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?
People sometimes struggle to understand what we mean by personas.
This is because there are two alternative definitions to personas. This blog posts explains each of these definitions.
Our team has been working on the question of how many personas to create for a while now. It’s an elusive questions that keeps us searching for a final answer that may not be out there.
Regardless, we have been able to identify some factors relevant to this question. In this post, I’ll briefly go through these factors.
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.
This is a guest post written by Mr. Timo Hänninen from Konvertigo.io, a global digital marketing agency. If you want to write a guest blog about personas, please contact us (firstname.lastname@example.org).