Data centric design and operation
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This document discusses data-centric design and operation for game businesses. It introduces key concepts like unit economy, acquisition, retention, monetization, and lifetime value. Acquisition focuses on user funnels and viral growth. Retention examines normalization charts and keeping users engaged. Monetization looks at metrics like ARPU and considers why users pay. Lifetime value models total revenue per user over their lifespan using techniques like Poisson distribution and segmentation. The goal is to use data to optimize the user experience and business metrics at each stage of the user journey.
Data centric design and operation
- 1. Data centric Design & Operation A data-driven and scientific approach for game business Nguyễn Chí Hiếu - Japan Dept – VNG Corporation
- 2. Methodology of data-centric approach What is data ? Disclaimers Unit economy Table of content
- 3. Methodology of data centric approach Japanese Methodology & Principle buzzword: Kaizen Just-in-time principle
- 4. Good at Math Bad at Math Opposite of Good at Math is not good at Literature or Creativity Data-Centric # Limitation of Creativity Methodology of data centric approach
- 5. What is data ? Is this the “data” we looking for ?
- 6. Option A Option B We still need a good game to start with Data-centric: Disclaimer
- 7. Profit = ( Revenue per User – Cost per User ) X Number of User Number of User Acquisition Revenue per User Retention Monetization Life Time Value Unit Economy
- 8. 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 …….
- 9. K-factor measurement needs reliable viral mechanic. Viral is becoming less and less effective. Acquisition – Viral K-factor
- 10. Profit = ( Revenue per User – Cost per User ) X Number of User Number of User Acquisition Revenue per User Retention Monetization Life Time Value Unit Economy
- 11. Let’s have a look at how new users stay in our games. Retention
- 12. Normalization chart for user retention over 1 month on daily basis. = 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 28-02-12 27-02-12 26-02-12 25-02-12 24-02-12 23-02-12 Percentage of staying user/total user Date Retention
- 13. Normalization chart for user retention over 1 month. How do we keep user ? How did they leave ? 40945 14,318 6,883 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Staying User Staying user Retention
- 14. Begging the players: “Don’t leave me, I can change for you” ? Retention
- 15. Lock them up ? Any better idea ? Let’s stay by asking ourself: “Why do users stay ?” Retention
- 16. 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. …… Retention
- 17. Profit = ( Revenue per User – Cost per User ) X Number of User Number of User Acquisition Revenue per User Retention Monetization Life Time Value Unit Economy
- 18. How 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 ? Monetization
- 19. Profit = ( Revenue per User – Cost per User ) X Number of User Number of User Acquisition Revenue per User Retention Monetization Life Time Value Unit Economy
- 20. 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
- 21. Normalization chart for user retention over 1 month. 40945 14,318 6,883 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Staying User Staying user Life Time Value – User Life Time
- 22. Life Time Value – User Life Time Projecting object lifetime is an old problem.
- 23. Life Time Value – Poisson Distribution
- 24. Segmentation criteria Daily cohort basis. Marketing Campaign basis. User source. User behavior. User Demographic. Life Time Value - Segmentation
- 25. Banner A new users: 1,000 Banner A cost: 500$ Banner B new users: 5,000 Banner B cost: 1,000$ Banner C new users: 500 Banner C cost: 500$ Total new user: 6,500 Total banner cost: 2,000$ Banner A user LTV: 1$ Banner A LTV: 1,000$ Banner A Profit: 500$ Banner B user LTV: 0.1$ Banner B LTV: 500$ Banner B Profit: -500$ Banner C user LTV: 3$ Banner C LTV: 1,500$ Banner C Profit: 1,000$ Life Time Value - Segmentation
- 26. Profit = ( Revenue per User – Cost per User ) X Number of Users Number of Users Acquisition Revenue per User Retention Monetization Life Time Value Unit Economy
- 27. Thank you ! My Contact: hieunc@vng.com.vn