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Metrics for early stage startups

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How to use metrics in a startup that is yet before it's product market fit.

How to use metrics in a startup that is yet before it's product market fit.

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  • 1. Metrics forEarly-Stage Startups #scb13 – @andreasklinger
  • 2. @andreasklinger“Startup Founder”“Product Guy” #scb13 – @andreasklinger
  • 3. @andreasklinger“Startup Founder”“Product Guy”What we will cover- Why early stage metrics are different.- Applicable methods & Lessons Learned. (this is an excerpt of 2h workshop - but with prettier slides ;) ) #scb13 – @andreasklinger
  • 4. The Main Problem with Metrics in Early Stage:- Product not ready or even wrong.- Little to no useable data.- Data points contradict each other.- External Traffic can easily mess up our insights.- What is actionable?- Are we on the “right” track? #scb13 – @andreasklinger
  • 5. Startup Founders. #scb13 – @andreasklinger
  • 6. Some startups haveideas for a new product.Looking for customersto buy (or at least use) it.Customers don’t buy.“early stage” #scb13 – @andreasklinger
  • 7. Product/Market Fittraction time With early stage I do not mean “X Years” I mean before product/market fit. #scb13 – @andreasklinger
  • 8. Product/market fitBeing in a good marketwith a product that can satisfythat market.~ Marc Andreessen #scb13 – @andreasklinger
  • 9. Product/market fitBeing in a good marketwith a product that can satisfythat market.~ Marc Andreessen= People want your stuff. #scb13 – @andreasklinger
  • 10. Product/Market Fittraction time #scb13 – @andreasklinger
  • 11. Product/Market Fittraction time Discovery Validation Efficiency Scale Steve Blank - Customer Development #scb13 – @andreasklinger
  • 12. Product/Market Fittraction time Discovery Validation Efficiency Scale Find a product the market wants. #scb13 – @andreasklinger
  • 13. Product/Market Fittraction time Discovery Validation Efficiency Scale Find a product Optimise the product the market wants. for the market. #scb13 – @andreasklinger
  • 14. Product/Market Fit traction time Discovery Validation Efficiency Scale Find a product Optimise the product the market wants. for the market.People in search Most clonesfor new product start here. start here. #scb13 – @andreasklinger
  • 15. Product/Market Fittraction time Discovery Validation Efficiency Scale Product & Customer Scale Marketing Development & Operations #scb13 – @andreasklinger
  • 16. Product/Market Fittraction time Discovery Validation Efficiency Scale Startups have phases but they overlap. #scb13 – @andreasklinger
  • 17. Product/Market Fittraction time Discovery Validation Efficiency Scale 83% of all startups are in here. #scb13 – @andreasklinger
  • 18. Product/Market Fittraction time Discovery Validation Efficiency Scale 83% of all startups are in here. Most stuff we learn about web analytics is meant for this part #scb13 – @andreasklinger
  • 19. Startups drown innon actionable datapoints.
  • 20. What does this mean for my product?Are we on the right track?Meant for channel (referral)optimization.
