Product analytics and experimentation · Large-scale retail e-commerce
Using product analytics to improve a high-scale e-commerce experience
Combining KPI design, behavioral analysis, experimentation, and executive reporting to guide product decisions on an e-commerce platform generating more than $30M in monthly revenue.
The situation
A high-scale retail e-commerce platform generating more than $30M in monthly revenue needed product and business teams to understand how customers were responding to features across the shopping journey.
The design question
How could behavioral data help teams decide what to improve next without treating every movement in a metric as proof of causation?
My contribution
I partnered with product managers to define success metrics and measurement frameworks for product initiatives. I used SQL, Python, and Power BI for exploratory and descriptive analysis, then applied A/B testing, cohort analysis, and funnel analysis to evaluate feature performance and customer behavior.
The system
The work connected product questions to measurable behaviors, segmented journeys, experiment outcomes, and executive reporting. Findings were brought into feature prioritization and roadmap conversations rather than left inside an analytics deliverable.
Outcome and lesson
The analysis contributed to product improvements associated with a 40% increase in user engagement and helped improve delivery time by 5% to 12%. The lesson was that analytics creates value when it changes the next product decision.