1. Data centric Design & Operation
A data-driven and scientific approach for game business
Nguyễn Chí Hiếu - Japan Dept – VNG Corporation

2. Table of content
 Methodology of data-centric approach
 What is data ?
 Disclaimers
 Unit economy

3. Methodology of data centric approach
Japanese Methodology & Principle buzzword:
 Kaizen
 Just-in-time principle

4. 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

5. What is data ?
Is this the “data” we looking for ?

6. Data-centric: Disclaimer
Option B
Option A
We still need a good game to start with

7. Unit Economy
Profit = ( Revenue per User – Cost per User ) X Number of User
Number of User
 Acquisition
Revenue per User
 Retention
 Monetization
 Life Time Value

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. Acquisition – Viral K-factor
 K-factor measurement needs reliable viral mechanic.
 Viral is becoming less and less effective.

10. Unit Economy
Profit = ( Revenue per User – Cost per User ) X Number of User
Number of User
 Acquisition
Revenue per User
 Retention
 Monetization
 Life Time Value

11. Retention
 Let’s have a look at how new users stay in our games.

12. Retention
Percentage 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.

13. Retention
Staying 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 ?

14. Retention
 Begging the players: “Don’t leave me, I can change for you” ?

15. Retention
 Lock them up ?
 Any better idea ?
 Let’s stay by asking ourself: “Why do users stay ?”

16. 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.
 ……

17. Unit Economy
Profit = ( Revenue per User – Cost per User ) X Number of User
Number of User
 Acquisition
Revenue per User
 Retention
 Monetization
 Life Time Value

18. Monetization
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 ?

19. Unit Economy
Profit = ( Revenue per User – Cost per User ) X Number of User
Number of User
 Acquisition
Revenue per User
 Retention
 Monetization
 Life Time Value

20. 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

21. Life Time Value – User Life Time
Staying 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.

22. Life Time Value – User Life Time
 Projecting object lifetime is an old problem.

23. Life Time Value – Poisson Distribution

24. Life Time Value - Segmentation
Segmentation criteria
 Daily cohort basis.
 Marketing Campaign basis.
 User source.
 User behavior.
 User Demographic.

25. 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$

26. Unit Economy
Profit = ( Revenue per User – Cost per User ) X Number of Users
Number of Users
 Acquisition
Revenue per User
 Retention
 Monetization
 Life Time Value

27. Thank you !
My Contact: hieunc@vng.com.vn