April 28, 2016
4 min

A short guide to personas

The word “persona” has many meanings. Sometimes the term refers to a part that an actor plays, other times it can mean a famous person, or even a character in a fictional play or book. But in the field of UX, persona has its own special meaning.

Before you get started with creating personas of your own, learn what they are and the process to create one. We'll even let you in on a great, little tip — how to use Chalkmark to refine and validate your personas.

What is a persona?

In the UX field, a persona is created using research and observations of your users, which is analyzed and then depicted in the form of a person’s profile. This individual is completely fictional, but is created based on the research you’ve conducted into your own users. It’s a form of segmentation, which Angus Jenkinson noted in his article “Beyond Segmentation” is a “better intellectual and practical tool for dealing with the interaction between the concept of the ‘individual’ and the concept of ‘group’”.

Typical user personas include very specific information in order to paint an in-depth and memorable picture for the people using them (e.g., designers, marketers etc).

The user personas you create don’t just represent a single individual either; they’ll actually represent a whole group. This allows you to condense your users into just a few segments, while giving you a much smaller set of groups to target.

There are many benefits of using personas. Here are just a few:

     
  • You can understand your clients better by seeing their pain points, what they want, and what they need
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  • You can narrow your focus to a small number of groups that matter, rather than trying to design for everybody
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  • They’re useful for other teams too, from product management to design and marketing
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  • They can help you clarify your business or brand
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  • They can help you create a language for your brand
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  • You can market your products in a better, more targeted way

How do I create a persona?

There’s no right or wrong way to create a persona; the way you make them can depend on many things, such as your own internal resources, and the type of persona you want.

The average persona that you’ve probably seen before in textbooks, online or in templates isn’t always the best kind to use (picture the common and overused types like ‘Busy Barry’). In fact, the way user personas are constructed is a highly debated topic in the UX industry.

Creating good user personas

Good user personas are meaningful descriptions — not just a list of demographics and a fake name that allows researchers to simply make assumptions.

Indi Young, an independent consultant and founder of Adaptive Path, is an advocate of creating personas that aren’t just a list of demographics. In an article she penned on medium.com, Indi states: “To actually bring a description to life, to actually develop empathy, you need the deeper, underlying reasoning behind the preferences and statements-of-fact. You need the reasoning, reactions, and guiding principles.”

One issue that can stem from traditional types of personas is they can be based on stereotypes, or even reinforce them. Things like gender, age, ethnicity, culture, and location can all play a part in doing this.

In a study by Phil Turner and Susan Turner titled “Is stereotyping inevitable when designing with personas?” the authors noted: “Stereotyped user representations appear to constrain both design and use in many aspects of everyday life, and those who advocate universal design recognise that stereotyping is an obstacle to achieving design for all.”

So it makes sense to scrap the stereotypes and, in many instances, irrelevant demographic data. Instead, include information that accurately describes the persona’s struggles, goals, thoughts and feelings — all bits of meaningful data.

Creating user personas involves a lot of research and analyzing. Here are a few tips to get you started:

1) Do your research

When you’re creating personas for UX, it’s absolutely crucial you start with research; after all, you can’t just pull this information out of thin air by making assumptions! Ensure you use a mixture of both qualitative and quantitative research here in order to cast your net wide and get results that are really valuable. A great research method that falls into the realms of both qualitative and quantitative is user interviews.

When you conduct your interviews, drill down into the types of behaviors, attitudes and goals your users have. It’s also important to mention that you can’t just examine what your users are saying to you — you need to tap into what they’re thinking and how they behave too.

2) Analyze and organize your data into segments

Once you’ve conducted your research, it’s time to analyze it. Look for trends in your results — can you see any similarities among your participants? Can you begin to group some of your participants together based on shared goals, attitudes and behaviors?

After you have sorted your participants into groups, you can create your segments. These segments will become your draft personas. Try to limit the number of personas you create. Having too many can defeat the purpose of creating them in the first place.

Don’t forget the little things! Give your personas a memorable title or name and maybe even assign an image or photo — it all helps to create a “real” person that your team can focus on and remember.

