Experimentation & Growth Metrics Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
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.
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.
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.
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.
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.
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.
Metric Frameworks & Guardrails
Designing a coherent metric framework to steer a product: defining a north-star metric, building metric hierarchies, distinguishing leading from lagging indicators, and aligning metrics to goals so that what is measured drives the intended behavior while avoiding vanity or easily-gamed metrics. Covers the guardrail side of the same design work: defining guardrail metrics, detecting negative side effects, and reasoning about tensions between competing metrics where a win on one degrades another. The scope is choosing, structuring, and safeguarding a metric system, not diagnosing a specific movement.
Metric Diagnosis & Segmentation Analysis
Investigating why a metric moved: root-causing a spike, drop, or plateau by decomposing it across segments and dimensions. Covers segmentation, cohorting, Simpson's-paradox traps, and distinguishing a real change from seasonality or a tracking artifact. The scope is diagnostic metric analysis rather than choosing which metric to track.
Conversion Funnel Optimization
Analyzing and improving a multi-step conversion funnel: mapping the journey, quantifying drop-off at each stage, and diagnosing where and why users fall out. Covers conversion-rate optimization tactics, landing-page and flow improvements, and structuring an optimization program. The concept scope is funnel analytics and the levers that raise stage-to-stage conversion.