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

Last week Optimal Workshop was delighted to sponsor UXDX USA 2024 in New York. The User Experience event brings together Product, Design, UX, CX, and Engineering professionals and our team had an amazing time meeting with customers, industry experts, and colleagues throughout the conference. This year, we also had the privilege of sharing some of our industry expertise by running an interactive forum on “Measuring the Value of UX Research” - a topic very close to our hearts.

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Our forum, hosted by Optimal Workshop CEO Alex Burke and Product Lead Ella Fielding, was focused on exploring the value of User Experience Research (UXR) from both an industry-wide perspective and within the diverse ecosystem of individual companies and teams conducting this type of research today.

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The session brought together a global mix of UX professionals for a rich discussion on measuring and demonstrating the effectiveness of and the challenges facing organizations who are trying to tie UXR to tangible business value today.

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The main topics for the discuss were: 

• Metrics that Matter: How do you measure UXR's impact on sales, customer satisfaction, and design influence?
• Challenges & Strategies: What are the roadblocks to measuring UXR impact, and how can we overcome them?
• Beyond ROI:  UXR's value beyond just financial metrics

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Some of the key takeaways from our discussions during the session were: 

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1. The current state of UX maturity and value
   • Many UX teams don’t measure the impact of UXR on core business metrics and there were more attendees who are not measuring the impact of their work than those that are measuring it. 
   • Alex & Ella discussed with the attendees the current state of UX research maturity and the ability to prove value across different organizations represented in the room. Most organizations were still early in their UX research maturity with only 5% considering themselves advanced in having research culturally embedded.

2. Defining and proving the value of UX research
   • The industry doesn’t have clear alignment or understanding of what good measurement looks like. Many teams don’t know how to accurately measure UXR impact or don’t have the tools or platforms to measure it, which serve as core roadblocks for measuring UXRs’ impact. 
   • Alex and Ella discussed challenges in defining and proving the value of UX research, with common values being getting closer to customers, innovating faster, de-risking product decisions, and saving time and money. However, the value of research is hard to quantify compared to other product metrics like lines of code or features shipped.

3. Measuring and advocating for UX research
   • When teams are measuring UXR today there is a strong bias for customer feedback, but little ability or understanding about how to measure impact on business metrics like revenue. 
   • The most commonly used metrics for measuring UXR are quantitative and qualitative feedback from customers as opposed to internal metrics like stakeholder involvement or tieing UXR to business performance metrics (including financial performance). 
   • Attendees felt that in organizations where research is more embedded, researchers spend significant time advocating for research and proving its value to stakeholders rather than just conducting studies. This included tactics like research repositories and pointing to past study impacts as well as ongoing battles to shape decision making processes. 
   • One of our attendees highlighted that engaging stakeholders in the process of defining key research metrics prior to running research was a key for them in proving value internally. 

4. Relating user research to financial impact
   • Alex and Ella asked the audience if anyone had examples of demonstrating financial impact of research to justify investment in the team and we got some excellent examples from the audience proving that there are tangible ways to tie research outcomes to core business metrics including:
   • Calculating time savings for employees from internal tools as a financial impact metric. 
   • Measuring a reduction in calls to service desks as a way to quantify financial savings from research.

5. Most attendees recognise the value in embedding UXR more deeply in all levels of their organization - but feel like they’re not succeeding at this today. 
   • Most attendees feel that UXR is not fully embedded in their orgnaization or culture, but that if it was - they would be more successful in proving its overall value.
   • Stakeholder buy-in and engagement with UXR, particularly from senior leadership varied enormously across organizations, and wasn’t regularly measured as an indicator of UXR value 
   • In organizations where research was more successfully embedded, researchers had to spend significant time and effort building relationships with internal stakeholders before and after running studies. This took time and effort away from actual research, but ended up making the research more valuable to the business in the long run. 

