Build funnels, retention cohorts, and revenue dashboards you can defend in a board meeting — and act on when the numbers move.
You open the dashboard, see traffic up and signups flat, and have no idea which of last month's five campaigns to run again. Every tool tells a different story: Google Analytics 4 (GA4) credits the last click, the ad platform claims every conversion it touched, and the spreadsheet nobody trusts says something else. This course fixes that. You set up clean event tracking and a UTM (Urchin Tracking Module) naming scheme that survives a year of campaigns, build funnels that show the exact step where people leave, and read retention cohort tables that tell you whether new users stick. You calculate monthly recurring revenue movements and net revenue retention, run A/B tests with a real sample size instead of stopping the moment the numbers look good, and compare attribution models side by side to see which channels are over-credited. Later modules cover marketing mix modeling (MMM), geo holdout tests, dashboards in Looker Studio, and a full audit of your own tracking. Every module ends with something you build against real 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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Split monthly recurring revenue into its moving parts so you can name the exact number dragging growth down this quarter.
Measure whether people reach the moment your product clicks — and how many of them still come back the following week.
Run geo holdout and lift tests, plus marketing mix modeling (MMM), to prove a channel caused sales instead of merely touching them.
Complete 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.