Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
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.
Skeleton screens, progress indicators, and temporal distortion in digital experiences. How our perception of time waiting shapes satisfaction more than objective milliseconds.
The long-term business cost of manipulative UX: churn, reviews, regulatory risk, and brand erosion. Why ethical persuasion outperforms manipulation over any meaningful time horizon.
Mobile UX beyond responsive design: touch targets, one-handed use patterns, and interruptible flows. Why mobile optimization requires rethinking interaction models, not just layout.
Card sorting, mental models, and how information hierarchy affects both findability and purchase confidence. The hidden economics of navigation design.
The engagement metrics that actually predict conversion: scroll depth, interaction rate, and qualified sessions. Why bounce rate tells you almost nothing useful and what to replace it with.
How grouping users by acquisition date reveals retention, engagement, and revenue patterns invisible in aggregate data. A behavioral lens on why cohorts expose the truth that averages conceal.
Data discrepancies between platforms, the observer effect in measurement, and how to build a single source of truth when every tool tells a different story.
Mean reversion in marketing channels, the diminishing returns curve, and when to trust your model vs. your gut. Why the past is an increasingly unreliable guide to the future in growth.
Why tracking plans fail, how naming conventions compound, and the hidden cost of retrofitting analytics. The architectural decisions that determine whether your data is an asset or a liability.
You study users who entered the funnel but ignore those who never started, creating systematically wrong conclusions about where to invest optimization effort.
The fundamental measurement problem in digital marketing and why all models are wrong but some are useful. An exploration of attribution through the lens of behavioral economics and epistemology.
Why pageviews, followers, and time-on-page are seductive but misleading without context. A behavioral science framework for distinguishing metrics that inform decisions from metrics that merely comfort.
Demystify A/B testing statistics — p-values, confidence intervals, Type I and Type II errors, and one-tail vs two-tail tests explained in plain English with business context.
The Elaboration Likelihood Model applied to copy length decisions based on product type, price point, and user intent. Why the length debate misses the point entirely.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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