Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
Most pricing page tests die for a simple reason, they chase clicks instead of cash. When I work on saas pricing page testing, I care less about a prettier page and more about whether more visitors become paid users.
Teams double their experiment volume and cut their learning rate in half. Here is what actually breaks when experimentation programs scale — and why the real problem is cognitive, not mechanical.
Atticus Li shares how he scaled NRG Energy's experimentation program from 20 tests per year to 150+ total experiments across 7 brands, tying every test to revenue per customer and generating over $1.2M in projected annual lift.
Complete troubleshooting guide for the Optimizely visual editor not loading or working. Covers the 6 most common causes — Chrome extension issues, CSP/X-Frame-Options, HTTPS mismatches, SPA timing, JavaScript conflicts, and missing snippet.
The honest numbers on Optimizely's page speed impact — async vs. synchronous snippet, anti-flicker costs, Core Web Vitals effects, and how to measure and minimize the actual performance hit.
The complete diagnostic guide for Optimizely experiments showing zero or very low visitor counts. Covers the 7 most common causes with exact debugging steps for each.
The complete guide to diagnosing and fixing Optimizely flicker (Flash of Original Content). Covers all four fix types, performance tradeoffs, and a 3-step diagnostic to identify which fix you actually need.
Honest, specific comparison of 6 Optimizely alternatives — VWO, AB Tasty, Statsig, Convert, LaunchDarkly, and GrowthBook — with a decision framework to help you pick the right tool for your team.
Not a list of random test ideas. These are 10 high-ROI tests with hypothesis templates, realistic lift benchmarks, and what to test next after a win or a loss — built from 100+ experiments at NRG Energy.
A/B testing and MVT are not interchangeable. The difference between them — interaction effects, traffic requirements, and what you actually learn — determines which one you should run. Here's the framework.
"Let's test a bigger button" is not a hypothesis. Here's the full hypothesis template, 5 bad-to-good rewrites, and how a good hypothesis turns a losing test into your next win.
Most definitions of statistical significance are wrong — or at least misleading. Here's what p < 0.05 actually means, why 95% confidence is a convention not a law, and how to avoid the significance theater killing your CRO program.
There's no single number. But there is a rigorous framework. Here's how to calculate exactly how long your A/B test needs to run — and why stopping early is the most expensive mistake in CRO.
Most A/B testing roadmaps fail because they list tests, not hypotheses. This guide covers the four roadmap categories, ICE scoring, sequencing strategy, and what a mature 90-day roadmap actually looks like.
A practical guide to A/B test sample size calculations, with worked examples, common mistakes, and what to do when your traffic is too low. Covers multiple variations, MDE sensitivity, and how to avoid the '2-week arbitrary duration' trap.
Most teams stop A/B tests for the wrong reasons. This framework gives you four conditions to verify before calling a test — and explains the peeking problem, sequential testing, and what 'results stabilized' actually means.
MDE is the most underrated concept in A/B testing. Get it wrong and you'll run underpowered tests for months. This guide covers MDE calculations, real benchmarks by test type, and how to frame it as a business decision.
A practical comparison of Bayesian and frequentist A/B testing from a CRO practitioner who's run 100+ experiments. Covers the peeking problem, sequential testing, and a decision table for choosing the right approach.
The correct Optimizely setup sequence — snippet installation, A/A testing, custom events, naming conventions, and the 5 mistakes that create months of bad data.
The front door to the Optimizely Practitioner Toolkit. Find the right learning path based on where you are, avoid the 5 most common mistakes, and access all 24 practitioner guides organized by cluster.
Visitor-based vs session-based conversion counting, the exact math showing how it changes your reported rate, unique vs all conversions, how to audit your setup, and common bugs that inflate conversions.
The three Optimizely metric types explained for practitioners — when revenue per visitor beats revenue per purchase, the variance problem with revenue metrics, ratio metric gotchas, and practical setup examples.
Why you can only have one primary metric, how to choose it correctly, why revenue per visitor usually beats CVR alone, and how metric selection affects test duration and statistical validity.
The exact technical difference between URL targeting and audience targeting in Optimizely, when to use each, wildcard patterns, regex examples, and the most common targeting mistakes that corrupt test data.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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