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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Free Learning · Better Decisions Make Growth Decisions
Make Growth Decisions
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Know what to test, when to trust the result, and what to do next. Practical guides for analysts, growth teams, and founders.
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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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Why Most Automation Systems Fail (And How To Build One That Actually Works)
Automation systems fail because they never activate correctly, not because they're incomplete. Build for activation first, execution second.
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Personality Tests Don't Fix Execution: What Actually Improves Behavior For High-Variance Operators
Personality tests give you a label. Labels don't change behavior. What works: functional bottlenecks, hard constraints, and feedback loops.
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Why Early SaaS Founders Build The Wrong Thing (And How To Fix It Before It Kills Your Product)
Early SaaS founders perfect architecture for products nobody uses. The fix: find the first value moment before you build anything else.
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Why Your Search-Enabled AI Lies To You (And How To Force It To Tell The Truth)
Your AI optimizes for speed, not truth. Here's why it confidently lies about real-time data and the prompting fixes that force verification.
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Zero-Trust Marketing: How To Build Credibility When Everything Looks Like AI Slop
In an era of AI-generated content, proof of work is the only currency of trust. Shipped code and public failures can't be faked.
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The Fractional Co-Founder Model: Why Solo Builders Are Replacing Partners With AI Agents
The 50/50 co-founder split is a legacy risk. Solo founders now use AI agents as fractional hires — keeping 100% equity until product-market fit.
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