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