Product Analytics, Metrics & LTV
Defining the numbers a product is steered by — and not fooling yourself with them
Define, compute and sanity-check the numbers a product is steered by: the metric hierarchy (north star, driver metrics, guardrails, the OEC, a worked metric tree for a subscription product, leading versus lagging), what makes a metric good (sensitivity, directionality, actionability, time-to-signal, Goodhart's law, validating proxy metrics), core definitions stated precisely (DAU, WAU, MAU and stickiness, activation, N-day versus unbounded versus bracketed retention, churn, resurrection, cohort triangles, growth accounting with the quick ratio), funnels (strict versus any-order, conversion windows, time-to-convert, attribution caveats), distributions rather than averages (heavy tails, per-user versus per-event, the ratio-metric trap with a delta-method A/A simulation, winsorization), unit economics (LTV by the simple formula, by cohort, with discounting and margin, CAC, payback, LTV/CAC pitfalls, predictive LTV), segmentation and Simpson's paradox with a mix-versus-rate decomposition, instrumentation (event taxonomy, identity stitching, sessionization rules, tracking QA), dashboards and metric ownership, and a worked example that builds a retention triangle and growth accounting from 12 event rows and computes three LTV variants for one cohort. Every number was recomputed by running code.
Practice questions (6)
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Define a North Star, Driver Metrics and Guardrails for a Grocery-Delivery Subscription
Intermediate · Free -
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Compute Day-N Retention from Raw Events Under Three Definitions
Intermediate · Free -
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Weekly Actives Grew 10 Percent While Retention Fell: Diagnose with Growth Accounting
Intermediate -
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Compute LTV with Churn, Margin and Discounting, Then Judge a CAC of 150 USD
Intermediate -
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Campaign A Converts Better Overall, but B Wins in Every Segment: Who Is Better?
Intermediate