“Meet Marketing Mary. She’s 45 years old, married with two kids, lives in the suburbs, makes $95,000 a year, and loves shopping at Target.”
Depending on your marketing specialization, chances are you’ve encountered some version of Marketing Mary’s buyer persona. You might’ve even spent hours creating a slide deck for your boss complete with a generic stock photo, personality traits, and a list of her go-to hobbies and brands.
However, I think we’re at an interesting crossroads in both life and the industry. And look, I’m not here to tell you traditional buyer personas are useless. In fact, I’m a huge advocate for understanding your audience on a psychological level. I dedicated a good portion of my previous article, The Ambidextrous Method, to explaining why understanding what makes your customers tick is essential to successful performance creative.
But here’s where things get interesting.
Marketing Mary might check every demographic box on your ideal customer profile. She’s the right age, has the right income, and lives in the right neighborhood.
But what if she’s not actually in the market for your product?
Meanwhile, someone who falls completely outside your carefully crafted persona is actively searching for the exact solution your business offers.
Who would you rather spend your advertising dollars reaching?
I don’t know about you, but my money is on the person who’s actually ready to buy.
Welcome to the evolution of audience targeting, where understanding who your customers are is only half the equation. The other half is understanding what they’re trying to accomplish and how to translate those insights into actionable PPC signals.
From Demographics to Intent: Give Marketing Mary a Reality Check
Traditional buyer personas provide a valuable foundation for understanding customer needs, motivations, and purchasing behaviors.
The problem isn’t the persona itself. It’s what happens when we treat it like a fixed identity rather than a starting point.
Think about it.
Marketing Mary might be researching a new CRM platform for her company on Monday morning. By Friday evening, she’s comparing luxury hotels for an upcoming vacation.
Her demographics haven’t changed. Her intent has.
That’s why I like to think of traditional personas as a photograph and dynamic audiences as a moving picture.
One tells us who someone is. The other helps us understand what they’re doing, why they’re doing it, and what they might do next.
This is where the Jobs To Be Done (JTBD) framework becomes especially valuable. Rather than defining customers solely by demographic characteristics, JTBD focuses on the problem they’re trying to solve or the progress they’re trying to make.
For performance marketers, that distinction is everything.
Instead of simply asking, “How do we reach more women between 35 and 45?” we should also be asking:
- What problem is our audience actively trying to solve?
- What language are they using to research solutions?
- What behaviors suggest they’re moving closer to a purchase?
Because once we understand the motivation behind the customer, we can start translating those insights into something much more actionable.
Translating Audience Research Into PPC Signals
Here’s where the strategist in me gets excited.
You’ve conducted your audience research, identified customer pain points, and developed your personas.
Congratulations! You’ve done the foundational work.
But if all that research does is sit in a presentation deck collecting digital dust, what was the point?
The real value comes from translating those insights into signals our advertising platforms can use.
Let’s say your company sells a marketing software designed to help small businesses manage appointments.
Your traditional persona might describe a business owner who values efficiency, struggles with administrative tasks, and wants more time to focus on growing their company.
That’s useful information for developing messaging.
But what does it actually tell Google Ads?
Not much on its own.
Now consider what that same customer might be searching for:
- “Best scheduling software for small businesses”
- “How to automate client appointments”
- “Appointment scheduling software pricing”
Suddenly, we’ve gone from describing a person to identifying behaviors that suggest a specific need and level of purchasing intent.
And those insights can inform several components of your PPC strategy.
- Custom Segments: Translate relevant search themes, competitor research, and customer interests into audience inputs that help inform targeting in eligible Google Ads campaigns.
- First-Party Audience Clusters: Use appropriately collected CRM and website data to distinguish existing customers, qualified leads, and engaged prospects. These groups can inform Customer Match, remarketing, and customer lifecycle strategies where eligible.
- Conversion Signals: Look beyond surface-level actions. When possible, connect campaign performance to meaningful outcomes such as qualified leads, purchases, or revenue.
