Six months. That’s how long a SaaS founder spent marketing his project management tool to freelance designers.
He’d built it to scratch his own itch—he was a freelance designer drowning in client chaos—so the targeting felt obvious. He posted in design communities. He used design terminology. He led with the product’s beautiful UI because that’s what designers care about. And the response was electric. Comments praising the interface. Shares across design forums. The kind of engagement metrics that make a marketing dashboard glow green.
Signups: almost zero. Paid conversions: functionally none.
His audience loved him. They just had no intention of buying from him.
That gap—between the audience that engages and the audience that converts—is the most expensive blind spot in marketing. And it’s hiding inside nearly every buyer persona document ever created.
The persona problem isn't bad research. It's the wrong subject.
I’ve lost count of how many persona documents clients have handed me over the years. They arrive in beautifully designed PDFs, complete with stock photos of people who don’t exist, alliterative names (“Marketing Mary,” “Founder Frank,” “Strategic Sarah”), and sections filled with information that could not have come from research because the client didn’t do any research.
You can always tell which sections were guessed. They have a particular texture—vague enough to sound plausible, specific enough to feel like someone did the work. “Reads industry blogs during her morning commute.” “Values work-life balance.” “Prefers email over phone calls.” It reads like a horoscope: true of basically everyone, predictive of basically nothing.
I’m not guessing about this. The Buyer Persona Institute’s Adele Revella—the person who literally founded the field—put it bluntly: “If you do a quick Google search for examples of personas, you will see profiles that include all kinds of generic information that serves no purpose to anyone, like education level, annual income, and hobbies. How does a persona with insights like ‘has a bachelor’s degree and wears multiple hats’ help you sell a comprehensive B2B solution?”
It doesn’t. And everybody knows it doesn’t. But the template has a field for it, so somebody fills it in.
A 2016 benchmark study of 137 B2B organisations found that 70% of companies who missed their revenue goals hadn’t conducted qualitative persona interviews. They built their personas from internal brainstorming—marketers and salespeople in a room, guessing. LinkedIn’s own marketing division called this “thin data: supplied by a third party and static, which means the personas are outdated even before they’re created.”
The result is a document that describes the public performance of your customer—the version who answers surveys thoughtfully, behaves rationally in focus groups, and presents a tidy professional identity to anyone who asks. The version who fills out your form.
The version who clicks “buy” is someone else entirely.
The buying self vs. the performing self
Here’s a test. Prince Charles and Ozzy Osbourne share identical demographics: born 1948, British, male, married twice, wealthy, live in large estates. By every standard persona data point, they’re the same buyer.
They are obviously, comically, not the same buyer.
Demographics describe the container. They tell you nothing about what’s inside—the beliefs, the fears, the aspirational identity, the private logic that determines whether someone reaches for their wallet or closes the tab.
The buying version of your customer is the person at 11pm wondering if they’re falling behind their peers. The one who won’t admit in a focus group that they chose your competitor last time because the sales rep made them feel like a bigger company than they actually are. The one whose real objection isn’t “I need to check with my team”—it’s “I don’t believe this will work for someone like me.”
Your persona captured their job title and their pain points. It missed the belief system that governs whether they ever convert.
And here’s what I find telling: when I build customer profiles using AI—prompting it with actual behavioural data, purchase history, support tickets, the language customers use in their own words—the output consistently surfaces layers of insight that the human-created persona never touches. Not because AI is smarter. Because the human who built the original persona was filling in a template, and templates reward completeness over depth. They ask “what does this person read?” instead of “what does this person believe about themselves that prevents them from buying?”
The AI doesn’t care about filling in every field. It follows the data into uncomfortable territory—the fears, the status anxiety, the aspirational gaps that no one puts on a slide deck.
The data keeps proving the gap exists
Stanley, the thermos company, spent 110 years marketing to industrial workers and outdoor tradespeople. That was their persona. Rugged. Masculine. Blue-collar.
Then around 2020, their sales data showed something their persona couldn’t explain. The Quencher tumbler—limping along since its 2016 launch within the traditional audience—was exploding among young women and lifestyle creators on TikTok. A demographic that appeared nowhere in Stanley’s documentation.
They pivoted. New colours, new finishes, influencer collaborations with the actual buying audience. Revenue went from $70 million in 2019 to $750 million by 2023. The persona said “construction worker.” The wallet said “lifestyle consumer.” The wallet was right.
AudienceView, an enterprise event ticketing platform, ran their paid campaigns against a carefully defined ideal customer profile on LinkedIn. Engagement looked healthy. Then they consolidated their data and discovered 10% of their target accounts were consuming 90% of their ad budget. The algorithm had found a pocket of enthusiastic clickers—junior researchers who engage with everything but sign nothing—and was burning nearly the entire spend on them. The actual decision-makers were barely seeing the ads.
Ten percent of the audience. Ninety percent of the budget. And every platform report said the campaigns were performing.
"But we've done our customer research"
I know. You’ve spent money on this. Maybe serious money. You have documents with names and demographics and pain points and buying triggers, and I’ve just spent 800 words telling you they’re incomplete.
So let me be specific about what they’re missing—because the problem isn’t that your research is wrong. It’s that it captured one layer of a three-layer problem, and the missing layers are the ones that determine whether money changes hands.
Layer one is who they are on paper. Demographics, firmographics, job title, company size. This is what most personas capture well. It’s also what matters least. Two companies with identical headcounts and industry codes can have completely different buying behaviours. The Prince Charles problem applies to B2B just as brutally.