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With the large range of UX maturity and the democratization of research across teams, we know there’s a lot of opportunity for our customers to improve their ability to tie their user research to tangible business outcomes and embed UX more deeply in all levels of their organizations. To help fill this gap, Optimal Workshop is currently running a large research project on Measuring the Value of UX which will be released in a few weeks.
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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.

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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.

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What is a persona?

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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’”.

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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).

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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.

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

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

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How do I create a persona?

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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.

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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.

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Creating good user personas

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

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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.”

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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.

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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.”

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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.

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

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1) Do your research

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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.

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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.

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2) Analyze and organize your data into segments

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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?

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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.

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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.

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3) Review and test

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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.

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

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User persona templates and examples

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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.

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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.

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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.

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Using Chalkmark to refine personas

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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?

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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.

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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.

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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.

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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.

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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?”

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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”.

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

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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!

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

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Further reading

   
• "From personas to user stories" - an article by Roman Pichler on how to discover the right stories and integrate them into a sprint.
   
• "Personas make users memorable for product team members" - Aurora Bedford looks at how personas support user-centered design throughout a project’s lifecycle.
   
• "Developing successful personas" - Luke Wroblewski presents notes from a talk by Tamara Adlin who outlines a set of practical ways to align stakeholder, developers, and designers to use personas.
   
• "Describing personas" - Indi Young looks at some of the problems in accurately describing personas.
 

As artificial intelligence (AI) continues to advance and permeate various industries, the field of user experience (UX) research is no exception. 

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At Optimal Workshop, our recent Value of UX report revealed that 68% of UX professionals believe AI will have the greatest impact on analysis and synthesis in the research project lifecycle. In this article, we'll explore the current and potential applications of AI in UXR, its limitations, and how the role of UX researchers may evolve alongside these technological advancements.

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How researchers are already using AI

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AI is already making inroads in UX research, primarily in tasks that involve processing large amounts of data, such as

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• Automated transcription: AI-powered tools can quickly transcribe user interviews and focus group sessions, saving researchers significant time.

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• Sentiment analysis: Machine learning algorithms can analyze text data from surveys or social media to gauge overall user sentiment towards a product or feature.

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• Pattern recognition: AI can help identify recurring themes or issues in large datasets, potentially surfacing insights that might be missed by human researchers.

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• Data visualization: AI-driven tools can create interactive visualizations of complex data sets, making it easier for researchers to communicate findings to stakeholders.

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As AI technology continues to evolve, its role in UX research is poised to expand, offering even more sophisticated tools and capabilities. While AI will undoubtedly enhance efficiency and uncover deeper insights, it's important to recognize that human expertise remains crucial in interpreting context, understanding nuanced user needs, and making strategic decisions. 

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The future of UX research lies in the synergy between AI's analytical power and human creativity and empathy, promising a new era of user-centered design that is both data-driven and deeply insightful.

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The potential for AI to accelerate UXR processes

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As AI capabilities advance, the potential to accelerate UX research processes grows exponentially. We anticipate AI revolutionizing UXR by enabling rapid synthesis of qualitative data, offering predictive analysis to guide research focus, automating initial reporting, and providing real-time insights during user testing sessions. 

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These advancements could dramatically enhance the efficiency and depth of UX research, allowing researchers to process larger datasets, uncover hidden patterns, and generate insights faster than ever before. As we continue to develop our platform, we're exploring ways to harness these AI capabilities, aiming to empower UX professionals with tools that amplify their expertise and drive more impactful, data-driven design decisions.

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AI’s good, but it’s not perfect

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While AI shows great promise in accelerating certain aspects of UX research, it's important to recognize its limitations, particularly when it comes to understanding the nuances of human experience. AI may struggle to grasp the full context of user responses, missing subtle cues or cultural nuances that human researchers would pick up on. Moreover, the ability to truly empathize with users and understand their emotional responses is a uniquely human trait that AI cannot fully replicate. These limitations underscore the continued importance of human expertise in UX research, especially when dealing with complex, emotionally-charged user experiences.