  • 21. Use of Metrics in Early Stage #scb13 – @andreasklinger
  • 22. Use of Metrics in Early StageFocus on People- Not Hits, Pageviews, Visits, EventsValidation of customer feedback- saying vs doing- eg. did they really use the app?- does the app do what they need it to?Validation of internal opinions- believing vs knowing- eg. “Our users need/are/do/try…”Doublecheck + Falsify #scb13 – @andreasklinger
  • 23. Segment Users into CohortsCohorts = Groups of people that share attributes. #scb13 – @andreasklinger
  • 24. Segment Users into Cohorts #scb13 – @andreasklinger
  • 25. Apply a framework: AARRR #scb13 – @andreasklinger
  • 26. AcquisitionVisit / Signup / etc ActivationUse of core feature RetentionCome + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  • 27. Example: PhotoappCohorts based on registration week WK acquisition activation retention referral revenue twice aPhotoapp registration first photo share … month 1 400 62,5% 25% 10% 2 875 65% 23% 9% 3 350 64% 26% 4% … … … … …
  • 28. Acquisition Visit / Signup / etc Activation Use of core featureWhich Metrics to focus on? Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  • 29. Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: RetentionFocus on Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  • 30. Acquisition Visit / Signup / etc Activation Use of core featureLong answer - It depends on two things: Retention Come + use againPhase of company Referral Invite + SignupType of Product (esp. Engine of Growth) Revenue $$$ Earned (c) Dave McClure
  • 31. Long answer - It depends on two things: Acquisition Visit / Signup / etc Activation Use of core feature Retention Come + use again Referral Invite + Signup Revenue $$$ Earned Source: Lean Analytics Book - highly recommend
  • 32. Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: RetentionFocus on Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  • 33. BecauseRetention = f(user_happiness)
  • 34. Because Retention = f(user_happiness)Crashpadder’s Happiness Indexe.g. Weighted sum over core activities by hosts.Cohorts by cities and time.= Health/Happiness Dashboard
  • 35. AARRR misses something AcquisitionAnd Happiness is not everything Activation Retention Referral Revenue (c) Dave McClure
  • 36. CUSTOMER INTENT Acquisition Activation Retention Referral RevenueFULFILMENT OF CUSTOMER INTENT (c) Dave McClure
  • 37. Metrics are horrible way to understand customer intent (c) Dave McClure
  • 38. Metrics are horrible way to understand customer intent Customer Intent = His “Job to be done” Products are bought because they solve a “job to be done”. Learn about Jobs to be done Framework Watch: http://bit.ly/cc-jtbd (c) Dave McClure
  • 39. Startups are obsessed by their solutionAnd ignore the customers job/problem Market Job/ Problem Our Solution #scb13 – @andreasklinger
  • 40. Metrics are horrible way to understand customer intent (c) Dave McClure
  • 41. Metrics are horrible way to understand customer intent Great Way: Customer Interviews But: We bias our people, when we ask them. Even if we try not to. Reason: we believe our own bullshit. Watch: www.hackertalks.io (c) Dave McClure
  • 42. Metrics are horrible way to understand customer intent OK Way: Smoke Tests If interviews suggest a new feature but you are Download Mobile Client unsure about critical mass (e.g. due to sample bias). Create Smoke Tests measure Click Conversion/ Signups Not for verification but falsification (c) Dave McClure
  • 43. CUSTOMER INTENT (JOB) AcquisitionCustomerInterviews Activation Retention ReferralCustomerInterviews& Metrics Revenue FULFILMENT OF CUSTOMER INTENT (c) Dave McClure
  • 44. Dig deeper - Good product centric KPIs:Framework: AARRR #scb13 – @andreasklinger
  • 45. Dig deeper - Good product centric KPIs:Linked to assumptions of your product (validation/falsify)Rate or Ratio (0.X or %)Framework: AARRRComparable (To your history (or a/b). Forget the market)Explainable (If you don’t get it it means nothing) #scb13 – @andreasklinger
  • 46. “Industry Standards”Framework: AARRR Use industry averages as reality check. Not as benchmark. - Usually very hard to get. - Everyone defines stuff different. - You might end up with another business model anyway. - Compare yourself vs your history data. #scb13 – @andreasklinger
  • 47. Example Mobile App: Pusher2000Trainer2peer pressure sport app (prelaunch “beta”).Rev channel: Trainers pay monthly fee.Two sided => Segment AARRR for both sides (trainer/user)Marketplace => Value = Transactions / SupplierSocial Software => DAU/MAU to see if activated users stay activeChicken/Egg => You need a few very happy chickens for loads of eggs.Week/Week retention to see if public launch makes senseFramework: AARRROptimize retention: Interviews with Users that leftMeasure Trainer Happiness ScoreActivated User: More than two training sessionsPushups / User / Week to see if the core assumption (People will domore pushups) is valid #scb13 – @andreasklinger
  • 48. Dig Deeper - Dataschmutz A layer of dirt obfuscating your useable data. Usually “wrong intent”. Usually our fault. (~ sample noise we created ourselves) #scb13 – @andreasklinger
  • 49. Dataschmutz A layer of dirt obfuscating your useable data.e.g. Traffic Spikes of wrongcustomer segment.(have wrong intent) #scb13 – @andreasklinger
  • 50. Dataschmutz Exam MySugr is praised as “beautiful app” example.… => Downloads => Problem: Not all are diabetic They focus on people who activated.
