Make Growth Decisions You Can Defend

Building an Experimentation Knowledge Base for High-Velocity Growth Teams: The Knowledge Compounding Rate

A knowledge base doesn't just store past experiments — it's how data beats the HiPPO in decisions.

Experimentation Management Systems: The Process Maturity Index for Mature Teams

Running a lot of A/B tests isn't maturity. Maturity is when the tests start showing up in the P&L.

How to Run Meta-Analysis Across Historical A/B Test Data: The Aggregation Validity Threshold

Meta-analysis isn't about combining experiments — it's about knowing when you have enough similar tests for the aggregate to tell you something true.

Turning 100 Experiments Into Strategic Insight: The Insight Density Curve

The value of the 50th experiment isn't the same as the value of the 5th.

How to Prevent Institutional Knowledge Loss in Your A/B Testing Program: The Tribal Dependency Index

The worst habit that kills institutional memory isn't forgetting to document. It's letting directional reads get filed as wins.

archiveApr 20, 20267 min read

Resurfacing Old A/B Tests: A System for Faster Iteration Cycles and the Revival Value Formula

Most old A/B tests contain insights your team no longer remembers. The Revival Value Formula tells you which ones are worth revisiting.

croApr 19, 20268 min read

CRO Knowledge Management: Building Institutional Memory Against the Knowledge Half-Life

The Knowledge Half-Life framework measures how fast insights decay in experimentation programs.

Organizing Experiments Across Product

Silos between experimentation teams aren't a culture problem — they're an economics problem. The Coordination Tax Ratio reveals the hidden 20-35% cost most orgs pay.

Experimentation Governance: Managing SRM, False Positives, and Bias

Statistical failures compound into credibility damage. The Statistical Trust Deficit framework explains why rigor in SRM detection and false positive…

Pre-Test and Post-Test Calculators: Statistical Guardrails and the Cost of Statistical Debt

The Statistical Debt framework shows how underpowered tests and post-hoc metrics compound silently — until one shipped false positive costs the team years…

Designing a Scalable Experiment Tracking System: The Learning Compound Rate

Scalability in experimentation isn't test volume — it's the Learning Compound Rate: how much past learning your team can still apply.

ab-testingApr 19, 20268 min read

When to Upgrade From Spreadsheets to an Experimentation Platform: The Economics of Migration

The Experimentation Overhead Ratio (EOR) framework tells you when the math says migrate off spreadsheets. Most teams wait 6-18 months past the breakeven point.

ab-testingApr 19, 202610 min read

A/B Testing: The Complete Guide for Practitioners

A program-level A/B testing guide from someone who has run 100+ experiments per year at a Fortune 150 company.

croApr 11, 20267 min read

Experimentation ROI vs. Brand Marketing ROI: Compare the Evidence

A practical way to compare experimentation, brand, and performance marketing evidence without turning modeled impact into a guarantee.

The Experimentation Win Rate Myth: Why Context Beats a Benchmark

How to report experiment win rates with a visible denominator, decision rule, evidence quality, business impact, and explicit limits.

The Politics of A/B Testing: How Results Get Spun and How to Stop It

How pre-committed metrics, decision rules, and a shared evidence record keep A/B test interpretation from drifting after results arrive.

ab-testingApr 11, 20269 min read

Post-Hoc Metric Shopping Is Killing Your Experimentation Program

Why choosing metrics after seeing results creates false confidence, and how pre-commitment protects an experimentation program.

ab-testingApr 11, 202610 min read

The Real-World Constraints of A/B Testing That No Article Talks About

How to make defensible experiment decisions with limited traffic, small teams, operational constraints, and stakeholder pressure.

experimentationApr 11, 202614 min read

What Breaks First When You Scale Experimentation

Scaling experimentation breaks intake, hypothesis integrity, capacity, and tooling — usually in that order. What fails at 20, 100, and 1,000 tests a year.

Vanity Metrics vs. Revenue Metrics: What Your A/B Tests Should Actually Measure

How to distinguish diagnostic engagement metrics from decision metrics and document the path from an experiment to revenue evidence.

What I Tell New Analysts Who Think 50/50 Splits Need to Be Perfect

Why imperfect A/B traffic splits are normal, when to suspect sample ratio mismatch, and which diagnostic and follow-up checks to run.

Why Every Company Has a Different Data Dictionary (And Why That Matters for Experimentation)

How to audit the real definitions behind users, sessions, conversions, and revenue before trusting an experiment result.

10 A/B Testing Best Practices I Learned Running 100+ Experiments a Year

Most A/B testing advice is written by people who've never defended a losing test in a business review.

aiApr 10, 202618 min read

The Death Of The Wrapper: Why The Future Of AI Is Vertical, Not Universal

The AI wrapper era is over. Solo builders shipping hyper-specific vertical tools win — not another ChatGPT skin with a logo on it.