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Metrics in early stage startups - Leancamp Berlin

  1. Metrics Lessons Learned Photo: Dstarg @fmssnr + @andreasklinger > #leancamp
  2. Florian Meissner, CEO of EyeEm @fmssnr Andreas Klinger Co-Founder a.D. of LOOKK @andreasklinger Hello Berlin. All slides are on www.slideshare.net/andreasklinger Photo: TTL
  3. Who here - wants to run - runs - works in - help startups? Photo: Dstarg
  4. Who here is before Product/Market Fit? Photo: kenjinakazawa
  5. Startup phases… traction log(time) Discovery Validation Efficiency Scale Acquisition Problem/Solution Product/Market Company building Source: Steve Blank’s Customer Development
  6. Goal of this session: Talk about metrics in early stage because feels kinda different… Photo: mecca dawn
  7. What does it tell me about my product?
  8. Vanity VS Actionable Usually: “We have 5000 (Total Registered) Users…” But also numbers that relate stronger to your PR bumps than to your product core. “We have 5000 Visitors / Month” is in early stage usually not actionable.
  9. problems in early stage: 1) external traffic messes up your insights 2) product is not ready for market communication, vp, market seg, channels, product - all is yet wrong. So how much does “10% improve really tell you” 3) small data pool of actually useable data Photo: Dstarg
  10. Metrics are applied differently in early stages Discovery Validation Efficiency Scale Qualitative Quantitative Validation Validation Source: Custdev.com
  11. Early stage metrics are useable for: Validation of customer feedback - saying vs doing - did they really use the app? Validation of internal opinions - believing vs knowing - “Our users need/are/do/try…” Photo: dstarg
  12. Framework: AARRR Photo: Pascal
  13. Example Photoapp Aquisition - User registered Activation - User took a photo Retention - Opened the app again <= 2pm Referral - Share a photo publicly Revenue - haha
  14. Example Photoapp Aquisition - User registered Activation - User took a photo Retention - Opened the app again <= 2pm Referral - Share a photo publicly Revenue - haha To see progress over time we create groups of users (cohorts) and compare them.
  15. Cohort - registration date WK acquisition activation retention referral revenue twice a Photoapp registration first photo share … month 1 4000 62,5% 25% 10% 2 8750 65% 23% 9% 3 3500 64% 26% 4% … … … … …
  16. In early stage focus on retention + activation Aquisition - User registered Activation - User took a photo Retention - Opened the app again <= 2pm Referral - Share a photo publicly Revenue - haha read: http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/
  17. Retention = people come again = people “do” again = people “buy” again read: http://danhilltech.tumblr.com/post/ 12509218078/startups-hacking-a-cohort- analysis-with-google
  18. Retention
  19. You focus on retention because... Retention = f(user_happiness) Photo: Marie
  20. Find your Happiness metric! e.g. crashpadder (exit to airbnb) cohorts hosts-happiness by city&time to create an health/happyness dashboard
  21. Dataschmutz A layer of dirt that obfoscutates your insightful/useable/real data. Photo: Axel Hala!sz
  22. Dataschmutz e.g. created by traffic spikes
  23. Dataschmutz - eg spike traffic WK visitors acquisition activation retention referral revenue twice a EyeEm downloads registration first photo share … month 1 6000 66% / 4000 62,5% 25% 10% 2 25000 35% / 8750 65% 23% 9% 3 5000 70% / 3500 64% 26% 4%
  24. Dataschmutz Exam MySugr is praised as “beautiful app” example.… => Downloads => Problem: Not all are diabetic They focus on people who activated.
  25. Dataschmutz KPIs not drilled down enough Example Garmz/LOOKK had 90% activation (votes) but they only voted for friends instead of actually using their platform.
  26. Dataschmutz Competitions Competitions create additional Value Proposition.
  27. The process of user activation Photo: きなこ
  28. The process of user activation Your users that are happy and retentive: What action differed them from your lost users?
  29. Example: Twitter signup process How many times do people need to use Twitter to come back next month? (7) What did they do? Magic number 30 (Follow 20 people, followed back by 10) How do we get people to 30? Make assumptions, create features and run tests! Watch: http://www.youtube.com/watch? v=L2snRPbhsF0
  30. KPIs & Dashboards Photo: @fmssr
  31. Good KPIs - Eliminates "Dataschmutz" - Is simple to explain - Focus on retention - To optimize retention focus on activtion - Drill down -> Metrics need to hurt.
  32. Team Photo: fisheyedreams
  33. “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” -Jim Barksdale, former CEO of Netscape
  34. Team Clear decision making hierarchy Broken code -> data not trustworthy -> trust lost -> data useless Implement data thinking (especially in core dev team) New features need to have a goal. And this goal needs to be represented by a KPI Focus on a simple and small set of KPIs, dont go crazy (example google analytics) in early stage not data driven but data validation
  35. Read on! Startup metrics for Pirtates by Dace McClure http://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-version Actionable Metrics by Ash Mauyra http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/ Data Science Secrets by DJ Patil - LeWeb London 2012 http://www.youtube.com/watch?v=L2snRPbhsF0 Twitter sign up process http://www.lukew.com/ff/entry.asp?1128 Lean startup metrics - @stueccles http://www.slideshare.net/stueccles/lean-startup-metrics Cohorts in Google Analytics - @serenestudios http://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis- with-google
  36. Thanks @andreasklinger @fmssnr Slides: http://www.slideshare.net/andreasklinger Photo: Yayoi Yaguchi

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