#product-analytics

Set up comprehensive funnel analytics to optimize conversion. Define key funnels: 1. Acquisition: landing page → signup → activation. 2. Conversion: trial start → paid conversion. 3. Engagement: login → core action → return visit. Track events: use event-based analytics (Amplitude, Mixpanel) not just pageviews. Event properties: user_id, timestamp, device, traffic source, feature variant. Conversion benchmarks: signup to activation 20-40%, trial to paid 15-25%, varies by industry. Analysis techniques: cohort analysis (retention over time), segmentation (power users vs. casual), funnel drop-off identification. Actionable insights: if 60% drop from signup to first use, focus on onboarding. A/B testing: experiment with different funnel steps. Reporting: weekly dashboards, monthly deep-dives, quarterly strategy reviews.

I want to run an A/B test on our e-commerce website's product detail page to increase the "add to cart" rate. The current button is blue and says "Add to Cart". Generate three different hypotheses for an A/B test. For each hypothesis, specify the change you would make (e.g., button color, text, placement) and the expected outcome.

Build comprehensive analytics infrastructure for data-driven decisions. Analytics architecture: 1. Data collection: event tracking, user interactions, system metrics. 2. Data pipeline: ETL processes, data validation, transformation. 3. Data warehouse: centralized storage, dimensional modeling. 4. Business intelligence: dashboards, reports, self-service analytics. Key metrics framework: 1. Acquisition: traffic sources, conversion rates, cost per acquisition. 2. Activation: onboarding completion, time-to-first-value, feature adoption. 3. Retention: DAU/MAU, cohort retention, churn analysis. 4. Revenue: ARPU, LTV, conversion rates, expansion revenue. 5. Referral: viral coefficient, NPS, organic growth rate. Reporting strategy: 1. Executive dashboards: KPIs, trends, alerts. 2. Product dashboards: feature usage, user flows, experimentation results. 3. Operational reports: performance monitoring, error tracking. Tools: Segment for data collection, Snowflake for warehousing, Tableau for visualization. Data governance: quality monitoring, access controls, privacy compliance, documentation standards.