Cookie Consent Bias in A/B Tests, and How I Correct It
Your test can look clean and still be wrong. If analytics starts only after consent, you are not measuring visitors. You are measuring the subset willing
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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.
Cluster Randomized Tests for B2B SaaS Accounts
When I review a B2B SaaS test plan, I start with one question: can people inside the same account affect each other? If the answer is yes, user-level A/B
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How I Handle Revenue Outliers in A/B Tests
A test can look like a winner because three customers showed up with a corporate card. I've seen teams ship bad changes, celebrate the lift, then spend a
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Experiment Naming Conventions for Search and Reporting
If your team can't find a winning test in 30 seconds, the name is broken. I care about experiment naming conventions because I've watched bad names slow
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SaaS Checkout Optimization: A/B Tests That Cut Friction
Most SaaS checkouts do not fail because the buyer suddenly stops wanting the product. They fail at the last minute when doubt beats momentum, which is why
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Ratio Metrics in A/B Testing Without Misreading Revenue Lift
A test can lift conversion and still hurt revenue. I have watched teams ship winners that looked great in the dashboard but weak in the finance review.
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Triggered Analysis for SaaS A/B Tests With Partial Exposure
You can run a clean test and still make the wrong call. I see it all the time in SaaS. The experiment is randomized, the stats look fine, and the
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Exposure Logging Attribution Makes A/B Test Results Trustworthy
Most bad A/B test calls are not statistics problems. They are measurement problems. I see the same mistake over and over.
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The Decision Latency Metric for Experimentation Teams
A test doesn't create value when the chart turns green. It creates value when somebody decides. I've seen teams run clean experiments, get solid analytics,
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Activation Metrics: How to Pick the One That Predicts Retention
Most teams track the wrong activation metric. A practitioner's guide to choosing an activation metric that statistically predicts retention, instrumenting…
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How to Write A/B Test Hypotheses That Actually Hold Up
A practitioner's guide to writing A/B test hypotheses — the structure that survives review, the three failure modes that produce inconclusive tests, and how…
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Regression to the Mean: Why A/B Test Wins Fade After Launch
The most dangerous SaaS test win is the one that looks clean, gets shipped fast, and fades a month later. I've seen teams forecast revenue off a headline
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The Commitment Trap: Why Forcing Users to Opt-In Destroys Conversions (and What Loss Aversion Actually Predicts)
An experiment requiring users to actively opt-in to autopay during plan selection caused a 15-20% drop in conversions.
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The Six-Figure Decision: How Strategic Price De-Emphasis Reveals the True Economics of Attention
When a consumer subscription business reduced the visual prominence of pricing during a high-price market period, conversions jumped 12-15% and generated…
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Behavioral Economics in Marketing: 10 Tactics Tested
Behavioral economics in marketing works best as hypothesis generation. Examine 10 tactics, their evidence, failure conditions, and what marketers should test.
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7 Real-World Examples of the Door-in-the-Face Technique (And When It Backfires)
Real examples of the door-in-the-face technique in marketing and sales - how the contrast principle drives compliance, and when the tactic backfires.
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Retail A/B Testing Isn't Broken — Your Prioritization Is
Walk into most retail optimization programs and you'll find the same thing: a backlog of tests that all feel urgent, a dashboard celebrating win rates that
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The Retail Experiment That Most Stores Are Running Wrong
Walk the floor of almost any retail operation running A/B tests, and you'll notice the same pattern: the tests are neat, the hypotheses are tidy, and the
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Retail A/B Testing Is Not the Same Game as SaaS Testing — and Most Playbooks Pretend It Is
Walk into any major retail site and look at how many decisions a shopper makes before completing a purchase. Product discovery. Filtering. Comparison. Sizing.
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The URGENT Trap: Why Smart People Spend Their Time on the Wrong Things
Zhu, Yang, and Hsee documented the Mere Urgency Effect in 2018 — subjects systematically chose objectively worse-rewarded tasks when those tasks were…
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The Endowment Effect Goes Bad: Where Behavioral Economics Crosses Into Manipulation
Kahneman, Knetsch, and Thaler's 1990 Cornell mug experiment proved that ownership roughly doubles perceived value.
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The Most Underrated Force in Marketing: What Subliminal Lipton Proved About the Mere Exposure Effect
Robert Zajonc's 1968 paper documented that humans like things more the more often they see them, even when the things are meaningless squiggles.
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The Anti-Apple: How Ron Johnson Took the Apple Playbook to JCPenney and Burned $1 Billion in 17 Months
In 2011, JCPenney hired the executive who'd built Apple's retail empire to do the same for them.
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The Goodbye You Forgot to Design: Why Most Brands Abandon Customers Right After the Sale
Festinger's 1957 cognitive dissonance research showed that the moment after purchase is when customers are most psychologically vulnerable.
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