Attribution Models Are Broken: Why Last-Click Is Lying to You and Multi-Touch Isn't Much Better
The fundamental measurement problem in digital marketing and why all models are wrong but some are useful.
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Know what to test, when to trust the result, and what to do next. Practical guides for analysts, growth teams, and founders.
Vanity Metrics vs. Actionable Metrics: A Framework for Knowing the Difference
Why pageviews, followers, and time-on-page are seductive but misleading without context.
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A/B Testing Statistics: P-Values, Confidence Intervals, and What They Actually Mean
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…
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Long-Form vs Short-Form Copy: When Length Helps and When It Kills Conversion
The Elaboration Likelihood Model applied to copy length decisions based on product type, price point, and user intent.
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Power Words vs. Clarity: When Emotional Copy Hurts Conversion
The Elaboration Likelihood Model applied to copy: high-involvement users need substance, not sizzle.
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Microcopy That Converts: The Psychology of Button Labels, Error Messages, and Helper Text
How word-level decisions in UI copy trigger or suppress action through cognitive fluency, loss framing, and autonomy.
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Social Proof Copy: Beyond 'Join 10,000+ Customers' — What Actually Persuades
Why specificity, similarity, and narrative social proof outperform generic numbers.
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The Curse of Knowledge in Product Copy: Why Experts Write the Worst Landing Pages
The cognitive bias where deep product knowledge makes it impossible to write from the user's perspective.
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The Readability-Conversion Correlation: Why Simpler Copy Outperforms Smart Copy
Flesch-Kincaid meets conversion data: cognitive load theory applied to marketing copy.
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Voice and Tone Systems: Why Consistent Copy Increases Trust by 33%
Brand voice as a trust signal through mere exposure and processing fluency.
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Headlines That Work: The Neuroscience of What Makes People Stop Scrolling
Attentional capture, novelty detection, and the information gap theory applied to headlines.
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Experiment Velocity: How to Run More Tests Without Sacrificing Quality
Higher experiment velocity compounds learning and growth, but only if quality is maintained.
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From Local Maximum to Global Maximum: Why Optimization Requires Bold Tests
Incremental A/B testing can trap your product at a local maximum. Learn the difference between exploitation and exploration, and why the most successful…
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The A/A Test: How to Validate Your Experimentation Setup
An A/A test pits two identical experiences against each other to validate your experimentation infrastructure.
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The Mean, Variance, and Sampling: Essential Statistics for A/B Testers
Understanding the mean, variance, and sampling is foundational for making sound A/B testing decisions.
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The Peeking Problem: Why Checking Your Test Early Destroys Validity
Early peeking at A/B test results inflates false positive rates and leads to costly decisions based on noise.
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Running Multiple A/B Tests Simultaneously: When It Works and When It Doesn't
Understand when running concurrent A/B tests is safe and when it introduces risk.
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How to Analyze A/B Test Results: Beyond the Dashboard
Learn why dashboard metrics alone can mislead your A/B test analysis. Discover how to verify results across multiple data sources, interpret inconclusive…
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Data Segmentation for A/B Tests: Finding Hidden Winners
Discover how to uncover segment-level insights hidden within overall A/B test results.
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Client-Side vs Server-Side A/B Testing: Choosing the Right Architecture
Understand the tradeoffs between client-side and server-side A/B testing architectures.
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What to Do With Inconclusive A/B Test Results
Inconclusive A/B test results are not failures. Learn how to extract learning from flat tests, distinguish between wrong hypotheses and weak…
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How to Archive A/B Tests: Building Organizational Knowledge
Transform your A/B testing program from isolated experiments into a compounding knowledge system.
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How to Prioritize A/B Tests: Frameworks That Actually Work
Move beyond gut-feel prioritization with structured frameworks for ranking A/B test hypotheses.
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External Validity Threats: Why Your A/B Test Results Might Not Generalize
Discover the external validity threats that can invalidate your A/B test results, from seasonality and sample pollution to the flicker effect, and how to…
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