Product Analytics
About this book
Rodrigues works through the analytical toolkit for consumer products, from defining metrics to building models that explain user behaviour. The book starts with behavioural theory and the argument that a metric needs a causal story behind it, then moves into descriptive analysis, cohort and retention modelling, and the statistics that experimentation depends on. Later sections cover causal inference outside the experiment, including matching methods and instrumental variables, along with simulation and forecasting. Code examples are given in both R and Python, with the reasoning explained alongside. Suited to an analyst or a technically inclined product manager who wants to answer questions an A/B test cannot.
Description via Product Digest.
Topics
Related concepts
- Privacy and consent in analyticsWhich facts inside an event property count as personal data, the lawful basis and the four tests a consent has to pass, how long a record may be kept, and the arithmetic showing what a declined consent rate does to every funnel and cohort the product reports.
- Identity and sessionsHow an analytics tool decides that two events came from one person, what stitching an anonymous identifier to a known account can and cannot recover, the browser storage limits that make a returning visitor look new, and the session as a timeout rule that changes every measure counted per visit.
- Product analyticsWhat product analytics is, how it differs from business intelligence and from user research, the event as the unit everything else is counted from, and the single test that separates a practice from a wall of dashboards.
- Funnels and conversionA funnel as a sequence of steps with a count at each one, the three choices that decide what it measures, a worked conversion calculation from forty thousand visitors down to four hundred and eighty customers, and why the denominator has to be stated before any rate means anything.
- Retention curvesThe shape a cohort traces over time, what a curve that flattens says about a durable audience, what a curve that reaches zero says about the absence of one, and how far apart day N, unbounded and bracket retention put the same cohort on the same data.
- Engagement and frequencyHow often people come back, daily over monthly active users as a ratio with a worked case where two very different products report the same 20 per cent, counting active days in a period, and the natural frequency that sets the ceiling for any product.