data centric design & operation: a data-driven and scientific approach for game business
DESCRIPTION
TRANSCRIPT
Data centric Design & OperationA data-driven and scientific approach for game business
Nguyễn Chí Hiếu - Japan Dept – VNG Corporation
Methodology of data-centric approachWhat is data ?DisclaimersUnit economy
Table of content
Methodology of data centric approach
Japanese Methodology & Principle buzzword:KaizenJust-in-time principle
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
What is data ?
Is this the “data” we looking for ?
Option AOption B
We still need a good game to start with
Data-centric: Disclaimer
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
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 …….
K-factor measurement needs reliable viral mechanic. Viral is becoming less and less effective.
Acquisition – Viral K-factor
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
Let’s have a look at how new users stay in our games.
Retention
Normalization chart for user retention over 1 month on daily basis.
0 2 4 6 8 10 12 14 16 18 20 22 24 26 280.00%
10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
=
28-02-1227-02-1226-02-1225-02-1224-02-1223-02-12
Percentage of staying user/total user
Date
Retention
Normalization chart for user retention over 1 month. How do we keep user ? How did they leave ?
1 3 5 7 9 11 13 15 17 19 21 23 25 27 290
50001000015000200002500030000350004000045000
40945
14,318
6,883
Staying User
Staying user
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. ……
Retention
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
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
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
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
Normalization chart for user retention over 1 month.
1 3 5 7 9 11 13 15 17 19 21 23 25 27 290
50001000015000200002500030000350004000045000
40945
14,318
6,883
Staying User
Staying user
Life Time Value – User Life Time
Life Time Value – User Life Time
Projecting object lifetime is an old problem.
Life Time Value – Poisson Distribution
Segmentation criteria Daily cohort basis. Marketing Campaign basis. User source. User behavior. User Demographic.
Life Time Value - Segmentation
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
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
