Independent analytics case study · product research
An end-to-end product analysis that paired behavioral data with primary research to isolate the experience issue most responsible for low satisfaction.
Context: The product's low satisfaction score made it unclear whether to invest in new functionality or repair the core experience. My role: Independent analyst responsible for combining behavior, survey, and interview evidence into a prioritized product decision.
What was actually driving low satisfaction - and which product investment should come first?
Research synthesis
Satisfaction signal
Technical reliability was the clearest driver of low satisfaction: users with high error frequency scored the experience 3.7/10, compared with 6.8/10 for users with low error frequency. The recommendation was to prioritize reliability before expanding features.
After reliability improvements, measure error rate, satisfaction, task completion, time to first value, support volume, and retention together. The test is whether a more reliable core experience changes the customer outcome.