088 - Doing UX Research for Data Products and The Magic of Qualitative User Feedback with Mike Oren, Head of Design Research at Klaviyo

Mike Oren, Head of Design Research at Klaviyo, joins today’s episode to discuss how we do UX research for data products—and why qualitative research matters. Mike and I recently met in Lou Rosenfeld’s Quant vs. Qual group, which is for people interested in both qualitative and quantitative methods for conducting user research. Mike goes into the details on how Klaviyo and his teams are identifying what customers need through research, how they use data to get to that point, what data scientists and non-UX professionals need to know about conducting UX research, and some tips for getting started quickly. He also explains how Klaviyo’s data scientists—not just the UX team—are directly involved in talking to users to develop an understanding of their problem space. Klaviyo is a communications platform that allows customers to personalize email and text messages powered by data. In this episode, Mike talks about how to ask research questions to get at what customers actually need. Mikes also offers some excellent “getting started” techniques for conducting interviews (qualitative research), the kinds of things to be aware of and avoid when interviewing users, and some examples of the types of findings you might learn. He also gives us some examples of how these research insights become features or solutions in the product, and how they interpret whether their design choices are actually useful and usable once a customer interacts with them. I really enjoyed Mike’s take on designing data-driven solutions, his ideas on data literacy (for both designers, and users), and hearing about the types of dinner conversations he has with his wife who is an economist ;-) . Check out our conversation for Mike’s take on the relevance of research for data products and user experience.    In this episode, we cover: Using “small data” such as qualitative user feedback  to improve UX and data products—and the #1 way qualitative data beats quantitative data  (01:45) Mike explains what Klaviyo is, and gives an example of how they use qualitative information to inform the design of this communications product  (03:38) Mike discusses Klaviyo data scientists doing research and their methods for conducting research with their customers (09:45) Mike’s tips on what to avoid when you’re conducting research so you get objective, useful feedback on your data product  (12:45) Why dashboards are Mike’s pet peeve (17:45) Mike’s thoughts about data illiteracy, how much design needs to accommodate it, and how design can help with it (22:36) How Mike conveys the research to other teams that help mitigate risk  (32:00) Life with an economist! (36:00) What the UX and design community needs to know about data (38:30)   Quotes from Today’s Episode “I actually tell my team never to do any qualitative research around preferences…Preferences are usually something that you’re not going to get a reliable enough sample from if you’re just getting it qualitatively, just because preferences do tend to vary a lot from individual to individual; there’s lots of other factors. ”- Mike (@mikeoren) (03:05) “[Discussing a product design choice influenced by research findings]: Three options gave [the customers a] feeling of more control. In terms of what actual options they wanted, two options was really the most practical, but the thing was that we weren’t really answering the main question that they had, which was what was going to happen with their data if they restarted the test with a new algorithm that was being used. That was something that we wouldn’t have been able to identify if we were only looking at the quantitative data if we were only serving them; we had to get them to voice through their concerns about it.” - Mike (@mikeoren) (07:00) “When people create dashboards, they stick everything on there. If a stakeholder within the organization asked for a piece of data, that goes on the dashboard. If one time a piece of information was needed with other pieces of

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Are you an enterprise data or product leader seeking to increase the user adoption and business value of your ML/AI and analytical data products? While it is easier than ever to create ML and analytics from a technology perspective, do you find that getting users to use, buyers to buy, and stakeholders to make informed decisions with data remains challenging? If you lead an enterprise data team, have you heard that a ”data product” approach can help—but you’re not sure what that means, or whether software product management and UX design principles can really change consumption of ML and analytics? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting product designer’s perspective on why simply creating ML models and analytics dashboards aren’t sufficient to routinely produce outcomes for your users, customers, and stakeholders. My goal is to help you design more useful, usable, and delightful data products by better understanding your users, customers, and business sponsor’s needs. After all, you can’t produce business value with data if the humans in the loop can’t or won’t use your solutions. Every 2 weeks, I release solo episodes and interviews with chief data officers, data product management leaders, and top UX design and research professionals working at the intersection of ML/AI, analytics, design and product—and now, I’m inviting you to join the #ExperiencingData listenership. Transcripts, 1-page summaries and quotes available at: https://designingforanalytics.com/ed ABOUT THE HOST Brian T. O’Neill is the Founder and Principal of Designing for Analytics, an independent consultancy helping technology leaders turn their data into valuable data products. He is also the founder of The Data Product Leadership Community. For over 25 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, NetApp, Roche, Abbvie, and several SAAS startups. He has spoken internationally, giving talks at O’Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast Experiencing Data, advises students in MIT’s Sandbox Innovation Fund and has been published by O’Reilly Media. He is also a professional percussionist who has backed up artists like The Who and Donna Summer, and he’s graced the stages of Carnegie Hall and The Kennedy Center. Subscribe to Brian’s Insights mailing list at https://designingforanalytics.com/list.