The Automation Trap: Why AI Won't Fix Your A/B Testing Culture
There's a pattern worth noticing every time a new category of "automated optimization" software launches: the marketing promises to replace the hard
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Writing · Lean Experiments
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Experimentation
Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
The AI A/B Testing Promise Has a Behavioral Science Problem
Every few months, a new platform promises to automate conversion optimization. The pitch is always the same: remove the human bottleneck, run more tests
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Adaptive Algorithms vs. Fixed-Sample A/B Tests: Why I Changed How I Run Experiments
After 100+ experiments per year, fixed-sample A/B testing's opportunity cost became impossible to ignore.
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A/B Test Repository Architecture
A/B test repositories don't fail because the schema is wrong. They fail because nobody can find what they need fast enough.
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A/B Testing Documentation Framework
The point of documenting experiments isn't to record what happened. It's to make the next similar hypothesis sharper.
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How to Avoid Repeating Failed Experiments: The Failure Recurrence Rate
Repeated failed experiments aren't a sign of ambition — they're a sign your team isn't reading its own archive.
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Best A/B Test Library Software: The Tool Fit Matrix for Evaluation
The best A/B testing platform isn't a single tool — it's the one that fits your team's scale, statistical needs, integration stack, and cost curve.
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Centralized A/B Testing Database Design: The Trace Ratio That Predicts Program Health
A centralized A/B testing database is only as useful as the fraction of experiments you can fully reconstruct.
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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.
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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.
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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.
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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.
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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.
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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.
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CRO Knowledge Management: Building Institutional Memory Against the Knowledge Half-Life
The Knowledge Half-Life framework measures how fast insights decay in experimentation programs.
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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.
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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…
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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…
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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.
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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.
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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.
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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.
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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.
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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.
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