3) Review and test

After you’ve finalized your personas, it’s time to review them. Take another look at the responses you received from your initial user interviews and see if they match the personas you created. It’s also important you spend some time reviewing your finalized personas to see if any of them are too similar or overlap with one another. If they do, you might want to jump back a step and segment your data again.

This is also a great time to test your personas. Conduct another set of user interviews and research to validate your personas.

User persona templates and examples

Creating your personas using data from your user interviews can be a fun task — but make sure you don’t go too crazy. Your personas need to be relevant, not overly complex and a true representation of your users.

A great way to ensure your personas don’t get too out of hand is to use a template. There are many of these available online in a number of different formats and of varying quality.

This example from UX Lady contains a number of helpful bits of information you should include, such as user experience goals, tech expertise and the types of devices used. The accompany article also provides a fair bit of guidance on how to fill in your templates too. While this template is good, skip the demographics portion and read Indi Young’s article and books for better quality persona creation.

Using Chalkmark to refine personas

Now it’s time to let you in on a little tip. Did you know Chalkmark can be used to refine and validate your personas?

One of the trickiest parts of creating personas is actually figuring out which ones are a true representation of your users — so this usually means lots of testing and refining to ensure you’re on the right track. Fortunately, Chalkmark makes the refinement and validation part pretty easy.

First, you need to have your personas finalized or at least drafted. Take your results from your persona software or template you filled in. Create a survey for each segment so that you can see if your participants’ perceptions of themselves matches each of your personas.

Second, create your test. This is a pretty simple demo we made when we were testing our own personas a few years ago at Optimal Workshop. Keep in mind this was a while ago and not a true representation of our current personas — they’ve definitely changed over time! During this step, it’s also quite helpful to include some post-test questions to drill down into your participants’ profiles.

After that, send these tests out to your identified segments (e.g., if you had a retail clothing store, some of your segments might be women of a certain age, and men of a certain age. Each segment would receive its own test). Our test involved three segments: “the aware”, “the informed”, and “the experienced” — again, this has changed over time and you’ll find your personas will change too.

Finally, analyze the results. If you created separate tests for each segment, you will now have filtered data for each segment. This is the real meaty information you use to validate each persona. For example, our three persona tests all contained the questions: “What’s your experience with user research?” And “How much of your job description relates directly to user experience work?”

Persona2 results
   Some of the questionnaire results for Persona #2

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bove, you’ll see the results for Persona #2. This tells us that 34% of respondents identified that their job involves a lot of UX work (75-100%, in fact). In addition, 31% of this segment considered themselves “Confident” with remote user research, while a further 9% and 6% of this segment said they were “Experienced” and “Expert”.

Persona #2’s results for Task 1
   Persona #2’s results for Task 1

These results all aligned with the persona we associated with that segment: “the informed”.

When you’re running your own tests, you’ll analyze the data in a very similar way. If the results from each of your segments’ Chalkmark tests don’t match up with the personas you created, it’s likely you need to adjust your personas. However, if each segment’s results happen to match up with your personas (like our example above), consider them validated!

For a bit more info on our very own Chalkmark persona test, check out this article.

Further reading

 

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Clara Kliman-Silver: AI & design: imagining the future of UX

In the last few years, the influence of AI has steadily been expanding into various aspects of design. In early 2023, that expansion exploded. AI tools and features are now everywhere, and there are two ways designers commonly react to it:

  • With enthusiasm for how they can use it to make their jobs easier
  • With skepticism over how reliable it is, or even fear that it could replace their jobs

Google UX researcher Clara Kliman-Silver is at the forefront of researching and understanding the potential impact of AI on design into the future. This is a hot topic that’s on the radar of many designers as they grapple with what the new normal is, and how it will change things in the coming years.

Clara’s background 

Clara Kliman-Silver spends her time studying design teams and systems, UX tools and designer-developer collaboration. She’s a specialist in participatory design and uses generative methods to investigate workflows, understand designer-developer experiences, and imagine ways to create UIs. In this work, Clara looks at how technology can be leveraged to help people make things, and do it more efficiently than they currently are.

