49 articles in Product Analytics
Revolut grew to over 30 million customers by shipping features at a pace unheard of in banking. This case study examines how the fintech challenger built an experimentation culture, used feature flags and gradual rollouts to manage risk in a regulated industry, and applied product-led growth principles to financial services.
Spotify's Discover Weekly and Wrapped features are masterclasses in using data to create delightful product experiences. This case study examines how the data science and product teams collaborate, how Discover Weekly's recommendation engine was built by a small team in a hackathon, and how Wrapped turned personal data into a viral annual marketing event.
Why true observability requires more than logs, metrics, and traces — and how to build systems that let you ask arbitrary questions about production behavior.
How to measure engineering team performance without destroying morale — focusing on system-level metrics, delivery performance, and developer satisfaction rather than individual output.
Growth hacking is not a bag of tricks — it is a disciplined process of hypothesis generation, rapid experimentation, and data-driven iteration focused on growth metrics. This article traces the origin of the growth hacking movement from Sean Ellis through the growth teams at Facebook, Airbnb, and Dropbox. It covers the growth funnel (acquisition, activation, retention, referral, revenue), the process of running growth experiments, and the organizational design required to sustain a growth practice. The emphasis is on sustainable, ethical growth through product improvement rather than manipulative tactics.
Surveys are the most widely used — and most widely abused — research instrument in business. Poorly designed surveys produce misleading data that can drive costly decisions. This article covers the principles of rigorous survey design: writing unbiased questions, choosing appropriate scales, avoiding leading and double-barreled questions, managing survey length, and sampling strategies. It also covers analysis techniques including how to handle response bias, calculate confidence intervals, and distinguish meaningful differences from noise.
Duolingo runs thousands of A/B tests simultaneously to optimize its gamification mechanics, from streak counts to leaderboards to animated characters. This case study explores how the company's growth team uses experimentation infrastructure, behavioral psychology, and game design to maintain daily active users in the hundreds of millions.
Even data-driven organizations fall prey to confirmation bias when analysts seek, interpret, and remember data that supports pre-existing beliefs while ignoring contradictory evidence. Davenport's research shows that teams given the same dataset reach conclusions aligned with their prior hypotheses 73% of the time. The article prescribes adversarial analysis practices, blind data exploration, and red team reviews that systematically challenge analytical conclusions before they inform decisions.
Madhavan explains how cohort analysis segments users by shared characteristics or time periods to reveal behavioral patterns hidden in aggregate data. The article walks through practical examples of retention cohorts, behavioral cohorts, and acquisition cohorts, demonstrating how each reveals different insights for product and growth teams.
Outlines a framework for selecting and tracking the metrics that genuinely reflect product health, distinguishing between vanity metrics and actionable indicators. Covers retention analysis, engagement scoring, and how to build a metrics hierarchy that connects daily team activities to company-level outcomes.
Bayesian reasoning offers a formal framework for updating beliefs as new evidence arrives, combating both stubbornness and overreaction to new data. This article translates Bayesian principles into practical business applications including A/B test interpretation, market forecasting, and competitive intelligence. Teams that adopt probabilistic thinking make better decisions under uncertainty and communicate assumptions more transparently.
Andreessen Horowitz explains why unit economics, particularly LTV/CAC ratios and contribution margins, are the most critical metrics for evaluating business viability. The article provides frameworks for calculating unit economics across different business models and explains how investors use these metrics to distinguish sustainable growth from subsidized growth.
Whether a medical procedure is described as having a 90% survival rate or a 10% mortality rate dramatically changes decisions, even among trained professionals. This article examines framing effects across business contexts including product marketing, investor communications, and internal reporting. Leaders who understand framing can present information more honestly while stakeholders can guard against manipulation.
Kaley synthesizes UX research on effective dashboard design, covering information hierarchy, progressive disclosure, and the cognitive principles that determine whether dashboards help or hinder decision-making. The article provides practical guidelines for choosing chart types, managing visual complexity, and designing for different user contexts.
Davenport examines the evolving role of Chief Data Officers and why most organizations struggle to become truly data-driven despite massive technology investments. The article argues that building a data culture requires leadership commitment, data literacy programs, and organizational structures that embed data-informed decision-making into daily workflows.
This guide breaks down the TAM/SAM/SOM framework for market sizing, explaining both top-down and bottom-up approaches with practical examples. The article demonstrates how to construct credible market size estimates that withstand investor scrutiny, avoiding common traps like citing overly broad industry reports.
Sull and Sull challenge the ubiquitous SMART goals framework and propose FAST goals (Frequently discussed, Ambitious, Specific, Transparent) as a more effective alternative. The research shows that transparent, frequently reviewed goals with ambitious targets drive significantly better organizational performance than the conventional approach.

A step-by-step framework for measuring and optimizing product/market fit using the Sean Ellis test, user segmentation, and a systematic approach to building what users love.
Skok provides the definitive guide to SaaS metrics, covering MRR, ARR, churn, LTV, CAC, and the benchmarks that indicate healthy SaaS businesses. The comprehensive framework helps both operators and investors evaluate SaaS companies using the metrics that actually predict long-term success and capital efficiency.
Kohavi and Thomke explain how controlled online experiments (A/B tests) enable data-driven product decisions at scale. Covers experimental design, statistical significance, common pitfalls like peeking at results too early, and how companies like Microsoft and Booking.com run thousands of experiments annually.