Putting It Into Practice
Let’s revisit our small-business owner who’s struggling to manage appointments.
Our research tells us she values efficiency. But how do we translate that insight into something actionable?
- Customer Motivation: Save time and reduce administrative work.
- Search Intent: Queries like “automated appointment scheduling” or “best booking software for small businesses.”
- Audience Signals: Custom segments informed by relevant search themes, alongside eligible first-party audiences built from website engagement or CRM data.
- Conversion Signals: Free trials, qualified demo requests, or subscriptions that indicate meaningful business value.
See what we did there?
Our persona is no longer just a description of who we’re trying to reach. It’s helping inform how we reach them, what we say, and how we measure success.
The goal isn’t to abandon traditional audience research. It’s to make that research more actionable.
Giving the Algorithm Better Direction
Let’s address the elephant in the room: automation.
As advertising platforms become increasingly reliant on AI and machine learning, marketers have less direct control over certain targeting and bidding decisions.
And depending on who you ask, that’s either the greatest advancement in digital advertising or the beginning of the end for traditional PPC management.
Personally, I think it’s an opportunity to become more strategic.
Take Performance Max, for example.
Google allows advertisers to provide audience signals using inputs such as customer lists, website visitors, and custom segments. These signals help guide machine learning toward potentially relevant audiences, although they aren’t strict targeting boundaries.
But here’s the thing.
Machine learning can identify patterns and optimize toward our campaign goals, but it’s still our responsibility to ensure those goals reflect meaningful business outcomes.
If every form submission is treated as equally valuable, automated bidding may prioritize generating more submissions, regardless of whether those leads ultimately become customers.
That’s why conversion quality, first-party data, and business context are so important.
Our job isn’t simply to give the algorithm more data. It’s to give it a more meaningful direction.
Audience research helps us understand who we’re trying to reach and why. First-party data helps us identify which customers create value. And conversion tracking helps communicate the outcomes we’re trying to achieve.
That’s the difference between relying on automation and building a strategy around it.
Your Audience Isn’t Static. Your Strategy Shouldn’t Be Either.
If there’s one thing I’ve learned working across different industries, it’s that the audience you expect to respond to your marketing isn’t always the audience that actually does.
And that’s not necessarily a bad thing.
Sometimes the most valuable insights emerge when the data challenges our original assumptions.
Maybe a keyword you initially considered secondary consistently generates qualified leads. Maybe a particular audience engages with your ads but rarely converts. Or maybe an unexpected customer segment turns out to be especially valuable.
These aren’t necessarily signs that your original research was wrong. They’re opportunities to make it better.
That’s why I believe audience development should be a continuous feedback loop:
Research → Translate → Activate → Validate → Refine
Start with your customer research and personas. Translate those insights into intent-based signals. Activate them through your campaigns, and then evaluate performance using platform data, GA4, CRM insights, and other available business intelligence.
What worked? What surprised you? And what can you learn from it?
Use those findings to refine your audience strategy, messaging, and conversion goals.
Then repeat.
Because your customers are constantly evolving, and your marketing strategy should be flexible enough to evolve with them.
The Competitive Advantage Is Still Human
The future of audience targeting isn’t about choosing between human insight and machine learning. It’s about understanding how to make them work together.
Traditional marketing research gives us context. Behavioral data helps us recognize intent. First-party intelligence connects those behaviors to business value. And advertising technology helps us activate those insights at scale.
Which brings me back to the same principle I explored in The Ambidextrous Method: successful performance marketing requires us to bridge the gap between human psychology and analytical execution.
Only this time, we’re not just translating audience insights into creative. We’re translating them into the signals that help power our campaigns.
So, by all means, keep Marketing Mary.
Give her a personality, understand her frustrations, and figure out what keeps her up at night.
Just don’t forget to ask what she’s searching for, what she’s trying to accomplish, and whether she’s actually ready to buy.
Because at the end of the day, the goal isn’t to build the prettiest persona deck in the company.
It’s to understand your customers well enough to reach them when it matters and turn that understanding into measurable business growth.
