Data-centric Design & Operation in Social game
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Data-centric Design & Operation in Social game

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Data-centric Design & Operation in Social game Data-centric Design & Operation in Social game Presentation Transcript

  • Data centric Design & OperationA data-driven and scientific approach for game business Nguyễn Chí Hiếu - Japan Dept – VNG Corporation
  • Table of content Methodology of data-centric approach What is data ? Disclaimers Unit economy
  • Methodology of data centric approachJapanese Methodology & Principle buzzword: Kaizen Just-in-time principle View slide
  • Methodology of data centric approach Good at Math Bad at Math Opposite of Good at Math is not good at Literature or Creativity Data-Centric # Limitation of Creativity View slide
  • What is data ? Is this the “data” we looking for ?
  • Data-centric: Disclaimer Option B Option A We still need a good game to start with
  • Unit Economy Profit = ( Revenue per User – Cost per User ) X Number of UserNumber of User  AcquisitionRevenue per User  Retention  Monetization  Life Time Value
  • Acquisition – User funnels Everyone Internet user Gamer Platform user base Target Segment Ads Awareness Interest Desire Action Registration Download client Chose character Tutorial Play Stay Regular player Payer Regular payer …….
  • Acquisition – Viral K-factor K-factor measurement needs reliable viral mechanic. Viral is becoming less and less effective.
  • Unit Economy Profit = ( Revenue per User – Cost per User ) X Number of UserNumber of User  AcquisitionRevenue per User  Retention  Monetization  Life Time Value
  • Retention  Let’s have a look at how new users stay in our games.
  • RetentionPercentage of staying user/total user 100.00% 90.00% 80.00% 28-02-12 70.00% 27-02-12 60.00% 26-02-12 50.00% 25-02-12 40.00% 24-02-12 30.00% = 23-02-12 20.00% 10.00% 0.00% Date 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28  Normalization chart for user retention over 1 month on daily basis.
  • RetentionStaying user 45000 40000 40945 35000 30000 25000 Staying User 20000 15000 14,318 10000 5000 6,883 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29  Normalization chart for user retention over 1 month.  How do we keep user ?  How did they leave ?
  • Retention  Begging the players: “Don’t leave me, I can change for you” ?
  • Retention  Lock them up ?  Any better idea ?  Let’s stay by asking ourself: “Why do users stay ?”
  • Retention User retention at a closer look.  How users funnel into your game.  How do you impress your player.  “Don’t make me think” - KISS (Keep It Stupidly Simple).  How can user understand “core design”.  Do you have Retention Features in your game cycle.  What is your Retention Feature KPIs.  1st Login to 2nd Login.  Define your Hardcore/Reg user.  ……
  • Unit Economy Profit = ( Revenue per User – Cost per User ) X Number of UserNumber of User  AcquisitionRevenue per User  Retention  Monetization  Life Time Value
  • MonetizationHow we frequently look at the most important part of our business: ARPU : Average Revenue Per User ARPPU : Average Revenue Per Paying User DARPU : Daily Average Revenue Per Paying User MARPU : Monthly Average Revenue Per Paying User Conversion Rate. Paying User Rate. Sale charts.Is that all ?Can we do better ?Why do user pay ?
  • Unit Economy Profit = ( Revenue per User – Cost per User ) X Number of UserNumber of User  AcquisitionRevenue per User  Retention  Monetization  Life Time Value
  • Life Time Value Life Time Value = Total Revenue you get from 1 user until they cease to be your user. Profit = (Cost per User – Life Time Value) X Number of User. Most reliable Life Time Value is historical data. Historical data = history, you need some way to predict, or project your Life Time Value 2 most simple Life Time Value Models on Cohort basis: LTV = ARPPU x Paying Rate x User Life Time = ARPU X User Life Time LTV = ARPPU x Paying User x Paying User Life Time
  • Life Time Value – User Life TimeStaying user 45000 40000 40945 35000 30000 25000 Staying User 20000 15000 14,318 10000 5000 6,883 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29  Normalization chart for user retention over 1 month.
  • Life Time Value – User Life Time  Projecting object lifetime is an old problem.
  • Life Time Value – Poisson Distribution
  • Life Time Value - Segmentation Segmentation criteria  Daily cohort basis.  Marketing Campaign basis.  User source.  User behavior.  User Demographic.
  • Life Time Value - Segmentation  Banner A user LTV: 1$  Banner A LTV: 1,000$ Banner A new users: 1,000 Banner A Profit: 500$ Banner A cost: 500$ Banner B new users: 5,000  Banner B user LTV: 0.1$ Banner B cost: 1,000$  Banner B LTV: 500$ Banner C new users: 500 Banner B Profit: -500$ Banner C cost: 500$  Banner C user LTV: 3$Total new user: 6,500  Banner C LTV: 1,500$Total banner cost: 2,000$ Banner C Profit: 1,000$
  • Unit Economy Profit = ( Revenue per User – Cost per User ) X Number of UsersNumber of Users  AcquisitionRevenue per User  Retention  Monetization  Life Time Value
  • Thank you !My Contact: hieunc@vng.com.vn