makes it so disorienting. 

“Basically, everything looks like it’s getting better, but the business is going in the opposite direction,” Jordan says. The marketing report and the business report are describing two different realities, and there’s rarely a translation layer between them. There’s no shared way to connect marketing attribution to actual marketing ROI. “It often feels like there’s the business reports, and then there’s the marketing reports, and they’re really not speaking to each other at all.”

Jordan has spent the last two years mapping exactly where that gap opens up and how to close it.

Why Marketing Goals Get Set to the Wrong Number

The first crack forms before any campaign ever launches, when goals get set.

Jordan describes a familiar pattern: a team looks at how their CPM has trended and decides that’s a good number to hold steady. Add more spend, keep the CPM where it is, growth follows.

Except that number was never connected to the business. It’s an artifact of the platform, not a measure of profit. Manage toward it, and every report will look successful by its own, built-in logic.

The fix starts by working backward from the business itself. Take a higher-ed client, though the same logic applies across industries. 

How to Set a Media Goal That’s Tied to the Business

1. Start with the real unit economics. What does it cost to deliver your product or service? What margin does the business need to sustain itself? These numbers live in the P&L, not the media platform.
2. Calculate what the business can actually afford to pay to acquire a customer. For this higher-ed client, that number was roughly $6,000 per enrolled student. This is the ceiling. Every media decision has to fit inside it.
3. Map the full funnel and timeline. How long does it take from first contact to conversion? What does the drop-off look like at each stage? You can’t set a lead goal without understanding how leads move through the funnel.
4. Translate the business goal into a media-actionable target. The $6,000 CAC ceiling becomes a per-lead value only when you know your conversion rates. In this case, that worked out to roughly $160 for a lead showing real signs of enrolling. That’s the number the media team can actually optimize toward.

This work has to happen before any platform metric means anything. Skip it, and a team optimizes toward a number that was never tied to the business in the first place.

Why Click-Based Attribution Misses the Channels That Actually Build Demand

The other half of the gap comes from how attribution works. It’s structural, not a simple matter of picking the “right” model.

“At the end of the day, all of the different attribution models are biased toward channels that require a click,” Jordan says, “because that’s the only way that attribution model would know that someone engaged with it.” 

Someone watches an entire YouTube video, absorbs the message, acts on it days later. “No one’s ever going to know.”

This is playing out in marketing departments across verticals. Budget moving out of TV, traditional radio, and events, concentrating into digital, and then concentrating further into branded search and Performance Max. 

On paper, this looks like optimization. Efficiency metrics improve. 

But Jordan is blunt about what’s happening: these channels “look really good from an efficiency standpoint, but they’re not helpful for reaching net new audiences.”

The channels capturing more budget are often the channels best at claiming credit for demand that already existed, not the channels creating new demand. And because the attribution model can only see the click, it can’t tell the difference.

Building the Translation Layer: How to Structure a Marketing Report the C-Suite Actually Trusts

To build a report that closes this gap, start by replacing the metrics.

“CPL, cost per conversion, and platform ROAS — if you deliver that to a CFO or CEO, they’re like, what does that mean?” Instead, Jordan points to CAC (customer acquisition cost) as the unit that translates. Specifically: CAC by audience segment or product segment versus your target CAC, and how both have moved when compared period over period.

That last part matters. Showing a CAC number is useful. Showing how CAC changed—and being able to explain why—is what earns trust. Did CAC go up because you expanded into harder-to-reach audiences? Did it go down because you cut spend that was duplicating organic traffic? The C-suite doesn’t need a data dump; they need the story behind the number.

“Marketers don’t just have those numbers at their fingertips,” Jordan says, “so that’s why there’s a lot of measurement setup needed to be able to do that.” 

Building the ability to translate marketing performance into C-suite reporting requires CRM data, defined economics, and often new tracking. But the payoff is a report that speaks the same language as the rest of the business.