In today’s context, that puts generative AI and machine learning right in her line of sight. The way this technology has boomed in recent times has many people scrambling to catch up - to identify the biggest opportunities and to understand the risks that come with it. Clara is a leader in assessing the implications of AI. She analyzes both the technology itself and the way people feel about it to forecast what it will mean into the future.

Contact Details:

You can find Clara in LinkedIn or on Twitter @cklimansilver

What role should artificial intelligence play in UX design process? 🤔

Clara’s expertise in understanding the role of AI in design comes from significant research and analysis of how the technology is being used currently and how industry experts feel about it. AI is everywhere in today’s world, from home devices to tech platforms and specific tools for various industries. In many cases, AI automation is used for productivity, where it can speed up processes with subtle, easy to use applications.

As mentioned above, the transformational capabilities of AI are met with equal parts of enthusiasm and skepticism. The way people use AI, and how they feel about it is important, because users need to be comfortable implementing the technology in order for it to make a difference. The question of what value AI brings to the design process is ongoing. On one hand, AI can help increase efficiency for systems and processes. On the other hand, it can exacerbate problems if the user's intentions are misunderstood.

Access for all 🦾

There’s no doubt that AI tools enable novices to perform tasks that, in years gone by, required a high level of expertise. For example, film editing was previously a manual task, where people would literally cut rolls of film and splice them together on a reel. It was something only a trained editor could do. Now, anyone with a smartphone has access to iMovie or a similar app, and they can edit film in seconds.

For film experts, digital technology allows them to speed up tedious tasks and focus on more sophisticated aspects of their work. Clara hypothesizes that AI is particularly valuable when it automates mundane tasks. AI enables more individuals to leverage digital technologies without requiring specialist training. Thus, AI has shifted the landscape of what it means to be an “expert” in a field. Expertise is about more than being able to simply do something - it includes having the knowledge and experience to do it for an informed reason. 

Research and testing 🔬

Clara performs a lot of concept testing, which involves recognizing the perceived value of an approach or method. Concept testing helps in scenarios where a solution may not address a problem or where the real problem is difficult to identify. In a recent survey, Clara describes two predominant benefits designers experienced from AI:

  1. Efficiency. Not only does AI expedite the problem solving process, it can also help efficiently identify problems. 
  2. Innovation. Generative AI can innovate on its own, developing ideas that designers themselves may not have thought of.

The design partnership 🤝🏽

Overall, Clara says UX designers tend to see AI as a creative partner. However, most users don’t yet trust AI enough to give it complete agency over the work it’s used for. The level of trust designers have exists on a continuum, where it depends on the nature of the work and the context of what they’re aiming to accomplish. Other factors such as where the tech comes from, who curated it and who’s training the model also influences trust. For now, AI is largely seen as a valued tool, and there is cautious optimism and tentative acceptance for its application. 

Why it matters 💡

AI presents as potentially one of the biggest game-changers to how people work in our generation. Although AI has widespread applications across sectors and systems, there are still many questions about it. In the design world, systems like DALL-E allow people to create AI-generated imagery, and auto layout in various tools allows designers to iterate more quickly and efficiently.

Like many other industries, designers are wondering where AI might go in the future and what it might look like. The answer to these questions has very real implications for the future of design jobs and whether they will exist. In practice, Clara describes the current mood towards AI as existing on a continuum between adherence and innovation:

  • Adherence is about how AI helps designers follow best practice
  • Innovation is at the other end of the spectrum, and involves using AI to figure out what’s possible

The current environment is extremely subjective, and there’s no agreed best practice. This makes it difficult to recommend a certain approach to adopting AI and creating permanent systems around it. Both the technology and the sentiment around it will evolve through time, and it’s something designers, like all people, will need to maintain good awareness of.

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Why user research is essential for product development

Many organizations are aware that staying relevant essential for their success. This can mean a lot of things to different organizations. What it often means is coming up with plenty of new, innovative ideas and products to keep pace with the demands and needs of the marketplace. It also means keeping up with the expectations and needs of your users, which often means  shorter and shorter product development life cycle times.  While maintaining this pace can be daunting, it can also be seen as a strength, tightening up your processes and cutting out unnecessary steps.

