Conversion Funnel Optimization Questions

Analyzing and improving a bounded, ordered conversion path: mapping the sequence of steps a user takes from acquisition through one terminal conversion or activation event (signup, first purchase, first paid order, trial-to-paid, onboarding to first-success), computing step-to-step and overall conversion rates and drop-off, and diagnosing where and why users fall out. Covers the SQL and query techniques for computing funnel metrics at scale (stage-by-stage conversion tables, time-to-conversion and time-to-first-value, cohort LTV measured within a funnel window, path analysis across non-linear user journeys, event instrumentation and data-quality practices for funnel tracking), attribution modeling for crediting conversions across channels and touchpoints (first-touch, last-touch, linear, time-decay, Markov-chain, and Shapley-value approaches) and customer acquisition cost by channel, and the experiment design and statistics used to validate funnel changes (A/B and multi-armed-bandit test design, sample-size and power calculations, quasi-experimental methods such as difference-in-differences and synthetic control when randomization is not possible, and testing whether a single funnel-stage drop is a real, statistically significant shift rather than noise). Also covers diagnosing UX and flow friction that causes drop-off (checkout, signup, and onboarding friction points) and prioritizing a program of funnel-improvement experiments (impact and effort frameworks such as RICE or ICE, guardrail metrics, roadmap sequencing). Distinct from User Retention and Engagement, which covers what an already-converted or already-activated user does afterward: repeat usage over time, cohort retention curves, DAU/WAU/MAU, churn, and reactivation. A question belongs here if it concerns a user's first, bounded pass toward one conversion or activation event; it belongs to User Retention and Engagement if it concerns recurring behavior after that event. General-purpose rolling-window anomaly and change-point detection techniques (CUSUM, Bayesian change-point, seasonality-aware baselines) for monitoring any metric over time belong to the companion topic Advanced SQL: Metric Monitoring, Anomaly Detection, and Data Correctness at Scale, not here.