Layer two is what they actually do before buying. Not what they say they do—what the data shows. The specific sequence of pages visited, the content consumed, the moment they shift from browsing to researching pricing. This layer is invisible unless your ad platform, website analytics, and CRM talk to each other. If you’re running separate agencies for each channel—one for Google, one for Meta, one for email—those systems almost certainly don’t connect. Each team reports on their own slice. Nobody sees the full behavioural picture.
Layer three is what they believe. About themselves, their situation, and solutions like yours. This is the private layer that never surfaces in surveys because people can’t articulate it—and often don’t recognise it. It’s the freelance designer who genuinely thinks your tool looks great but also believes, without conscious awareness, that project management software is for “real businesses” and not for solo operators. No feature list overcomes a belief the customer can’t name.
Here’s the structural issue: standard persona templates are almost entirely layer one, with a thin coat of layer two. Layer three doesn’t have a field in the template. So it doesn’t get captured. And that’s the layer where the purchase decision actually lives.
Konrad Sanders of The Creative Copywriter shared a persona he encountered for a B2B cloud storage company. It opened with: “Every morning at 7am, Chris (42M, IT Manager) drags himself from the bed he shares with Grace, his wife of 14 years, shuffles down the stairs to the kitchen, eats his muesli alone…” It went on to describe Chris’s marriage problems, his secret Meghan Markle obsession, and his teenage son’s headphone habits. Sanders’ assessment: “Not an unfinished novel. Not American Beauty fanfiction. A buyer persona.”
That’s what happens when you have a template with empty fields and no behavioural data to fill them with. Somebody writes fiction. And then an entire marketing strategy gets built on top of it.
What the mismatch actually costs
Nexford University learned this the expensive way. They generated massive lead volume for their online MBA programs—numbers that made quarterly reviews look great. But when they finally connected their marketing data to their admissions criteria, they discovered a huge percentage of those leads were fundamentally ineligible to enrol. Slight credential mismatches that meant the lead could never convert, regardless of how good the nurture sequence was.
The ad algorithms had been trained on “who fills out the form” not “who can actually become a student.” After connecting the data and feeding eligibility criteria back into targeting, their lead-to-customer conversion rate jumped 400%. Enrolments rose 205% in a year. Acquisition costs dropped 20%.
Not because they changed their copy. Because they changed who they were talking to.
A fitness brand hit the same wall from the opposite direction. Every campaign targeted young adults—the demographic that looked like their customer. When they consolidated sales data, the actual buyers were middle-aged professionals with higher disposable income and a deeper commitment to long-term health. Shifting the targeting to match reality drove a 50% increase in total sales.
These aren’t edge cases. This is what the research predicts. Chapman and Milham demonstrated mathematically that as you add attributes to make a persona more specific, the number of real people matching that description approaches zero—the “granularity problem.” You’re not narrowing your audience. You’re inventing a fictional character and then wondering why real people don’t behave like them.
How to catch the mismatch before it catches you
You don’t need to burn your persona docs. You need to pressure-test them against reality.
Start with one question: does the profile of the person your ads reach match the profile of the person who actually pays? Pull your top-of-funnel audience data and your closed-won customer data into the same view. If you can’t do that—if those datasets live in different tools managed by different teams with no connection between them—that’s your first and most urgent problem.
Check for the AudienceView trap. Look at whether a small subset of your audience is consuming a disproportionate share of your ad spend. Algorithms chase engagement. Engagement doesn’t equal intent. The people who click most aren’t necessarily the people who buy.
Audit your persona for guesswork. Go section by section. For each data point, ask: did this come from actual customer behaviour, or did someone fill it in because the template required it? If you can’t trace a data point to a real source, it’s fiction. And fiction is driving your targeting.
Connect one data loop. You don’t need a $200K data warehouse to start. Feed your CRM conversion data back into one ad platform. Just one. When the algorithm sees who actually converts downstream—not who clicks, not who downloads, but who pays—it recalibrates in ways that no amount of manual persona tweaking can match.
Test layer three with five customers. Pick your five most recent buyers and ask: what did they believe about themselves, their situation, or your category before they bought? Not what problem they had—what they held to be true. If you can’t answer that, your marketing is addressing a character that may not exist.
Not sure where your mismatch is hiding? [That’s what the Agency Waste Audit finds →]
The SaaS founder's three weeks
Remember the designer-turned-founder? The one who spent six months marketing to freelance designers who loved his product and never bought it?
When he stopped looking at engagement and started looking at the handful of people who’d actually converted and stayed, he found them. They weren’t freelancers at all. They were owners of small creative agencies—teams of three to eight people—who didn’t care about the UI aesthetics. They needed client portals and fast onboarding for new hires. Functional concerns. Operational pain.
The buying version of his customer had completely different priorities than the persona version. He rewrote his messaging. Changed his targeting. Spoke to the person who actually bought instead of the person he’d assumed would buy.
Three weeks generated more signups than the previous six months combined.
The person who fills out your survey and the person who clicks “buy” are not always the same human. The sooner you connect the data to find out, the sooner you stop pouring budget into a character someone invented to fill in a template—and start reaching the buyer who’s been there all along, waiting to be spoken to directly.
See who’s actually buying (and who’s just clicking)
Map your ad spend against your actual conversion data — and find out whether your budget is reaching buyers or feeding the algorithm’s favourite clickers.
Most clients find their ads are optimised for engagement, not revenue — because nobody connected the front end to the back end. Takes 30 minutes.
You don’t need better personas. You need to stop marketing to a character someone invented to fill in a template.