Without further ado, here are nine things that people often get wrong about personas (based on our experience):
Personas have broad applicability for digital innovation, giving their applicability for human centered user, customer, and audience understanding in activities such as product development, design, testing, content creation, and marketing.
It is important that a persona is fictional (not an actual individual) yet realistic (based on real data from actual users).
The persona technique has inherent advantages relative to other user analytics techniques, in that persona provides a human face to “cold” numbers, among others. Personas can also be used in conjunction with these other techniques.
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.
Persona and Persona Profile
A persona is a humanized representation of a user segment, audience segment, or customer segment.
The persona is presented in a persona profile. A persona profile is the actual representation of the persona. So, a persona is a conceptualize. The person profile is the physical manifestation of that concept.
The persona profile contains various attributes, insights, and information about the persona. Each of these are an element of the persona profile.
Example of a Persona Profile
Here is an example of a data-driven persona profile created by APG, the automated persona generation system.
What elements does a persona profile typically contain?
We are putting the finished touches on our book, Data-Driven Personas by Bernard J. Jansen, Joni O. Salminen, Soon-Gyo Jung, and Kathleen Guan, Hamad Bin Khalifa University (HBKU) and University College London. Morgan & Claypool Publishers.
The book is comprised of 12 chapters (10 content chapters, plus an introduction and conclusion chapter) divided into 6 themed sections.
Plus, we are adding as a bonus, 3 appendices that practitioners (and researchers) will find valuable.
In a previous post, we analyzed the demographic Bias in Artificially Generated Facial Pictures that raised a concern that the generated images might not fairly represent all demographic groups.
In this post, we discuss if these artificially generated pictures are good enough for use in personas profiles for real-world systems and applications, which are highly dependent on images for the personas. One of the key aspects of generating personas using a data-driven approach is to be able to represent the persona profile with a matching picture.
1) Define target market by industry (x), country (y), and size
2) Find out the job titles (z) in these companies that make decisions about buying
3) For each of the x * y * z = n segments, locate 5-10 people to interview
4) Conduct the interviews
5) Analyze the interviews for key pain points, needs, willingness to buy
6) Based on patterns, reduce the number. For example, 12 segments can become 3 personas.
7) Give your personas name and picture. Write descriptions that include pain points, needs, and willingness to buy. Add direct quotes to support. Done.