From there, Jordan’s structure for an executive-ready report is simple: Start with the bottom line (growth, revenue) and how the team is pacing against it, regardless of whether that team’s channel can claim full credit. 

“Even if I only manage your paid media and I can’t fully impact your full business revenue, starting with that helps earn the respect of the C-suite,” Jordan says. “We establish that we care about the same metrics. We’re driving towards the same thing.”

Then narrow: CAC by target, broken out by business line if relevant. What changed period over period. And, most importantly, what that change points to next. 

Jordan’s framing is direct: “Trying to do that concisely in three slides is a lot more impactful than a 50-page report. Most executives just want to know what they should do next and what data supports that.”

How to Report on Channels That Can’t Be Directly Tied to Revenue

Not every activity has a clean connection to the business’s bottom line. Jordan’s approach doesn’t pretend that connection exists when it doesn’t.

For channels like SEO or content, where you can’t simply say, “We shifted budget here and revenue moved accordingly,” Jordan looks for a leading indicator: something measurable today that has historically correlated with revenue down the line.

One example: a client selling a small number of very high-value items each month. Too few sales for any meaningful close-rate analysis by channel. 

Instead of forcing attribution onto a process that can’t support it, Jordan’s team checked a range of metrics against revenue and close rate over time: branded search impressions, total leads, subscribers, marketing-qualified leads. The goal was to find something that moves before revenue does.

Branded search volume turned out to be the signal, but it wasn’t immediately visible. Facebook spend against branded search, week to week, showed nothing. Build in a two-week lag, and the two lined up clearly. 

Makes sense once you see it: someone browsing Facebook is often earlier in their journey. They engage with content, and only later do they search for the brand by name.

“If you just throw up branded impressions, they’d probably be like, I don’t care about this. This is a fluffy metric,” Jordan says. “But if you can prove out, hey, no, this really correlates to your bottom line — and there’s a lag time between when you do this activity and it hits — you can really start to build that momentum.”

A Real Example: What Happens When the Translation Layer Doesn’t Exist

Here’s what happens when this translation layer doesn’t exist.

A client came to Silverback after letting go of their in-house marketer. That marketer’s reports had told a consistent story for months: paid media performing well, platform ROAS strong, path forward simple — more budget. 

Over six months, the business followed that advice. Spend went up. Revenue did not.

When Jordan’s team dug in, the pattern was familiar: branded search and Performance Max, with much of the PMax spend itself going toward branded terms. 

Organic search looked fine in aggregate, even improving. But that number was hiding something. The company had built strong non-branded organic visibility, and that growth was masking a sharp decline in organic branded clicks specifically. 

The business had a strong brand and great reviews, meaning a growing share of paid spend was just paying for customers who’d have found the business anyway.

Jordan’s team had a hypothesis. But reallocating a large share of budget on a hypothesis alone is a real risk if it’s wrong. So before changing anything, they tested it: split the client’s markets in two, held Performance Max spend steady in half, turned it down in the other half. Then watched what happened to revenue in each group.

Revenue in the markets where PMax spend dropped didn’t move. Organic brand search activity picked back up in those markets, exactly as predicted.

With that confirmed, roughly a third of the client’s budget moved out of Performance Max and branded search, into Meta and YouTube. 

Media spend stayed flat. Top-line revenue increased 14% year-over-year.

[Read the Full Case Study]

What Marketing Leaders Should Do Next

Most marketing leaders already sense something’s off. 

“I think 80% know that attribution is wrong,” Jordan says, “but only 10% have a good solution for it.” The tools to close that gap (customer unit economics, media mix modeling, incrementality testing) are more accessible to mid-market businesses than they’ve ever been.

The translation layer is the difference between a marketing team that can say, “Here’s why this is working,” with evidence the rest of the business actually trusts, and one stuck explaining, every quarter, why the dashboard and the P&L don’t agree.

The post Translating Media Performance Data Into Something the C-Suite Can Actually Use appeared first on Silverback Strategies.

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.