The Veblen Vodka: How Sidney Frank Sold $2 Billion of Status by Making Vodka More Expensive
In 1996, an American who didn't speak French and didn't drink vodka set out to build the world's most expensive vodka brand.
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Writing · Lean Experiments
Revenue-First
Revenue-First
Experimentation
Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
The Experiment Layer Is Moving Closer to the Money
Something interesting happened quietly in a recent ad platform API release: experiment statistics got pulled directly into the same reporting layer as
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Statistical Significance Is Not the Finish Line
Most teams treat the moment a test hits significance like a gun going off at the end of a race. The experiment reaches p<0.
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How to Write A/B Test Hypotheses That Pass the Falsifiability Test
Most stakeholder-submitted hypotheses describe a goal ("make X clearer") instead of an intervention.
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The Test-Everything Trap: Why Running More Experiments Doesn't Mean Learning More
There's a moment in most experimentation programs when volume becomes the goal. The team hits a rhythm. The tooling is set up.
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The 5-Element Diagnostic Checklist Every New Testing Team Should Standardize
A bare point estimate is uninterpretable. This guide walks through the 5 elements that should appear on every test readout — power analysis, MDE, confidence…
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5 A/B Testing Mistakes That Derail New CRO Teams (And How to Avoid Each One)
Five statistical mistakes that derail new DTC testing teams, with practical checks for sample size, peeking, metrics, and interpretation.
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How to Read CRO Case Studies as a New Analyst (A Practical Reading Guide)
A five-step method for learning from CRO case studies without mistaking curated success stories for representative evidence.
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Regression to the Mean in A/B Tests: A Practical Guide
A plain-English guide to why selected extreme A/B test results often shrink, and why no universal discount can recover the true effect.
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Early Stopping in A/B Tests: A Complete Guide for New CRO Analysts
How outcome-dependent stopping changes A/B test error rates, why there is no universal peeking multiplier, and how to precommit a valid stopping rule.
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Pricing Page Anatomy: Why Your Decoy Probably Doesn't Work
The standard 3-tier pricing-page playbook — anchor + decoy + "Most Popular" badge — works under specific conditions. Here are the ones that break.
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Bright Patterns: The Ethical Alternative to the Dark Pattern Catalog
Dark patterns optimize for the next quarter. Bright patterns optimize for the next decade. The 12 alternatives, and why your A/B test will misjudge them.
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Dark Patterns: The 12-Pattern Taxonomy and What They Cost Your Brand
Princeton found dark patterns on 11% of shopping sites. The FTC, EU, and California now regulate them. The 12-pattern taxonomy and what each costs you.
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The Modern Don't Make Me Think Stack: Usability for 2026
Krug's usability principles still hold — but the cost of misapplying them now compounds in places dashboards cannot see. The complete Krug-2026 reading list.
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Above-the-Fold CTA Hierarchy: How Many CTAs Is Too Many?
A practical framework for deciding which above-the-fold CTAs serve distinct intent and which create competition worth testing.
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Click-to-Conversion Ratio: The CTA Diagnostic Most Teams Ignore
Use click-to-conversion ratios to find wrong-intent clicks, destination friction, and cannibalization hidden by aggregate CTA reports.
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CTA Cannibalization: When a Small Lift Hides a Net Loss
Use placement-level conversion and source intent to test whether a directional CTA result is genuinely additive or cannibalized.
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Copy Intent vs Visual Prominence: Why "Sign Up" Doesn't Mean "Shop"
Separate CTA visibility from intent match by comparing click-through, destination completion, and source-page context together.
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Inside a Large A/B Test Ledger: What CRO at Scale Looks Like
Lessons from a historical enterprise testing ledger, with explicit limits on the denominator and no universal win-rate or device-performance benchmark.
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Mobile vs Desktop CTA Asymmetry: When the Same Test Tells Two Stories
How pre-specified mobile and desktop analysis can reveal heterogeneous CTA effects without claiming that device segmentation universally raises win rates.
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Time-on-Page as a Friction Signal: When Faster Is Actually Better
Interpret time-on-page with conversion, scroll depth, and interaction data to separate faster decisions from abandonment or confusion.
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Trust Badges: Why Ambiguity Can Be Worse Than Absence
Use behavioral diagnostics and explanatory content to test whether a benefit badge reduces uncertainty or adds friction.
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The A/B Testing Mistake That Feels Like Progress
Most teams discover A/B testing and immediately want to run more experiments. The logic seems sound: more tests mean more data, more data means better
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'Not Significant' Doesn't Mean Your A/B Test Failed — It Means You're Uncertain
The most expensive misreading in A/B testing is treating 'not statistically significant' as 'no difference.' It actually means 'we didn't collect enough…
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