Metrics in early stage startups - Leancamp Berlin
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Metrics in early stage startups - Leancamp Berlin

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Metrics in early stage startups - Leancamp Berlin

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Metrics in early stage startups - Leancamp Berlin Metrics in early stage startups - Leancamp Berlin Presentation Transcript

  • Metrics Lessons LearnedPhoto: Dstarg @fmssnr + @andreasklinger > #leancamp
  • Florian Meissner, CEO of EyeEm @fmssnr Andreas Klinger Co-Founder a.D. of LOOKK @andreasklinger Hello Berlin. All slides are on www.slideshare.net/andreasklingerPhoto: TTL
  • Who here - wants to run - runs - works in - help startups?Photo: Dstarg
  • Who here is before Product/Market Fit?Photo: kenjinakazawa
  • Startup phases…traction log(time) Discovery Validation Efficiency Scale AcquisitionProblem/Solution Product/Market Company building Source: Steve Blank’s Customer Development
  • Goal of this session: Talk about metrics in early stage because feels kinda different…Photo: mecca dawn
  • What does ittell me aboutmy product?
  • Vanity VS Actionable Usually: “We have 5000 (Total Registered) Users…” But also numbers that relatestronger to your PR bumps than to your product core.“We have 5000 Visitors / Month” is in early stage usually not actionable.
  • 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 dataPhoto: Dstarg
  • Metrics are applied differently in early stages Discovery Validation Efficiency ScaleQualitative QuantitativeValidation Validation Source: Custdev.com
  • 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
  • Framework: AARRRPhoto: Pascal
  • Example PhotoappAquisition - User registeredActivation - User took a photoRetention - Opened the app again <= 2pmReferral - Share a photo publiclyRevenue - haha
  • Example PhotoappAquisition - User registeredActivation - User took a photoRetention - Opened the app again <= 2pmReferral - Share a photo publiclyRevenue - hahaTo see progress over time we create groups of users(cohorts) and compare them.
  • Cohort - registration date WK acquisition activation retention referral revenue twice aPhotoapp registration first photo share … month 1 4000 62,5% 25% 10% 2 8750 65% 23% 9% 3 3500 64% 26% 4% … … … … …
  • In early stage focus on retention +activationAquisition - User registeredActivation - User took a photoRetention - Opened the app again <= 2pmReferral - Share a photo publiclyRevenue - haharead: http://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/
  • Retention= people come again= people “do” again= people “buy” again read: http://danhilltech.tumblr.com/post/ 12509218078/startups-hacking-a-cohort- analysis-with-google
  • Retention
  • You focus on retention because... Retention = f(user_happiness)Photo: Marie
  • Find your Happiness metric!e.g. crashpadder (exit to airbnb)cohorts hosts-happiness by city&timeto create an health/happyness dashboard
  • Dataschmutz A layer of dirt that obfoscutates your insightful/useable/real data.Photo: Axel Hala!sz
  • Dataschmutze.g. createdby trafficspikes
  • Dataschmutz - eg spike trafficWK visitors acquisition activation retention referral revenue twice aEyeEm 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%
  • Dataschmutz Exam MySugr is praised as “beautiful app” example.… => Downloads => Problem: Not all are diabetic They focus on people who activated.
  • DataschmutzKPIs not drilled down enoughExampleGarmz/LOOKKhad90% activation (votes)but they only voted for friendsinstead of actually using their platform.
  • DataschmutzCompetitionsCompetitions createadditional ValueProposition.
  • The process of user activationPhoto: きなこ
  • The process of user activationYour users that are happy and retentive:What action differed them from your lost users?
  • Example: Twittersignup processHow many times do peopleneed to use Twitter to comeback next month?(7)What did they do?Magic number 30(Follow 20 people, followedback by 10)How do we get people to 30?Make assumptions, createfeatures and run tests!Watch: http://www.youtube.com/watch?v=L2snRPbhsF0
  • KPIs & DashboardsPhoto: @fmssr
  • Good KPIs- Eliminates "Dataschmutz"- Is simple to explain- Focus on retention- To optimize retention focus on activtion- Drill down -> Metrics need to hurt.
  • TeamPhoto: fisheyedreams
  • “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
  • TeamClear decision making hierarchyBroken code -> data not trustworthy -> trust lost -> datauselessImplement data thinking (especially in core dev team)New features need to have a goal. And this goal needs tobe represented by a KPIFocus on a simple and small set of KPIs, dont go crazy(example google analytics)in early stage not data driven but data validation
  • Read on!Startup metrics for Pirtates by Dace McClurehttp://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-versionActionable Metrics by Ash Mauyrahttp://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/Data Science Secrets by DJ Patil - LeWeb London 2012http://www.youtube.com/watch?v=L2snRPbhsF0Twitter sign up processhttp://www.lukew.com/ff/entry.asp?1128Lean startup metrics - @stueccleshttp://www.slideshare.net/stueccles/lean-startup-metricsCohorts in Google Analytics - @serenestudioshttp://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis-with-google
  • Thanks @andreasklinger @fmssnr Slides: http://www.slideshare.net/andreasklingerPhoto: Yayoi Yaguchi