3 Common Mistakes When Looking At Freemium Metrics

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Stam Beremski @ NaturalMotion presentation for 2013 Games Industry Analytics Forum

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3 Common Mistakes When Looking At Freemium Metrics

  1. 1. 3 COMMON MISTAKES WHEN LOOKING AT FREEMIUM METRICSSunday, 3 February 13
  2. 2. INTRODUCTION • Product Lead at NaturalMotion • Previously worked on: stan.beremski@naturalmotion.comSunday, 3 February 13
  3. 3. OVERVIEW OF FREEMIUM GAMESSunday, 3 February 13
  4. 4. THE FREEMIUM MODEL App Store Engage Retain Monetize + InviteSunday, 3 February 13
  5. 5. Good metrics give you insight into conversion at each stage of the model App Store Engage Retain Monetise + Invite Good metrics have explanatory powersSunday, 3 February 13
  6. 6. MISTAKE #1 NOT FOCUSING ON EXPLANATORY METRICSSunday, 3 February 13
  7. 7. Vanity Explanatory • Users • Total Revenue / Total Users • Cohort segmentation • Page Views • Session per player / day • Retention • Daily Revenue • Invites sent / player • LTV • Total Mins of Play • Invites accepted / Invite • Sessions pp / day • Total Sessions • ARPDAU • DAU/MAU • Behavioural segmentation • Whales vs Free • Single player vs Multi • Funnels • Tutorial / Quest • Virality funnel Counts Ratios Segmented RatiosSunday, 3 February 13
  8. 8. MISTAKE #2 NOT CONTROLLING FOR VARIABLESSunday, 3 February 13
  9. 9. You have just released an update to your game and you take a look at the metrics to gauge success... • Did this update improve monetisation? • How long did it take to us get a conclusive answer? (Data is fictional)Sunday, 3 February 13
  10. 10. There is a problem if you only look at New Users, Revenue and ARPDAU to gauge the success of an update... (Data is fictional) Problem: Metrics can be affected by uncontrolled variables • In our fictional game players spend 90% of their LTV within 7 days of first playing the app • An large influx of new players will cause a revenue spike even if the app remains unchanged Solution: Look at metrics which isolate what you are interested in and control for other variables (in this case the player’s lifecycle within the app)Sunday, 3 February 13
  11. 11. The solution is to look at player LTV at specific points in the player’s lifecycle Total Revenue from cohort Player LTV N days into LTV = Day N LTV = the lifetime of a cohort Total # Player of a cohort (Data is fictional)Sunday, 3 February 13
  12. 12. MISTAKE #3 NOT LOOKING AT THE DISTRIBUTION OF UNDERLYING DATASunday, 3 February 13
  13. 13. We often look at averaged data. {1,2,3,3,3,4,4,5,10} Mean = 3.89 Median = 3 Mode = 3Sunday, 3 February 13
  14. 14. Power-law distribution are common in freemium games Represents $65,000 revenue from110,000 players Max: $624 Min: $0 (Data is fictional) Mean: $0.57 Median: $0 Mode: $0 Std Dev: 7.12 % Payers: 5%Sunday, 3 February 13
  15. 15. But statistics can be misleading... Property Value Mean  of  x  in  each  case 9  (exact) Variance  of  x  in  each  case 11  (exact) Mean  of  y  in  each  case 7.50  (to  2  decimal  places) Variance  of  y  in  each  case 4.122  or  4.127  (to  3  decimal  places) CorrelaAon  between  x  and  y  in  each  case 0.816  (to  3  decimal  places) Linear  regression  line  in  each  case y  =  3.00  +  0.500x  (to  2  and  3  decimal  places,  respecAvely)Sunday, 3 February 13

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