Context
Product and CRO teams regularly introduced new features and changes intended to improve key customer outcomes such as conversion after trial, activation or retention.
The challenge was not only to track whether these metrics moved, but to understand whether a change had actually caused the improvement.
Approach
I used funnel and customer journey analysis to identify where users dropped off and where the largest opportunities for improvement existed.
Based on these patterns, together with research and product context, we developed hypotheses about which changes might improve the experience.
For CRO initiatives, selected ideas were sometimes pre-tested with users before running an experiment. This helped uncover misunderstandings, validate the underlying proposition and prioritize the most promising hypotheses rather than testing every idea indiscriminately.
For product changes where causal impact mattered, I designed and evaluated A/B tests with clearly defined hypotheses, success metrics and relevant guardrail metrics.
The results were then analyzed to determine whether observed differences in conversion, activation or retention could reasonably be attributed to the tested change rather than to other changes in the customer mix or environment.
Key Insight
Outcome
The approach helped teams identify meaningful optimization opportunities, prioritize stronger hypotheses and make product and CRO decisions based on causal evidence rather than before-and-after comparisons alone.
Methods
Funnel Analysis · Customer Journey Analytics · Hypothesis Development · CRO Research · Concept Pre-Testing · Experiment Design · A/B Testing · Statistical Evaluation