Your ad data can't tell you everything about your audience

TL;DR

Ad platform data gives you demographic snapshots and performance metrics, but it can't tell you the whole story about your audience. Marketing personas with overly specific details aren't practical for media buying, even if they help creative teams remember they're marketing to real humans. The real insight? Stop over-segmenting based on small data samples. Creative does the heavy lifting in audience targeting now, and your best-performing segments might actually be the least incremental. Focus on broader reach, use your first-party data wisely, and break down those silos between departments. Everyone needs to share what they know about your audience.

Audience targeting in digital advertising is messy. You've got first-party data, third-party research tools, platform demographics, and those infamous marketing personas that seem to multiply in every brand deck. So let’s talk about audience targeting and what it can and should actually mean. 

The many faces of “audience”

Before we dive into what's broken, let's define what we're actually talking about. "Audience" means different things depending on who's in the room:

• Target audience (media buying): Who you're putting ads in front of
• Ideal customer profile: Your best customers
• Total addressable market: Everyone who could theoretically buy from you
• Existing customer segments: Who's already bought, broken down by behavior or value

These distinctions matter because a performance marketer optimizing for conversions and a brand strategist building awareness campaigns need completely different approaches to audience definition.

How brands actually define audiences

Most brands pull audience data from a patchwork of sources:

First-party data is limited to what customers voluntarily share — usually age ranges and gender, inferred rather than explicitly stated. B2B companies might collect job titles and company information, but even that doesn't paint a complete picture.

Market research tools like Kantar, Ipsos, or GWI survey general populations about interests, demographics, and purchase consideration. Media strategists use these to build hypotheses about who might want the product.

Ad platform data comes from what Google, Meta, and others know about their users (birthdays, genders, interests tied to accounts). These platforms offer demographic breakdowns of who's engaging with your ads, but remember: this data reflects who the algorithm found, not necessarily who your ideal customer is.

There's no single source of truth. Smart marketers synthesize signals from multiple places to build a working theory of their audience.

The marketing persona problem

Those detailed marketing personas that brands love? The ones where "Marketing Maggie is 33, loves riverside dog walks, and only buys software during the waxing crescent moon"? They're mostly useless for media buying. But we do see a purpose for them. 

Creative teams benefit from personas that help them craft differentiated messaging. A CRM product needs different creative for IT professionals worried about data security versus marketing professionals who need attribution insights. Personas remind teams they're marketing to real people with full lives, not just conversion metrics.

But when you try to translate "Marketing Maggie" into actual targeting parameters it falls apart. You can't build a media buying strategy around someone's dog-walking habits. Ad platforms don't offer that level of granularity, and even if they did, you'd be fragmenting your budget across audiences so small you couldn't learn anything meaningful.

The segmentation trap

Here's where brands get into trouble: seeing one demographic segment perform better in a small campaign and immediately pivoting the entire strategy to chase it.

You run $50K in ads, notice that women aged 25-34 converted at a higher rate, and suddenly all your targeting narrows to that group. But that sample size is tiny. Your first-party data might tell a completely different story. And worse, you're now ignoring where the majority of your revenue actually comes from.

The real problem? Small sample sizes leading to big strategic decisions.

Those "high-performing" segments might just be closest to purchase anyway. Ad platforms are sophisticated enough to find people ready to buy right now. That doesn't mean narrowing your targeting to only those people will make your campaigns more effective; it might actually make them worse by cutting off everyone else in your addressable market.

Creative often does the targeting now

There's a reason people say "creative is the new targeting." Ad platforms have gotten so good at optimization that when you're running bottom-of-funnel campaigns, they're already finding the in-market segments within whatever broad targeting you set.

Even if you target all women aged 25-50 in the US with no additional layering, the algorithm will naturally start serving ads to the people engaging with your creative. If your creative resonates with a specific sub-segment, the platform figures that out on its own.

This is especially true when you're optimizing for conversions or engagement rather than pure reach. The platform does implicit segmentation you're not even controlling.

When segmentation actually matters

Segmentation isn't completely dead, it just needs to match your goals and product reality.

When to segment:

• Your product genuinely serves different audiences (women's vs. men's retail, for example)
• You're B2B and need to reach specific decision-makers
• You have distinct product lines that appeal to different end users
• Your creative speaks to fundamentally different use cases or pain points

When not to segment:

• You're running awareness campaigns trying to build market share (you need reach, not precision)
• Your budget is too small to learn anything meaningful across multiple segments
• The segments you're considering have massive overlap anyway
• You're reacting to performance differences in tiny campaign samples
  
  

The overlap problem

Even when you think you're targeting different audiences, you're probably reaching mostly the same people. Set up two interest-based audiences in Meta, say, "fitness enthusiasts" and "health-conscious consumers", and the Venn diagram overlap is enormous.

So when you see performance differences between those audiences? That's not necessarily because one audience is better. It's because the algorithm optimized differently within each campaign, or because of dozens of hidden variables you're not seeing. You can't draw causal conclusions from that data.

What brands should do instead

Use your own behavioral data. Most brands underutilize what they're already collecting. Google Analytics shows you what content people consume on your site. Search Console reveals the actual queries people use when looking for solutions in your space. Customer reviews tell you how the market thinks about your product in their own words.

This behavioral data shows you how the market actually interacts with your brand, not just who they are demographically. It reveals how your messaging shifts perception over time. When you launch brand campaigns, you can watch keyword patterns change in Search Console.

Break down the silos. Marketing knows things sales doesn't. Sales hears objections marketing never sees. Customer service understands pain points that never make it back to the product team.

In-house analytics teams build complex models for sales to use, but marketing never gets access. Marketing creates lead scoring that ignores actual sales conversation data from tools like Gong. Everyone's optimizing their own piece of the customer journey without talking to each other.

Align incentives across teams. When marketing is measured on MQLs and sales is measured on close rates, neither team is actually responsible for revenue. Marketing can drive terrible leads that sales won't call. Sales can complain about lead quality without helping marketing understand why leads aren't closing.

Better approach: Put the entire customer journey under one umbrella where teams share responsibility for revenue, not just their individual metrics.

Stop changing your targeting every month. Constantly shifting audience parameters because you saw a tiny performance difference means you can never benchmark anything. You lose the ability to collect trend data over time. You're optimizing for noise, not signal.

Build stability into your targeting strategy so you can actually measure what's working across a meaningful time horizon.

The bottom line

Your ad data shows you patterns, not truth. It reflects algorithmic optimization, platform limitations, and sample sizes that are often too small to mean anything.

Most brands don't need hyper-granular audience segmentation. They need better creative that resonates, broader reach in their addressable market, and systems that let different teams share what they're learning about customers.

Stop chasing demographic details that don't translate to media buying. Start using the behavioral data you're already collecting. And remember: audiences aren't just marketing's problem, they're everyone's problem.