Experimentation & Growth Metrics Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Product-Led Growth & Self-Serve Funnels
Growth driven by the product itself: self-serve signup and onboarding funnels, free-to-paid conversion, and in-product mechanics that acquire and expand users without sales touch. Covers instrumenting and optimizing the self-serve journey and the metrics that gauge a product-led motion. The concept scope is the PLG model and its funnels.
Experiment Analysis & Result Interpretation
Reading out an experiment after it runs: interpreting the treatment effect, deciding ship/no-ship, and reconciling conflicting or flat results. Covers reasoning under uncertainty, acting on inconclusive or limited data, and translating a measured effect into a business decision. The emphasis is turning experiment output into a defensible recommendation.
User Retention & Engagement
Measuring and improving how users stick with a product after they first convert: retention rate and cohort retention curves (day 1, day 7, day 30, and longer horizons), churn rate, reactivation and resurrection of lapsed users, and engagement-depth signals such as DAU, WAU, MAU, and the DAU/MAU stickiness ratio. Covers defining and computing these metrics, including basic SQL and event-log implementations, diagnosing where and why a retention curve breaks (onboarding gaps, feature-adoption gaps, seasonal or cohort-specific effects), designing experiments and product interventions that deepen habitual usage, cohort-based lifetime value (LTV), and communicating retention findings to stakeholders. This is the post-acquisition, repeat-usage side of the user lifecycle: getting a first-time visitor through signup or first purchase is covered by the companion topic Conversion Funnel Optimization, and pure-SQL implementation depth at large scale (approximate-distinct computation, survival-curve modeling, multi-state subscription churn state machines, and standalone timezone-aware day bucketing) is covered by the companion topic Advanced SQL: Metric Monitoring, Anomaly Detection, and Data Correctness at Scale.
Growth Metrics & Unit Economics
The core quantitative vocabulary of growth: activation, retention, referral and revenue metrics, growth-accounting frameworks such as AARRR, and unit economics including LTV, CAC, and payback period. Covers defining these metrics precisely and computing growth calculations that reveal whether growth is efficient and durable. The scope is metric definitions and economic math, not go-to-market execution.
Growth Experimentation & Tactics
The tactical, iterative side of growth: rapid ideation of unconventional levers, viral and referral loops, and incentive design that changes user behavior. Covers the growth-hacking mindset of cheap, fast tests to find scalable channels and mechanics. The scope is generating and testing growth tactics, distinct from formal statistical experiment design.
A/B Test Design & Statistical Rigor
Designing and statistically defending a controlled online experiment: framing a testable hypothesis, defining control and treatment variants, choosing the randomization unit, setting the primary success metric, and computing sample size, power, and minimum detectable effect. Covers the statistical foundations that make a readout trustworthy, including hypothesis testing, p-values, confidence intervals, statistical vs practical significance, and Type I/II error. Emphasizes avoiding the common pitfalls that invalidate a test, such as peeking, multiple-comparison inflation, underpowered designs, and how test duration and stopping rules affect the validity of conclusions.
Conversion Funnel Optimization
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.
Growth Strategy & Prioritization
Setting direction for growth: identifying constraints and bottlenecks, choosing which growth levers to pull, and sequencing a roadmap across short- and long-term horizons. Covers prioritization frameworks, portfolio balancing across bets, and scaling proven initiatives. The concept scope is strategic planning and prioritization of growth work, independent of any specific company or vertical.
Feature Success Measurement
Judging whether a shipped feature worked: defining success criteria before launch, measuring adoption and impact, and separating a feature's effect from background trends. Covers post-launch readouts, tying a feature to a target metric, and deciding whether to iterate, keep, or roll back. The scope is evaluating feature impact rather than designing the test that produced it.