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Furthermore, the creative problem-solving aspect of UX research remains firmly in the human domain. While AI can identify patterns and trends with remarkable efficiency, the creative leap from insight to innovative solution still requires human ingenuity. UX research often deals with ambiguous or conflicting user feedback, and human researchers are better equipped to navigate these complexities and make nuanced judgment calls. As we move forward, the most effective UX research strategies will likely involve a symbiotic relationship between AI and human researchers, leveraging the strengths of both to create more comprehensive, nuanced, and actionable insights.

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Ethical considerations and data privacy concerns‍

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As AI becomes more integrated into UX research processes, several ethical considerations come to the forefront. Data security emerges as a paramount concern, with our report highlighting it as a significant factor when adopting new UX research tools. Ensuring the privacy and protection of user data becomes even more critical as AI systems process increasingly sensitive information. Additionally, we must remain vigilant about potential biases in AI algorithms that could skew research results or perpetuate existing inequalities, potentially leading to flawed design decisions that could negatively impact user experiences.

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Transparency and informed consent also take on new dimensions in the age of AI-driven UX research. It's crucial to maintain clarity about which insights are derived from AI analysis versus human interpretation, ensuring that stakeholders understand the origins and potential limitations of research findings. As AI capabilities expand, we may need to revisit and refine informed consent processes, ensuring that users fully comprehend how their data might be analyzed by AI systems. These ethical considerations underscore the need for ongoing dialogue and evolving best practices in the UX research community as we navigate the integration of AI into our workflows.

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The evolving role of researchers in the age of AI

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As AI technologies advance, the role of UX researchers is not being replaced but rather evolving and expanding in crucial ways. Our Value of UX report reveals that while 35% of organizations consider their UXR practice to be "strategic" or "leading," there's significant room for growth. This evolution presents an opportunity for researchers to focus on higher-level strategic thinking and problem-solving, as AI takes on more of the data processing and initial analysis tasks.

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The future of UX research lies in a symbiotic relationship between human expertise and AI capabilities. Researchers will need to develop skills in AI collaboration, guiding and interpreting AI-driven analyses to extract meaningful insights. Moreover, they will play a vital role in ensuring the ethical use of AI in research processes and critically evaluating AI-generated insights. As AI becomes more prevalent, UX researchers will be instrumental in bridging the gap between technological capabilities and genuine human needs and experiences.

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Democratizing UXR through AI

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The integration of AI into UX research processes holds immense potential for democratizing the field, making advanced research techniques more accessible to a broader range of organizations and professionals. Our report indicates that while 68% believe AI will impact analysis and synthesis, only 18% think it will affect co-presenting findings, highlighting the enduring value of human interpretation and communication of insights.

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At Optimal Workshop, we're excited about the possibilities AI brings to UX research. We envision a future where AI-powered tools can lower the barriers to entry for conducting comprehensive UX research, allowing smaller teams and organizations to gain deeper insights into their users' needs and behaviors. This democratization could lead to more user-centered products and services across various industries, ultimately benefiting end-users.

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However, as we embrace these technological advancements, it's crucial to remember that the core of UX research remains fundamentally human. The unique skills of empathy, contextual understanding, and creative problem-solving that human researchers bring to the table will continue to be invaluable. As we move forward, UX researchers must stay informed about AI advancements, critically evaluate their application in research processes, and continue to advocate for the human-centered approach that is at the heart of our field.

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By leveraging AI to handle time-consuming tasks and uncover patterns in large datasets, researchers can focus more on strategic interpretation, ethical considerations, and translating insights into impactful design decisions. This shift not only enhances the value of UX research within organizations but also opens up new possibilities for innovation and user-centric design.

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As we continue to develop our platform at Optimal Workshop, we're committed to exploring how AI can complement and amplify human expertise in UX research, always with the goal of creating better user experiences.

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The future of UX research is bright, with AI serving as a powerful tool to enhance our capabilities, democratize our practices, and ultimately create more intuitive, efficient, and delightful user experiences for people around the world.

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