Successfully reported this slideshow.

Metrics for early stage startups

201

Share

Loading in …3
×
1 of 63
1 of 63

More Related Content

Related Books

Free with a 14 day trial from Scribd

See all

Related Audiobooks

Free with a 14 day trial from Scribd

See all

Metrics for early stage startups

  1. Metrics for Early-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 have ideas for a new product. Looking for customers to buy (or at least use) it. Customers don’t buy. “early stage” #scb13 – @andreasklinger
  7. Product/Market Fit traction time With early stage I do not mean “X Years” I mean before product/market fit. #scb13 – @andreasklinger
  8. Product/market fit Being in a good market with a product that can satisfy that market. ~ Marc Andreessen #scb13 – @andreasklinger
  9. Product/market fit Being in a good market with a product that can satisfy that market. ~ Marc Andreessen = People want your stuff. #scb13 – @andreasklinger
  10. Product/Market Fit traction time #scb13 – @andreasklinger
  11. Product/Market Fit traction time Discovery Validation Efficiency Scale Steve Blank - Customer Development #scb13 – @andreasklinger
  12. Product/Market Fit traction time Discovery Validation Efficiency Scale Find a product the market wants. #scb13 – @andreasklinger
  13. Product/Market Fit traction 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 clones for new product start here. start here. #scb13 – @andreasklinger
  15. Product/Market Fit traction time Discovery Validation Efficiency Scale Product & Customer Scale Marketing Development & Operations #scb13 – @andreasklinger
  16. Product/Market Fit traction time Discovery Validation Efficiency Scale Startups have phases but they overlap. #scb13 – @andreasklinger
  17. Product/Market Fit traction time Discovery Validation Efficiency Scale 83% of all startups are in here. #scb13 – @andreasklinger
  18. Product/Market Fit traction 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 in non 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 Stage Focus on People - Not Hits, Pageviews, Visits, Events Validation 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 Cohorts Cohorts = Groups of people that share attributes. #scb13 – @andreasklinger
  24. Segment Users into Cohorts #scb13 – @andreasklinger
  25. Apply a framework: AARRR #scb13 – @andreasklinger
  26. Acquisition Visit / Signup / etc Activation Use of core feature Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  27. Example: Photoapp Cohorts based on registration week WK acquisition activation retention referral revenue twice a Photoapp registration first photo share … month 1 400 62,5% 25% 10% 2 875 65% 23% 9% 3 350 64% 26% 4% … … … … …
  28. Acquisition Visit / Signup / etc Activation Use of core feature Which Metrics to focus on? Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  29. Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: Retention Focus on Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  30. Acquisition Visit / Signup / etc Activation Use of core feature Long answer - It depends on two things: Retention Come + use again Phase of company Referral Invite + Signup Type of Product (esp. Engine of Growth) Revenue $$$ 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 $$$ Earned Source: Lean Analytics Book - highly recommend
  32. Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: Retention Focus on Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure
  33. Because Retention = f(user_happiness)
  34. Because Retention = f(user_happiness) Crashpadder’s Happiness Index e.g. Weighted sum over core activities by hosts. Cohorts by cities and time. = Health/Happiness Dashboard
  35. AARRR misses something Acquisition And Happiness is not everything Activation Retention Referral Revenue (c) Dave McClure
  36. CUSTOMER INTENT Acquisition Activation Retention Referral Revenue FULFILMENT 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 solution And 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) Acquisition Customer Interviews Activation Retention Referral Customer Interviews & 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: AARRR Comparable (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: Pusher2000 Trainer2peer pressure sport app (prelaunch “beta”). Rev channel: Trainers pay monthly fee. Two sided => Segment AARRR for both sides (trainer/user) Marketplace => Value = Transactions / Supplier Social Software => DAU/MAU to see if activated users stay active Chicken/Egg => You need a few very happy chickens for loads of eggs. Week/Week retention to see if public launch makes sense Framework: AARRR Optimize retention: Interviews with Users that left Measure Trainer Happiness Score Activated User: More than two training sessions Pushups / User / Week to see if the core assumption (People will do more 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 wrong customer 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 a Birchbox 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. Dataschmutz Competitions 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 hurt If you are not ashamed about the KPIs in your 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 hurt Example: Garmz/LOOKK Great Numbers: 90% activation (activation = vote) But they only voted for friends instead 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 Twitter How often did activated users use twitter in the first month: 7 times What did they do? Follow 20 people, followed back by 10 Churn: If they don’t keep them 7 times in the first 30 days. They will lose them forever. It doesn’t matter when a user remembers to unsubscribe #scb13 – @andreasklinger
  58. Example Twitter Example Twitter: How did they get more people to follow 30people within 7visits in the first 30 days? Ran assumptions, created features and ran experiments! Watch: http://www.youtube.com/watch?v=L2snRPbhsF0 #scb13 – @andreasklinger
  59. Checkout Intercom.io Customer 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 on Startup metrics for Pirates by Dave 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 Rob Fitzpatrick’s Collection of best Custdev Videos - @robfitz http://www.hackertalks.io Lean Analytics Book http://leananalyticsbook.com/introducing-lean-analytics/ Actionable Metrics - @lfittl http://www.slideshare.net/lfittl/actionable-metrics-lean-startup-meetup-berlin App Engagement Matrix - Flurry http://blog.flurry.com/bid/90743/App-Engagement-The-Matrix-Reloaded My Blog http://www.klinger.io #scb13 – @andreasklinger
  63. Thank you @andreasklinger #SCB13 Slides: http://slideshare.net/andreasklinger All pictures: http://flickr.com/commons #scb13 – @andreasklinger

×