Creating personas with secondary data
Personas are realistic fictional characters, which represent different target users. They are an integral part of the design process and are usually based on research. Persona as a term and format for user representations was introduced in 1999 by Alan Cooper. Since then, personas have been subject to much discussion, debate, and further development. Personas are widely used in the UX industry and they play an important part in product development.
“A persona helps project teams answer two fundamental questions: who are we solving for and who are we not solving for?’” — Miaskiewicz and Kozar (2011)
Research has identified 4 main benefits of using personas:
They help to focus the design effort on specific users and their needs. Using personas in the design process allows designers to focus on user priorities and limit the product feature set.
Personas aid the communication between the team and the stakeholders. They help the product team adopt a common language about users. This improves the communication between the design team and the stakeholders. For example, the term ‘user’ can mean different things to different people within the team.
Personas make implicit assumptions of users explicit. They help the development team avoid self-referential design; projecting their own goals, motivations, and skills to the user.
Finally, personas bring empathy to the design process. They allow the product team to humanise large collections of data. This makes designers and stakeholders relate to and empathise better with users. According to Nielsen, increased empathy helps designers better understand their users and their needs.
“It is easier to design for a specific somebody, rather than a generic everybody” — Goltz
Traditionally, personas are created by conducting extensive research, including ethnographic interviews, and focus groups. This process can be lengthy and cost a lot to the company. This could be an issue for start-ups or companies without enough resources. However, this does not mean that creating data drive personas is not possible. We can create personas by using secondary data that might already be available to us. When creating personas with secondary data, we rely on existing data (either previous research that has been conducted or information from external sources). This method can save the company money but it still requires time in order to find the right type of data and conduct the analysis. Where can we get secondary data? It is possible to find suitable user data through social media platforms, existing case studies, or previous internal research.
In most cases, target groups are composed of individuals with various characteristics and different needs. As a result, we need to develop multiple personas to represent our target group more accurately. Each persona should be representative of a segment of the target group. The number of personals that need to be developed depends on the target group and how diverse it is. There is no golden rule on how many personas we need but trying to limit them to three or four personas is best. The goal of personas is to focus on the major needs of the most important user groups. It is important to be able to see clear differences between personas. If they are too similar, this could indicate we have too many and some might need to be merged. In order to identify how many personas we need, we have to pre-define our core target group.
Steps
The first step is to make a list of the core target group of our product and their motivations. This should be based on our previous experience and expectations and it’s only an initial list to get started, not a complete, extensive list of the target group. It should be seen as a form of brainstorming.
Once the list is ready, we can start looking for secondary data to give us more insight into our target core group. We will need data on the following topics:
demographics: this should include information such as age, gender, geographical location of the user. Some potential sources for demographics are social media analytics (examine who follows the channels of our company), website analytics, previous research conducted in our company, competitive research.
psychographics: personality types, values, opinions, beliefs, attitudes, activities, interests, lifestyles, etc. We can find this information by looking at social media analytics, industry publications, and blogs, academic research.
skills: this should be focused on education and relevant skills and professional experience. Any technical skills related to our product should be considered as well as soft skills. Some likely sources of information for this can be previous studies conducted by our company (e.g., surveys, usability tests), first-hand experience with our users, external research.
motivation: why are the users using our product? Insights on this can be found in previous research (internal or external), competitive research, first-hand experience with users.
personal motivation/goals: what are the personal motivations of our users? For example, what are their short-term and long-term goals (personal/career)? Information on motivations/goals can be found in the sources used for motivation, psychographics, and skills.
The next step involves updating our initial assumptions with the collected information and organising the elements into persona groups that represent our target users. Each group should be named or classified.
The next stage is refinement. The personas should be combined and prioritised. If we end up with a large number of personas, it is important to separate them into primary, secondary, and, if necessary, complementary categories. At the end of this stage, we should have roughly 3–5 primary personas with clearly identified characteristics depending on the size of the project.
The next part is the most creative of the process; it’s when we get to be a bit more creative and create a story to go with each persona. We need to put our research into the demographics, psychographics, skills, and motivations together with a compelling story about our fictional character. The aim is to develop appropriate descriptions of each personas background, motivations, and expectations. Some personal information can be included but it should not be the focus of the persona.
To increase the empathising aspect of personas, Nielsen (2013) suggests employing film writing techniques to write more engaging persona descriptions with rich characters and narratives.
Elements of a Persona
Some of the information that can be included in the personas are the following:
Persona Group (e.g., PhD student)
Fictional name (e.g, Blanche Devereaux): this personifies the persona bringing it to life and making it more realistic.
Job titles and major responsibilities
Demographics such as age, education, ethnicity, and family status
Description of a daily life in a narrative form
Personality type, qualities, and personal motivations: We can use adjectives to describe the persona (e.g., friendly, hard-working, curious, risk-taker).
Context-related goals and motivations: what are they trying to achieve using our product?
Context-related skills and knowledge: this should include skills and previous knowledge related to our product.
Pain points or frustrations related to the context
A quote that sums up what matters most to the persona as it relates to our product. For example, the quote could summarise the persona’s expectations while using a specific product. This could be a real quote from secondary research.
Photo or illustration of a representative user: these can either be created or stock images can be used.
Ideally, every statement in the persona should have a point of data to support it. However, this is very difficult to achieve when mostly relying on secondary data. Some practitioners argue that the value created by the engaging narrative is greater than the high accuracy of the persona description. For example, Cooper claimed that it is more important to define the persona in great and specific detail than ensuring the persona is perfectly accurate.
In any case, some assumptions have to be used when creating personas. In order to maintain transparency and keep our biases in check, it is important to document where assumptions about the personas have been made. A document can be created including all the sources used for the creation of the personas as well as our assumptions. After persona creation, the next steps recommended are persona validation, scenario creation, educating the organisation, and using personas in design work.
How good are secondary data personas?
Research has shown that secondary data can produce personas that are rich in description. However, using only secondary data should be seen as a first step as it might not be sufficient in order to gain a deeper understanding of our users. The quality of personas created using only secondary data depends on the quality of the data used in their creation process. In some cases, it is challenging to find good quality data relevant to the product of interest. The availability of data has to be considered. Sometimes, the secondary data might not be enough and more research might be required.
Another potential issue with this kind of personas is the varying levels of detail in the data. This can result in uneven persona descriptions. For example, we might have more information about a specific type of user but less detail about others.
There is limited evidence about the validity of personas created with secondary data. Even though they can be perceived as reliable by designers and the product team, there is no evidence showing how well they represent our target group. This is something we can measure after creating our personas with user research.
Personas created with secondary data can be seen as a step between proto-personas and true data-driven personas. They can be helpful when we have access to a rich amount of secondary data and we don’t have the resources to conduct primary research.