Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
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.
Statistical approaches for low-traffic B2B experimentation: Bayesian methods, qualitative validation, and proxy metrics that make meaningful testing possible even with limited account volumes.
Multiple decision-makers with conflicting motivations require different persuasion strategies than B2C. Understanding buyer committees through behavioral science reveals why standard conversion tactics fail in enterprise sales.
The uncanny valley of personalization and the privacy-relevance tradeoff. Why more data does not always produce better experiences, and how to find the optimal personalization depth.
Cognitive load management through layered information architecture. How strategic information hiding improves decision quality and accelerates conversion.
Don Norman's three levels of design processing applied to SaaS: visceral, behavioral, and reflective. When emotional design accelerates growth and when it undermines credibility.
The business case for accessibility: larger addressable market, better SEO, and cleaner code. Why designing for the edges improves the experience for everyone.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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