Product Analytics for PMs, With AI Help
Ask better product questions and use AI to explore data without fooling yourself
A taste of a lesson
Users who use our templates feature retain much better. Should we push everyone to templates?
Not yet. That pattern could mean templates help, or it could mean already engaged users are the ones who find templates. Both produce the same correlation. To test whether templates cause better retention, you would want an experiment: for example, show a template prompt to a random half of new users and compare retention between the halves. Talk to an analyst about sample size. Meanwhile, check one thing: do template users differ from others before they first use templates, say in how active they were in week one?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain events, funnels, retention, cohorts and active users with examples
- Turn a vague product question into a specific, measurable one
- Use AI to draft queries and summaries and check them before trusting results
- Spot common traps such as small samples, tracking changes and correlation
- Write a short, honest weekly metrics note
Lesson plan
- 1 The building blocks of product data Understand events, users and the core metrics product teams use. Start
- 2 Funnels, retention and cohorts Read funnels, retention curves and cohort tables with confidence. Start
- 3 From vague question to clear metric Rewrite a fuzzy product question into one you can measure. Start
- 4 Querying with AI, and checking it Use AI to draft queries and verify the logic before using the numbers. Start
- 5 Traps that fool smart people Recognise the most common ways product data misleads. Start
- 6 Writing an honest metrics note Summarise the week's data clearly, with uncertainty stated. Start
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About this tutor
For new and aspiring product managers who need to read product data but are not analysts. You will learn the core ideas of product analytics: events, funnels, retention, cohorts and active users, and how to turn a vague question into a measurable one. You will use AI assistants to help write simple queries, explain charts, summarise feedback alongside numbers and draft insight summaries, while learning the checks that stop you drawing wrong conclusions, such as small samples, changed definitions and correlation mistaken for cause. You practise with example datasets or your own product's numbers and finish able to write a short, honest weekly metrics note.
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About the teacher
Product management for AI features: deciding, specifying, testing and pricing them well
9 tutors 388 lessons taught Sample
I teach product managers and founders how to build AI features that people trust and keep using. I come from product work on software teams, where I learned that the hard part of an AI feature is rarely the model. It is deciding whether the feature should exist, writing down what good looks like, testing it before customers do and...
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