10 articles
AcquisitionHow to build a structured creative testing system for SaaS paid social that identifies winners quickly, prevents audience fatigue, and turns learnings into compounding institutional knowledge.
Product ManagementHow SaaS teams define and monitor guardrail metrics that stop growth experiments from improving one number while silently degrading retention, revenue, or product quality.
Product ManagementA practical guide to multiple comparisons correction, experiment sequencing, and false discovery rate control for SaaS teams running high-velocity experimentation programs.
PLGA quantitative framework for prioritizing the PLG experiment backlog by conversion leverage — calculating expected impact on trial-to-paid conversion, retention, and expansion, then sequencing experiments by leverage, not intuition.
PricingHow to architect pricing experiments that expose only new cohorts, define grandfathering mechanics before rollout, avoid segment-mix confounds, and measure results with the correct observation window.
RetentionRetention experiments are structurally harder than acquisition experiments because the outcome variable has a 12-month lag. This framework covers leading-indicator proxies, matched-control experiment design, and the sample-size constraints most teams hit before they realize it.
Product AnalyticsA rigorous cost and quality analysis of building versus buying an experimentation platform, including break-even math by experiment volume, the statistical capabilities that matter most, and when each option is the right choice.
PricingBuild pricing A/B tests that produce defensible conclusions. Covers hypothesis framing, metric selection, sample sizing, runtime control, and common design failures that invalidate results.
PricingA structured backlog of pricing page experiments ranked by expected impact, implementation complexity, and evidence strength. Covers plan presentation, comparison tables, CTA copy, social proof, FAQ placement, and guarantee design.
PricingCalculate the sample size required to detect pricing test effects with statistical confidence. Covers power analysis fundamentals, the effect of baseline conversion rate on required sample, runtime estimation, and the cost of underpowered tests.