The 4 Layers of Effective Buyer Personas

While we’re obviously biased in favor of data-driven behavioral segmentation, today we’ll take a look at how qualitative, quantitative, and gut feeling-built personas can complement each other to foster real engagement.

What is an actual persona? At a high level, it is a representation of a segment of buyers based on data to reflect behaviors, attitudes, or characteristics. Essentially a persona is an abstraction of a group out of a population as a whole, they can be used in a variety of ways.

Now let’s acknowledge a distinction that exists between the terms “persona” and “segment.”

The most common way to distinguish the two is that personas can be more qualitative descriptions (based around general demographic traits and sentiments) and segments are quantitative (using data around purchase behavior, average spend, engagement metrics, etc.).

Segmentation is the process of splitting up your customers into groups based on behaviors, characteristics and/or needs. Several levels of segmentation exist, including geographic, demographic, psychographic, and behavioral. For example, a segment can be “all customers who live in San Francisco” or “all customers who purchased handbags.”

For our purposes in this article, the distinction is not terribly important because both approaches have the same goal in mind and—in a sophisticated retail organization—investment is both can lend a more nuanced understanding of your customers.

Today we’re going to use the terms interchangeably because we’re simply talking about the idea of organizing customer insight to better understand your customers so that you can improve acquisition and retention.

Now, on to the layers of differentiating customers!


The first way a persona is created is by intuition and gut feeling. This is a valid and reasonable approach for small upstart retailers who have “me and my friends” in mind for an ideal audience or they have some special psychographic understanding of a particular trend.

These kinds of personas might actually be more useful than rigorous market research is the case of certain creative products. If every “creative” product was built based on some amalgamation of past purchase behavior we would a ton of “creative” products with no actual creativity or innovation.

But gut feel will only get you so far, especially as product offerings expand. The “me and my friends” model of personas might be enough to get you your first wave of customers, but then it’s time to get more thorough and deliberate about understanding your audience.



The second persona is built around demographic data. This is typical, even in mass advertising context or in a situation where you might not have specific customer-level data, but you might have good overall abstractions of customer data like age, location, or where they're from.

These are useful segments when you either don't have the data or you’re trying to draw some massive generalization. But in the context of things that are used for marketing or building products, it can sometimes be generalized.  



Many companies build clusters of customers or non-customers based on stated attitudes. For example, a car company would build personas based on customers that state that they reject the status quo. Oftentimes in mass advertising or where you're trying to tap into a more emotional vein within a group of customers or non-customers, stated preferences can be helpful.

If you’re still wondering about the distinction between personas and segments, stated preferences fall firmly in the “persona” camp and is the result of interviewing customers and doing follow-up research around what you learned (say, for instance, they named their favorite blogs and magazines, you might get up to speed on those publications and even advertise there).



The final form of buyer personas is built around observed preferences, i.e., actual buying behavior. As the name implies, these types of personas are based on—not so much the way people feel or the way they think—but what they've actually done and what we actually observe of their behaviors, typically with first-party data.

In the context of retail, this generally means we've seen their purchase history, and we can somehow group their purchase history by a series of attributes and conduct the analysis to create the actual personas.


With all of these ways to sort, segment, and organize your customers based on various types of inputs, it can get easy to get carried away. That’s why we like to keep it R.E.A.L. — Relevant, Efficient, Actionable and Lasting


Segments should be defined on attributes that can explain differences in customer behavior. That is, there should be some plausible explanation for the uniqueness of the segment causing a behavior rather than just co-occurring with it.

For example, a geographic segmentation that results in customers from Miami buying more beach towels than those from Montreal is better than a geographic segmentation that results in customers from Miami buying more yellow highlighters than those from Montreal.


As mentioned before, creating segments that are too granular is impractical and misses out on the opportunity to identify common behaviors and preferences. While there is no “correct” number of segments, you want to have few enough so that you aren't overwhelmed by the overhead associated with treating the segments differently.


Similarly, you should be able to measure and act upon segmentation dimensions.

For example, an online car rental site may have anecdotal evidence that indicates that taller-than-average customers are more likely to choose sedans with extra legroom over compact cars, but if the company doesn't know the height of each of their potential customers upfront and/or they can't collect that information easily, then segmenting by height is not a very useful idea.



Good segmentation relies on dimensions that will remain relatively stable over time. Segmenting customers based on how they responded to a one-time promotion is useless for categorizing customers who weren't exposed to that promotion.

If you want to track the performance of these segments over time and the customers in that segment are changing very quickly, any comparisons you make will be meaningless.



All of the above layers of segmentation can be helpful and complementary in serving different use cases. We might be biased, but if we had to choose one, we’d go for data-driven, behavior-based segmentation to give you a surgical advantage for improving acquisition and retention.

If you’re interested in learning more about building personas and segments that are measurably useful, check out our short Segmentation Course over at CustoraU, where we cover demographic and behavioral segmentation and tie it all together by applying segmentation to common use cases.


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