Analyzing Case Study Findings

You ran the sessions. You found the themes. The insights feel right. But before you present, a quiet question lingers, did I go deep enough? Did I check the right things? This is the part of qualitative UX research we don’t always emphasize. Not just doing the work with care, but supporting it with structure. Adding rigor isn’t about questioning your effort - it’s about strengthening your insights. It brings clarity, consistency, and confidence - for you, your team, and anyone who’ll act on what you’ve found. Here are eight practical ways to add that kind of rigor without slowing your work down. Start with triangulation. Don’t rely on just one type of data. Pair interviews with usability testing, behavior logs, or survey responses. Ask another researcher to take notes independently and compare interpretations. This builds confidence that your insights reflect more than one lens. Maintain an audit trail. Keep a record of key decisions, theme changes, or shifts in scope. Use a shared doc, spreadsheet, or even versioned codebooks. Others should be able to see how your findings evolved- not just the end product. Practice reflexivity. Before analysis, write down what you expect to find. During synthesis, notice when your background might be influencing what feels important. If you’re working in a team, make this a shared habit. You’re part of the instrument, and that’s worth tracking. Use member checking. Once your findings are drafted, send a summary to a few participants and ask if it reflects their experience. Their feedback will tell you where you’ve nailed it- and where you need to dig deeper. Use structured frameworks. Lincoln and Guba’s trustworthiness criteria are great for longer studies. The PARRQA checklist helps keep fast-paced projects grounded. Either way, frameworks give your work consistency and make your choices visible. Look for negative cases. Instead of just confirming patterns, search for outliers. Find the participant who doesn’t fit the theme. Revising your analysis to include their story makes your findings more durable. Make your insights transferable. Don’t stop at “users want X.” Add who those users were, what tools they used, and what constraints they faced. When findings are rich in context, teams can apply them more confidently. Document key decisions as they happen. Use a shared log or notes thread. Track sampling shifts, analysis changes, design pivots. Later, include this in your final report. It shows how you got from raw data to real insight- and helps others trust it. Rigor isn’t about adding more work - it’s about adding more strength. Even a few thoughtful checks, built into your workflow, can make your qualitative UX research clearer, more credible, and easier to stand behind when the pressure’s on.

“When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.”

Case studies are a great way to identify and solve real-life problems. Here's today's topic: → 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗖𝗵𝘂𝗿𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀. I'll explain what I do ... from when I get the data to when I share my insights and recommendations. But first ... Why customer churn analysis? All companies should care about one thing → Analyzing why customers stop using their products/services. So, what do I do? Here's a 4-step guideline: → 𝗦𝗲𝗻𝘀𝗲 𝗖𝗵𝗲𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮   → What data am I using?   → In which file format is this data?   → Is there anything I don't understand? → 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮   → What's the shape of the dataset?   → Which fields (columns) does it have, what are the data types, and which values are on the records (rows)? I don't rush this process because I need to familiarize myself with the data I'm working with. Throughout my career, I've learned a valuable lesson: if you try to skip this section, you'll regret it later. Trust me. → 𝗠𝗲𝘁𝗿𝗶𝗰𝘀   → I'm going directly to my objective → analyze customer churn. So there's really just one key metric (Churn rate). Then, I break this down into smaller steps. (example: to get the churn rate, I must have the total number of customers and the total number of churned customers). → 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀   → I focus on segmenting the data with 2 variables involved ...   → Psychographics   → Demographics ... because the purpose of this work is to identify the ideal "churned customer."   → Are there any significant differences in churn rates between countries?   → Is there an age group that has the highest churn rate?   → Does the churn rate differ significantly between card types? These lay the foundation of all my visualizations. → 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴   → You could say: Germany has the highest churn rate, with 20% more than other countries. But it's better if you add a story behind it to justify the numbers: "Germany has the highest churn rate, but it’s important to note that it also has the largest population of males aged 24-35, a group that typically shows higher churn rates. This context might explain why Germany’s churn rate is higher than that of other countries." I hope that this simple case study helps you. Which domain do you work with / are interested in working on? Have you ever done a case study? Let me know!