Measure what matters — from cohort analysis to attribution models to decisions under partial data.
Most teams drown in data but can't answer simple questions about what's working. This course builds rigorous analytics fundamentals — funnel analysis, cohort retention, revenue analytics, A/B test interpretation, attribution models, and incrementality testing — so you can make better decisions even with incomplete data.
Built by Lakshya Kumar
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I am learning marketing analytics and attribution — funnel analysis, cohort retention, revenue metrics (MRR/NRR), A/B test design and analysis, attribution models, product analytics, and incrementality testing. Help me build a measurement framework that drives real decisions.
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Sign in to applyComplete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Audit a real product's analytics implementation: validate event tracking in GA4/Mixpanel, build a 12-week retention cohort table, calculate current NRR and identify the top expansion or retention lever, design one A/B test with correct sample size and stopping rules, and propose an attribution model that accounts for your actual channel mix. Document every finding and recommendation with supporting data.
Implement two attribution models on the same data: first-touch and time-decay multi-touch. Compare the channel attribution under each. Identify which channels are over- or under-credited and document a recommendation for which model to standardize on.
Define a north star metric for a real product. Decompose it into 3 levels of driver metrics. Build a dashboard (Mixpanel, Amplitude, or SQL) that updates daily. Demonstrate identifying a regression by reading the tree.
Take 6 months of user data; produce monthly cohort retention curves. Identify the cohort with anomalously high or low retention; investigate root cause via segmentation. Write a one-page memo summarizing findings for a product team.
Design an in-house A/B testing platform: assignment service, exposure logging, statistical engine (sequential testing or fixed-N), guardrail metrics. Spec the schema, the API, and the runbook. Optionally prototype the assignment service.
Product analytics documentation covering funnels, cohorts, and retention analysis.