Qual data’s tricky but worth it. We use basic tagging for user comments. Helps spot trends without drowning in details. Still mostly rely on numbers though.
Qualitative data’s a beast, but it’s where the real nuggets hide.
We use a hybrid approach. Quick pulse surveys for ongoing feedback, then deep dives every quarter.
Key is to have a system. We code responses into categories (UX, features, pricing) and track sentiment trends. Makes it easier to spot issues before they blow up in the metrics.
For analysis, we use AI tools to surface common themes. Saves time and catches stuff we might miss.
Balance is crucial. Use quant to see what’s happening, qual to understand why. Just don’t let analysis paralysis set in. Act on clear insights, keep testing everything else.
Totally feel you on the qualitative data struggle. It’s messy but gold.
For our dating app, we’d get swamped with user feedback. We started tagging comments by theme (UI issues, matching complaints, etc.) and tracking sentiment. Helped us spot patterns faster.
The real game-changer was recording user interviews. Watching someone fumble through our onboarding told us more than a month of analytics.
But yeah, it’s a time sink. We now do deep dives quarterly, not monthly. Rest of the time, we stick to key metrics and only dig into comments if numbers look weird.
One tip: Share the qual insights widely. Product team geeks out on numbers, but execs remember that angry user video way more.