Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
Step-by-step guide to setting up A/B tests properly — from writing testable hypotheses to choosing between server-side and client-side tools to the QA checklist before launch.
Understand why A/B test results might not hold in the real world. Learn about seasonality, selection bias, novelty effects, and how to protect your conclusions from external validity threats.
Learn when you can safely run multiple A/B tests simultaneously and when interaction effects will corrupt your results. Includes traffic allocation strategies and detection methods.
Stop losing experiment learnings. Build an A/B test archive and knowledge base that compounds institutional knowledge, prevents duplicate tests, and accelerates onboarding.
Learn how to calculate the right sample size and test duration for A/B tests. Understand regression to the mean, why peeking kills tests, and the magic number myth.
Learn how to prioritize your A/B test backlog using data-driven frameworks like PXL. Stop testing based on opinions and start testing based on evidence and potential impact.
Master the four-phase A/B testing process that separates systematic optimization from random testing. Learn why most teams skip the first two phases and why that kills their programs.
Go beyond the textbook definition of A/B testing. Learn what controlled experimentation really means for digital products, why most teams get it wrong, and the five components every valid test requires.
Learn how to properly analyze A/B test results beyond the dashboard green light. Master segmentation, effect size interpretation, and honest reporting that builds credibility.
Discover the six research methods that separate high-impact A/B tests from random guessing. From heuristic analysis to user testing, learn how to find the problems worth solving.
The mathematical reality of diminishing returns in conversion rate optimization explains why early tests produce dramatic gains, why mature programs plateau, and when the rational strategy shifts from optimization to acquisition.
Analysis of 1,000 email subject line A/B tests reveals how curiosity gaps, personalization, numbers, and length interact with audience expectations to drive open rates across industries.
A behavioral science analysis of checkout abandonment reveals that unexpected costs trigger trust violations, payment friction activates loss aversion, and the commitment-consistency gap explains why intent fails to convert.
Cross-device behavior analysis reveals that the mobile conversion gap is driven by cognitive load differences, the research-on-mobile-buy-on-desktop pattern, and attribution models that misallocate credit.
A meta-analysis of 500 form optimization experiments reveals consistent patterns in field reduction, progressive profiling, and cognitive load management that challenge conventional conversion wisdom.
An analysis of 200 SaaS pricing pages reveals that the highest-converting designs share patterns in tier structure, feature framing, and social proof placement rooted in decision psychology.
Comparing hub, funnel, and narrative homepage architectures reveals that the optimal design depends on visitor intent distribution, brand awareness, and the cognitive load each structure imposes on different audience segments.
AI transforms hypothesis generation, test velocity, and real-time personalization in experimentation programs while the fundamental requirements of statistical rigor, sample size, and causal inference remain unchanged.
Behavioral segmentation vs. demographic segmentation and why specificity in targeting improves everything downstream. Most ideal customer profiles describe markets, not customers, and the difference is costly.
Self-service vs. high-touch through the lens of decision complexity, perceived risk, and social proof needs. Why the right growth model depends on buyer psychology, not just product category.
Creating demand vs. capturing it: different psychological mechanisms, different metrics, different timelines. Why conflating these two functions produces mediocre results in both.
How optimizing for conversion can destroy lifetime value, brand perception, and organic traffic. The tension between short-term conversion metrics and long-term business health.
Why organic compounds like an investment and paid is linear like an expense, and when each is optimal. A framework for thinking about acquisition channel allocation through the lens of asset economics.
The reciprocity principle applied to content strategy: giving away knowledge as an acquisition strategy. Why ungated content builds more pipeline than gated content captures leads.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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