As we learned in the previous post, “What are user personas, and why are they important?”, personas are used to orient design, development, marketing, sales, and strategy teams to the user experience so they can deeply understand their customer set. Although there are many methods that can be used to create user personas, the three most common are Proto, Qualitative, and Statistical. These methods vary in depth and breadth of research required, and are each individually suitable for different teams with different needs.
Proto personas
Proto personas are a light version of personas that are created with no new research. They can be based on existing user data, however, in many cases are based solely on the assumptions of the team creating them. Typically, proto personas are created in a workshop that involves a team and key stakeholders. The workshop usually takes 2–4 hours, and each participant creates 2–5 proto personas based on their own understanding using a simple template. The group then comes together to discuss all the personas collectively and edits the options into a final set of 3–6 proto personas.
Pros of the proto persona methodology include:
- Proto personas are well-suited to teams that are working in a Lean UX framework or have low UX maturity, and would otherwise not use personas at all.
- Proto personas make a team’s implicit assumptions about users explicit. Cataloging the team’s different assumptions provides some shared direction, even if the results do not accurately capture the real users.
- They can be a gateway to future research if the team considers them to be hypotheses that can be validated with research (or revised once any incorrect assumptions are brought to light).
Cons of the proto persona methodology include:
- Proto personas are not driven by research, meaning they are often an inaccurate representation of users and can echo a team’s incorrect assumptions.
- If the team finds little value in these personas, they can lead to a negative halo effect towards personas in general and towards other collaborative UX activities.
Qualitative personas
Qualitative personas are created by conducting qualitative research such as interviews, and then segmenting users based on shared attitudes, goals, pain points, and expectations. These personas start by interviewing 5–30 users; interviews can be separate sessions or integrated into a usability test or field study. The research will typically uncover user pain points, expectations for the features and behavior of a product or service, uses of a product or service, and goals. Once interviews are conducted, transcripts are reviewed, and data is categorized into major themes (also known as coding the data). During this coding/analysis, patterns are identified by looking for interviewees that overlap on key themes. Personas are then created based on these similarities.
Pros of qualitative personas include:
- Qualitative personas are suitable for most teams, especially when considering the effort involved in creating them relative to their value.
- They require a low time commitment.
- Qualitative personas are more accurate than proto personas because they are based on user data, and they provide key insights about user motivations, expectations, and needs – data points that are impossible to get from either analytics data, demographic info, or assumptions alone.
Cons of qualitative personas include:
- Due to the smaller sample size, there is no way to determine the actual proportion of the user population that each persona represents.
- There is a possibility that some users with unique characteristics will be omitted, or outliers with uncommon viewpoints may be overrepresented.
- There could be the need to constantly push back on claims that qualitative personas are “not scientific,” especially if the organization has low UX maturity.
Statistical personas
Statistical personas are mixed-method personas that include both qualitative and quantitative research. They are the most resource-intensive personas to create but are also the most accurate. The first step in creating statistical personas is to conduct exploratory qualitative research to identify main themes that come up repeatedly among users. Based on this qualitative data, a survey is then created to allow researchers to collect quantitative data about the major themes on a larger scale. It is recommended that at least 100 (ideally 500+) respondents are surveyed — statistical-analysis techniques work better with large sample sizes. Next, a statistical clustering technique such as latent class analysis, factor analysis, or K-means clustering is used to find the patterns in the survey data.
Pros of statistical personas:
- Large sample sizes ensure that outliers are not overrepresented in personas.
- It is possible to calculate what percentage of the total user base each persona represents, which can be helpful for trade off decisions that benefit one persona over another.
- Statistical personas allow to reverse-engineer the persona clustering (using discriminant analysis) to figure out which survey questions most strongly predict which persona someone was clustered into. Those questions can then be used to recruit users in future studies.
Cons of statistical personas:
- Statistical persona segmentation is expensive, time-consuming, and requires expertise in statistical analysis.
- The patterns that often come up in this type of analysis may not be meaningful for designers, and it may be hard to put into words the criteria used for classifying users based on these analyses.
- It is not uncommon for a team to do the statistical work and end with personas that are very similar to purely qualitative personas based on the same qualitative research data.
HelloInfo has supported many clients in the development of qualitative user personas. In one study, HelloInfo interviewed electrical engineers to support a client’s User Experience Team in deeply understanding how individuals in this role work through a project workflow on a day-to-day basis. In another study, HelloInfo interviewed a set of client customers and non-customers (based on an ideal customer profile) to understand how they use and purchase equipment and services, and how they prefer to receive sales and marketing communications. These projects support our clients in better understanding their customers and allow product design teams to gain empathy for the customer when they are developing products.
Are you interested in learning more about persona types and how they can help your marketing, strategy, sales, design, and development teams delight users? How well do your colleagues understand your customers? HelloInfo can help your team lay the groundwork to provide a superior user experience. Schedule a meeting with us to start the conversation.
