Smarter segmentation: why we rewrote the rules on Persona sizing
Faraday has replaced traditional persona clustering with a dynamic scaling model that automatically adjusts the number of personas based on audience size, ensuring marketing segments are always granular enough to be actionable without becoming overly complex.



Faraday automatically groups your customers into distinct, actionable subgroups based on shared traits. To ensure your segments are highly useful, we dynamically scale the number of Personas based on your total audience size. This gives you the perfect level of granularity for personalized marketing.
In 2026, marketers know that a generic message is a campaign killer. Your buyers are unique. Blasting your entire list with the same offer won't win the deal anymore. In this context, the question is: how do you effectively segment a massive, messy audience into groups you can actually talk to? And the answer is generally by building customer Personas.
But how many Personas do you need? If you have a customer list of 1,000 people, organizing them into a few distinct personas makes sense. But what if your list is 100,000 people? Or 10 million?
If you are still squeezing a massive, diverse audience into those same basic Personas, you aren't personalizing at all. You are just generalizing at a slightly more granular scale.
The problem with traditional Persona clustering
Historically, many data scientists have used a technique called the “elbow method” to help decide how many personas (or clusters) to create. The method tests different numbers of groups—one, two, three, four, and so on—and measures how tightly each customer fits within each grouping. As you add more clusters, the fit improves, but the improvement slows over time; each additional cluster helps a little less than the one before it. The “elbow” is the point where adding another group no longer meaningfully improves the separation.
In theory, that elbow represents an optimal number of personas.
In practice, real customer data rarely produces a clean, obvious elbow. Human behavior overlaps. Traits blend together. Instead of a sharp bend in the curve, you often get a gradual flattening. When that happens, the algorithm generally defaults to a standard output: three clusters. And often, three is not what you need
The myth of the "true" persona
The elbow method breaks down because it is optimized to find a mathematically correct answer. It is hunting for what data scientists call “true clusters”.
But when our product team talks to clients, we often say: “In marketing there is no such thing as correct personas, only useful personas”.
For a mathematically true persona to exist, your audience data needs a massive, undeniable gap. For example, if your brand sells two entirely unrelated things—like skateboards for teenagers and mobility aids for seniors—your buyers will naturally split into two separate demographic groups. The math easily finds that gap. But if your groups are that distinct, you probably didn't need clustering tools to point them out to you at all.
For almost every other brand, human data is messy. Your customers have overlapping traits and nuanced behaviors. Because those massive gaps don't exist, the elbow method gets confused and tends to lump everyone into the default number of buckets: three. But often, three personas is not what you actually need.
Segmenting for usefulness, not perfection
To build personas that drive value, we realized we needed to stop chasing mathematical perfection and start optimizing for utility.
Instead of relying on an algorithm that forces arbitrary limits, Faraday has rewritten the rules. We now dynamically scale your persona count based directly on the size of your audience. Using a proportional sizing model, Faraday now assigns a persona count ranging from a minimum of 2 up to a maximum of 8 based on the volume of your cohort.
Here is why this changes the game for your campaigns:
- Granularity that scales with you: An audience of 1,000 yields 3 personas. 10,000 yields 4. 100,000 yields 5. Massive lists finally get the rich segmentation they deserve.
- Characteristics shine through: Because large audiences are no longer crammed into three tiny boxes, the unique traits that define your buyers become visible and actionable.
- No more micro-segments: Conversely, smaller cohorts will never be artificially chopped into too many tiny, useless personas. Your groupings stay distinct and easy to manage.
Marketing isn't about hitting an arbitrary number of segments. It's about finding the best way to speak directly to your customers' unique characteristics. With proportional personas, you have the exact right number of segments to make your next campaign your highest-converting one yet.
Ready to see your new segments? Log in to Faraday to explore your dynamically scaled persona sets today, or read the updated docs for more details on analyzing your audience.

Andrew Becker
Andrew is a Data Scientist at Faraday, best known for fixing models that are haunted by bias, leakage, or mysterious problems that only appear when someone important is watching. He rebuilds targets and architecture until things behave like math again. Trained in statistics and applied ML, he often converts one-off rescues into permanent platform upgrades. When not debugging reality, he restores vintage vehicles, maintains a farm and vineyard, and makes his own clothing for reasons no one fully understands.

Ben Rose
Ben Rose is a Growth Marketing Manager at Faraday, where he focuses on turning the company’s work with data and consumer behavior into clear stories and the systems that support them at scale. With a diverse background ranging from Theatrical and Architectural design to Art Direction, Ben brings a unique "design-thinking" approach to growth marketing. When he isn’t optimizing workflows or writing content, he’s likely composing electronic music or hiking in the back country.
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