4x Users in Just One Year
summary
Operabase focuses on one cohort — audience members — uses first-principles research and cheap, rapid experiments to pick the right problem, then layers machine learning: a collaborative filtering recommender, personalized homepage tiles and automated emails. Results: returning users jump from 8% to 23%, daily actives rise 4x, monthly actives grow from ~150k to ~675k, homepage CTR triples (≈39% to ≈92%) and time on site rises ~25%, showing the approach scales and sustains.
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chapters (9)
- 0:00Introduction and welcome
- 1:03First principles approach
- 3:12Why solving the right problem matters
- 9:54Case study introduction Opera Base
- 15:36Targeting returning visitors
- 19:13Machine learning for recommendations
- 22:23Onboarding and user experience
- 23:44Results and growth metrics
- 27:14Key takeaways and conclusion
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