Lean Startup Analytics/Metrics
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Presented at Lean Startup Circle on 4/15/14 for their back to basics night. The theme for the evening was analytics.
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Lean Startup Analytics/Metrics
- 1. Vanity Dies Hard Back to Basics
- 2. @ElliotSusel
- 3. Vanity Metrics Actionable Metrics Innovation Accounting Systems
- 4. Vanity Metric Low correla/on to what makes your business successful
- 5. These are important… But let’s not overstate their significance
- 6. Success Theater Look at our [insert vanity metric]! Our business is amazing!
- 7. What then DO we measure?
- 8. Actionable Metric High correla/on to what makes your business successful
- 10. But there’s a problem The ac/onable metric is a lie
- 12. • Outreach • Promo/ons • Support • Technology Stuff we control
- 13. • Weather • Seasonality • Holidays • Events (Sports, Religious, Concerts, Others…) • System Outages (Fleet, Dispatch, Internal) Stuff we don’t control
- 14. THIS IS SCARY We think we have meaningful data We do not have meaningful data
- 16. Innovation Accounting Understand the factors impac/ng your metrics
- 17. Understanding Ride Volume • New vs Return Customers • Distribu/on by plaMorm • Distribu/on by fleet/geographic region • Distribu/on by day of week and /me of day
- 18. Why? Validated learning is a startup’s most valuable commodity
- 19. Nice concepts How do we execute?
- 20. A SYSTEM, YOU SAY? Losers have goals Winners create systems
- 21. Components of Our System • Every development request has a goal • Every goal has quan/fica/on • Architecture review includes analy/cs • Code review includes analy/cs • Every PROD deployment requires hooks to monitoring/analy/cs tools
- 22. Stuff we’ve learned Or at least, closing thoughts
- 23. Culture Matters If leadership doesn’t value analy/cs, no one will value analy/cs
- 25. Accidental Learning Informa/on radiators are your friend
- 26. Create a System A project accounts for metrics from incep/on to delivery
- 27. Then tune it Because you’re never done learning
Hinweis der Redaktion
- Today we’re going to talk about vanity… but probably not in the way that you expected
- Downloads, app opens, app reviews, # of registered users
- But if you were to overstate their significanceDanger… you run the risk of losing sight of what really matters.
- Rides.
- The understanding of the metric is incomplete if we don’t understand why it’s changing
- Ridership metrics subject to the following factors which are completely out of our hands and may completely invalidate all would-be meaningful conclusions
- Ridership metrics subject to the following factors which are completely out of our hands and may completely invalidate all would-be meaningful conclusions.
- You don’t even know if all the “great changes” you’re making are actually improving the product, or if it was getting better in spite of your efforts.
- How do we get to meaningful conclusions?
- Examples of how we could do this. Callback to things that do/don’t impact metrics. Such as rain. We know it increases ridership, but we can’t control whether or not it rains, at least, not yet.
- Meaningfulanalytics and metrics empower you to draw intelligent conclusions that you might not have been able to determine otherwise. It lets you validate and challenge your assumptions.
- Targeting this conversation at small companies (>30 and <100). Speak to my experience. Talk about systems.
- NOTE: THIS IS NOT PERFECT
- If you send me 30 pages of reports I will read 0 pages of reports
- On that note, I’d like to close out and leave some time for questions