Without further ado, here are nine things that people often get wrong about personas (based on our experience):
The problem with mean-centered personas (i.e., those that describe average, typical users) is the general problem with the mean: if half of your users are right-handed and half are left-handed, should your persona be middle-handed?
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The Basics of Data-Driven Personas
Data-driven personas can be generated from almost any data. Essentially, the generation consists of two steps: (1) pattern seeking, and (2) enrichment.
We often get questions from prospective clients about how to use personas. Like, “okay, after I have these personas, then what? How can I use them?“.
I have two standard responses for this:
- Persona analytics is just like any analytics. If you want to understand your customers, there must be some reason for that. What is that reason?
- What are your marketing/business/design goals? What do you want to improve? After knowing these, we can then tell how we think personas could be useful.
The point is that we are not some magicians that can tell you what to do. Instead, we should figure it out together. It’s called co-creation — you, as the client know more about your organization, your problems and goals than us. We need that information to figure out if personas make sense and if so, how.
Data-Driven Personas are algorithmically created from user behavioral and demographic data.
These analytic personas are a valuable Human Computer Interaction (HCI) design technique in themselves, especially when incorporated into an interactive persona analytics system.