A vital part of developing new (or tweaking existing) products is considering the end user first. There really is no point in creating anything new if it isn’t meeting a need or filling a gap in the market. How can you make sure you are hitting the right mark? Ask your users.  We look into some of the key user research methods available to help you in your product development process.

If you want to know more about how to fit research into your product development process, take a read here.

What is user research? 👨🏻💻

User experience (UX) research, or user research as it’s commonly referred to, is an important part of the product development process. Primarily, UX research involves using different research methods to gather qualitative and quantitative data and insights about how your users interact with your product. It is an essential part of developing, building, and launching a product that truly meets the needs, desires, and requirements of your users. 

At its simplest, user research is talking to your users and understanding what they want and why. And using this to deliver what they need.

How does user research fit into the product development process? 🧩🧩

User research is an essential part of the product development process. By asking questions of your users about how your product works and what place it fills in the market, you can create a product that delivers what the market needs to those who need it. 

Without user research, you could literally be firing arrows in the dark, or at the very best, working from a very internal organizational view based on assuming that what you believe users need is what they want. With user research, you can collect qualitative and quantitative data that clearly tells you where and what users would like to see and how they would use it.

Investing in user research right at the start of the product development process can save the team and the organization heavy investment in time and money. With detailed data responses, your brand-new product can leapfrog many development hurdles, delivering a final product that users love and want to keep using. Firing arrows to hit a bullseye.

What user research methods should we use? 🥺

Qualitative ResearchMethods

Qualitative research is about exploration. It focuses on discovering things we cannot measure with numbers and typically involves getting to know users directly through interviews or observation.

Usability Testing – Observational

One of the best ways to learn about your users and how they interact with your new product is to observe them in their own environment. Watch how they accomplish tasks, the order they do things, what frustrates them, and what makes the task easier and/or more enjoyable for your subject. The data can be collated to inform the usability of your product, improving intuitive design and what resonates with your users.

Competitive Analysis

Reviewing products already on the market can be a great start to the product development process. Why are your competitors’ products successful? And how well do they behave for users? Learn from their successes, and even better, build on where they may not be performing as well and find where your product fills the gap in the market.

Quantitative Research Methods

Quantitative research is about measurement. It focuses on gathering data and then turning this data into usable statistics.

Surveys

Surveys are a popular user research method for gathering information from a wide range of people. In most cases, a survey will feature a set of questions designed to assess someone’s thoughts on a particular aspect of your new product. They’re useful for getting feedback or understanding attitudes, and you can use the learnings from your survey of a subset of users to draw conclusions about a larger population of users.

Wrap Up 🌯

Gathering information on your users during the product development process and before you invest time and money can be hugely beneficial to the entire process. Collating robust data and insights to guide the new product development and respond directly to user needs, and filling that all-important niche. Undertaking user experience research shouldn’t stop at product development but throughout each and every step of your product life cycle. If you want to find out more about UX research throughout the life cycle of your product, take a read of our article UX research for each product phase.

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A beginner’s guide to qualitative and quantitative research

In the field of user research, every method is either qualitative, quantitative – or both. Understandably, there’s some confusion around these 2 approaches and where the different methods are applicable.This article provides a handy breakdown of the different terms and where and why you’d want to use qualitative or quantitative research methods.

Qualitative research

Let’s start with qualitative research, an approach that’s all about the ‘why’. It’s exploratory and not about numbers, instead focusing on reasons, motivations, behaviors and opinions – it’s best at helping you gain insight and delve deep into a particular problem. This type of data typically comes from conversations, interviews and responses to open questions.The real value of qualitative research is in its ability to give you a human perspective on a research question. Unlike quantitative research, this approach will help you understand some of the more intangible factors – things like behaviors, habits and past experiences – whose effects may not always be readily apparent when you’re conducting quantitative research.A qualitative research question could be investigating why people switch between different banks, for example.

