Using Data to Bridge Product and Engineering Teams
summary
Shows concrete practices: a one‑page problem brief and a separate design brief to force hypotheses, metrics, alternatives, plus an after-action report required after launches. Describes an A/B test at Stack Overflow that lowered answer rate when old unanswered questions were boosted, and a Heap data analysis that finds "five monthly querying users" is the best predictor of retention. Emphasizes measurable outcomes over opinion, simple fingertip metrics, and iterating until the expected business impact appears.
AI-written summary · about
chapters (14)
- 0:00Introduction
- 1:22How decisions are made
- 3:20Good vs great PMs
- 4:24Why PMs work with engineers
- 6:59The problem brief
- 8:39The design brief
- 10:10The launch treadmill
- 12:07Tell a story
- 14:41After Action Report
- 15:39Simplicity
- 17:49fingertip metrics
- 19:28leading vs lagging indicators
- 20:33the simple metric
- 21:35PM behaviors
related talks
24mEffective Product Experimentation: Using Data-Driven Techniques for Iteration
27mCustomer Driven Development : What, Why & How?
37mEffective Data-driven Product Roadmaps
44mProduct 101 for Techies and Tech Teams with Amanda Savluchinske
14mBuilding a Data-Driven Culture: An Anthology of Strategies and Insights
40mBuilding Data Cultures
35mExperimentation for Engineers
56mEssential Analytics and Coding for the ‘Complete’ UX Professional