Experimentation & Growth Metrics Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
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.
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.
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.
Attribution & Conversion Measurement
Measuring what drives a conversion: event tracking and instrumentation, attribution models (first-touch, last-touch, multi-touch), and connecting user actions to outcomes. Covers the analytics plumbing for reliable conversion measurement and the limits of each attribution approach. The scope is the measurement layer for conversions, not the creative or channel strategy that generates them.
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.