When to use qualitative research

Qualitative research is best suited to identifying how people think about problems, how they interact with products and services, and what encourages them to behave a certain way. For example, you could run a study to better understand how people feel about a product they use, or why people have trouble filling out your sign up form. Qualitative research can be very exploratory (e.g., user interviews) as well as more closely tied to evaluating designs (e.g., usability testing).Good qualitative research questions to ask include:

  • Why do customers never add items to their wishlist on our website?
  • How do new customers find out about our services?
  • What are the main reasons people don’t sign up for our newsletter?

How to gather qualitative data

There’s no shortage of methods to gather qualitative data, which commonly takes the form of interview transcripts, notes and audio and video recordings.Here are some of the most widely-used qualitative research methods:

  • Usability test – Test a product with people by observing them as they attempt to complete various tasks.
  • User interview Sit down with a user to learn more about their background, motivations and pain points.
  • Contextual inquiry – Learn more about your users in their own environment by asking them questions before moving onto an observation activity.
  • Focus group – Gather 6 to 10 people for a forum-like session to get feedback on a product.

How many participants will you need?

You don’t often need large numbers of participants for qualitative research, with the average range usually somewhere between 5 to 10 people. You’ll likely require more if you're focusing your work on specific personas, for example, in which case you may need to study 5-10 people for each persona.While this may seem quite low, consider the research methods you’ll be using. Carrying out large numbers of in-person research sessions requires a significant time investment in terms of planning, actually hosting the sessions and analyzing your findings.

Quantitative research

On the other side of the coin you’ve got quantitative research. This type of research is focused on numbers and measurement, gathering data and being able to transform this information into statistics.Given that quantitative research is all about generating data that can be expressed in numbers, there multiple ways you make use of it. Statistical analysis means you can pull useful facts from your quantitative data, for example trends, demographic information and differences between groups. It’s an excellent way to understand a snapshot of your users.A quantitative research question could involve investigating the number of people that upgrade from a free plan to a paid plan.

When to use quantitative research

Quantitative research is ideal for understanding behaviors and usage. In many cases it's a lot less resource-heavy than qualitative research because you don't need to pay incentives or spend time scheduling sessions etc). With that in mind, you might do some quantitative research early on to better understand the problem space, for example by running a survey on your users.Here are some examples of good quantitative research questions to ask:

  • How many customers view our pricing page before making a purchase decision?
  • How many customers search versus navigate to find products on our website?
  • How often do visitors on our website change their password?

How to gather quantitative data

Commonly, quantitative data takes the form of numbers and statistics.

Here are some of the most popular quantitative research methods:

  • Card sorts – Find out how people categorize and sort information on your website.
  • First-click tests – See where people click first when tasked with completing an action.
  • A/B tests – Compare 2 versions of a design in order to work out which is more effective.
  • Clickstream analysis – Analyze aggregate data about website visits.

How many participants will you need?

While you only need a small number of participants for qualitative research, you need significantly more for quantitative research. Quantitative research is all about quantity. With more participants, you can generate more useful and reliable data you can analyze. In turn, you’ll have a clearer understanding of your research problem.This means that quantitative research can often involve gathering data from thousands of participants through an A/B test, or with 30 through a card sort. Read more about the right number of participants to gather for your research.

Mixed methods research

While there are certainly times when you’d only want to focus on qualitative or quantitative data to get answers, there’s significant value in utilizing both methods on the same research projects.Interestingly, there are a number of research methods that will generate both quantitative and qualitative data. Take surveys as an example. A survey could include questions that require written answers from participants as well as questions that require participants to select from multiple choices.

Looking back at the earlier example of how people move from a free plan to a paid plan, applying both research approaches to the question will yield a more robust or holistic answer. You’ll know why people upgrade to the paid plan in addition to how many. You can read more about mixed methods research in this article:

Where to from here?

With an understanding of qualitative and quantitative user research, the next best step would be to start learning more about the various methods that fall under each of these research approaches and how to actually conduct research effectively.

Here are some of the best articles to read next:

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