Articles exploring analytics through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
10 articles
When breakdown rows exceed total users, you're seeing overlapping populations, not a funnel. Here's why dashboards fail and how to fix it.
Atticus Li shares lessons from leading marketing analytics at Silicon Valley Bank and NRG Energy — covering Google Analytics vs Adobe Analytics, data storytelling for executives, geo-incrementality testing, and what it means to be a consultant, not just an analyst.
Bad tracking corrupts A/B test results silently. Learn how to detect and prevent instrumentation bugs that make your experiment data unreliable or misleading.
Create AI-powered dashboards without writing SQL using natural language queries, automated visualizations, and real-time data connections.
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.
Visitor-based vs session-based conversion counting, the exact math showing how it changes your reported rate, unique vs all conversions, how to audit your setup, and common bugs that inflate conversions.
Optimizely and GA4 will never show identical numbers — and that's expected. This guide explains the 5 root causes of discrepancies, how to audit each one, and when a discrepancy signals a real problem vs. normal variance.
The top-line result is often a lie. This guide shows you how to segment Optimizely results correctly, which segments actually matter, and how to avoid the statistical traps that turn exploratory data into false conclusions.
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.
Dashboard design inadvertently reinforces confirmation bias by making favorable metrics prominent and burying contradictory signals. Understanding this cognitive trap is essential for teams that want data to drive decisions rather than validate them.