Experimentation & Growth Metrics Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Segmentation Scheme Design and Governance
Designing and governing the segmentation schemes used to analyze product and business metrics. Covers choosing which dimensions materially affect a metric, cardinality handling and the bucketing of continuous dimensions, correlated and redundant dimensions, RFM versus clustering versus rule-based segmentation, granularity against statistical power, and canonical segment definitions that stay stable across analyses. The scope is how a segmentation is built, chosen and maintained, not diagnosing why a specific metric moved.
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.
Experiment Prioritization & Roadmap
Running experimentation as a program: building and ranking a test backlog, prioritizing ideas by expected impact and effort, and sustaining experimentation velocity and iteration cadence. Covers scaling and rolling out winning variants, and the learning loop that feeds the next round of tests. The scope is the operating rhythm of a testing program, not the design of any single test.
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.
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.
Advanced Experimentation Designs
Experimentation techniques beyond the simple two-arm A/B test: causal inference and quasi-experiments, sequential and always-valid testing, multi-armed bandits, factorial and multivariate designs, and handling interference or network effects. Covers when randomized experiments are infeasible and difference-in-differences, instrumental variables, or switchback designs apply. The concept scope is methodology selection for hard experimental situations.