Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
Minimum detectable effect (MDE) is the most important input to A/B test design. Learn how to calculate and choose the right MDE for business impact and traffic.
How to use an A/B test sample size calculator: the four inputs, minimum sample size per variant, MDE sensitivity, and what to do when traffic is too low.
One honest experiment result can get a growth program funded or gutted. The fix isn't better reporting — it's pre-registration, borrowed from clinical trials.
Repeat visitor tests can lie with a straight face. The dashboard says variant B won, but what it may have found is memory, not lift.
The dream of a 'master orchestrator' that auto-picks the cheapest model that can do each coding task is a research spiral, not a shortcut.
There's no single best AI coding tool — the largest study of real merged pull requests found no universal winner.
One-shotting a feature with AI isn't luck — it's a method. The goal isn't the prettiest first draft; it's the fewest total tokens to a change that compiles…
If every test feels urgent, you do not have a broken experimentation strategy. You have a decision quality problem. Most B2B SaaS teams are not short on
Most token-saving advice for Claude Code quietly makes the agent worse — starve its context or drop to a cheap model and you pay for the retries.
Six UK hotel-booking sites — and 25 more that followed — resolved a CMA consumer-protection investigation without paying a single pound in penalties.
A compliant-looking cookie banner and a compliant cookie banner aren't the same thing — the difference is measured in click counts and visual weight, not…
A four-page, six-click, fifteen-option cancellation sequence didn't happen by accident — it happened because a retention metric and a simplicity proposal…
A good UX instinct — don't interrupt the flow of the experience — produces a different outcome when the action being smoothed is a real-money purchase…
Credit Karma's 'pre-approved' claim wasn't a lie — it was an A/B test winner.
FTC and international regulators don't just allege dark patterns anymore — their complaints now include the internal emails, A/B test data, and executive…
The lift in your test report and the lift finance sees a quarter later rarely match. Winner's curse, novelty decay, and regression all shrink it.
GA4, your server-side pipeline, and your BI tool report different numbers, and teams cite whichever fits. Why reconciliation is skipped, and the fix.
When three teams each claim the same conversion, attributed revenue exceeds reality and budget follows the best dashboard, not the best channel.
Enterprises govern reversible A/B tests like irreversible decisions, and velocity dies in the approval queue. The reframe that unlocks it.
Old experiment flags never get cleaned up, quietly contaminate new tests, and occasionally reactivate dead code. The carrying cost of zombie experiments.
An A/B test can show a clean aggregate win while the variant loses in every real segment. Simpson's paradox, why the topline lies, and the fix.
Switching A/B testing tools silently redefines your metrics and breaks historical comparability. What the sales demo never shows, and what to check first.
A winning A/B test isn't a shipped feature. The gap between the tested variant and what actually reaches production is where the value leaks away.
Some SaaS changes should raise revenue. Others should simply not break it. A billing flow rewrite, navigation cleanup, design system migration, or applied
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