  • 51. How to minimize the impact of Dataschmutz Base your KPIs on wavebreakers. WK visitors acquisition activation retention referral revenue twice aBirchbox visit 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%
  • 52. DataschmutzCompetitions create artificial incentive Competition Created “Dataschmutz” Competitions (before P/M Fit) “Would you use my app and might are nothing but Teflon Marketing win 1.000.000 USD?” * Users had huge extra incentive. People come.can hurt your numbers. * Marketing People leave. * While we decided on how to relaunch we had dirty numbers. #scb13 – @andreasklinger
  • 53. Dig Deeper - Metrics need to hurt #scb13 – @andreasklinger
  • 54. Dig Deeper - Metrics need to hurtIf you are not ashamed about the KPIs inyour dashboard than something is wrong.Either you do not drill deep enough.Or you focus on the wrong KPIs. #scb13 – @andreasklinger
  • 55. Dig Deeper - Metrics need to hurtExample: Garmz/LOOKKGreat Numbers:90% activation (activation = vote)But they only voted for friendsinstead of actually using the platform.We drilled (not far) deeper:Activation = Vote for 2 different designers. Boom. Pain. #scb13 – @andreasklinger
  • 56. User activation.Some users are happy (power users)Some come never again.What differs them? It’s their activities in their first 30 days.How we think about Churn is wrong. #scb13 – @andreasklinger
  • 57. Example TwitterHow often did activated usersuse twitter in the first month:7 timesWhat did they do?Follow 20 people, followedback by 10Churn:If they don’t keep them 7 timesin the first 30 days.They will lose them forever.It doesn’t matter when a userremembers to unsubscribe #scb13 – @andreasklinger
  • 58. Example TwitterExample Twitter:How did they get more peopleto follow 30people within7visits in the first 30 days?Ran assumptions, createdfeatures and ran experiments!Watch: http://www.youtube.com/watch?v=L2snRPbhsF0 #scb13 – @andreasklinger
  • 59. Checkout Intercom.ioCustomer segmenting and messaging done right. #scb13 – @andreasklinger
  • 60. Summary #scb13 – @andreasklinger
  • 61. Summary- Use Metrics for Product and Customer Development.- Use Cohorts.- Use AARRR.- Figure Customer Intent through non-biasing interviews.- Understand your type of product and it’s core drivers- Find KPIs that mean something to your specific product.- Avoid Telfonmarketing (eg Campaigns pre-product).- Filter Dataschmutz- Metrics need to hurt- Focus on the first 30 days of customer activation.TL;DR: Use metrics to validate/doublecheck.Use those insights when designing for/speaking to your customers. #scb13 – @andreasklinger
  • 62. Read onStartup metrics for Pirates by Dave 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-googleRob Fitzpatrick’s Collection of best Custdev Videos - @robfitzhttp://www.hackertalks.ioLean Analytics Bookhttp://leananalyticsbook.com/introducing-lean-analytics/Actionable Metrics - @lfittlhttp://www.slideshare.net/lfittl/actionable-metrics-lean-startup-meetup-berlinApp Engagement Matrix - Flurryhttp://blog.flurry.com/bid/90743/App-Engagement-The-Matrix-ReloadedMy Bloghttp://www.klinger.io #scb13 – @andreasklinger
  • 63. Thank you@andreasklinger #SCB13Slides: http://slideshare.net/andreasklingerAll pictures: http://flickr.com/commons #scb13 – @andreasklinger

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