Articles exploring cro through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
18 articles
Most A/B testing advice is written by people who've never defended a losing test in a business review. Here are 10 practices that survived real traffic, real org politics, and real budget pressure — from an experimentation lead running 100+ tests a year.
Behavioral economics is powerful, but the field has had a reputation crisis. Here is how to use principles like loss aversion, anchoring, and social proof without getting burned by unreliable research.
Most experimentation advice assumes perfect statistical significance. Here is how to make the best decision when the data will never be complete — a pragmatic framework from 150+ enterprise experiments.
Most experimentation teams write hypotheses that are really just disguised solutions. Here is why problem-first hypothesis design produces better tests — and how to stop solutionizing before you understand the leak.
The fastest way to scale an experimentation program is to prove dollar-value impact on the first few wins. Here is how to make experimentation a revenue function instead of a cost center.
The biggest CRO influencers run programs at companies with Netflix-level traffic. That is not your reality. Here is how to think about experimentation advice when you operate under real-world constraints.
Atticus Li's PRISM Method is a five-step experimentation framework — Probe, Revenue Rank, Implement, Score, Multiply — that ties every A/B test to projected revenue impact, achieving a 24%+ win rate at NRG Energy.
CRO and SEO teams often pull in opposite directions. Learn where the conflicts happen, why they exist, and how to resolve them with data instead of politics.
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.
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 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.
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.
Most testing roadmaps are just feature wishlists. Here's how to build a real experimentation roadmap—with prioritization frameworks, sequencing logic, and stakeholder buy-in tactics that keep the program moving.
"Let's test a bigger CTA" is not a hypothesis. Here's the exact structure for writing A/B test hypotheses that produce useful results whether they win or lose—with 5 real rewrites and a hypothesis library framework.
Not all A/B tests are equal. Here are 10 experiments with tight behavioral hypotheses, realistic lift expectations, and the exact failure modes to watch out for — plus 3 bonus tests the standard lists always miss.