Eng: Mobile Game Data Analysis F
1. Acquisition: analysis in channel, ad injection
Daily New Users, DNU: users who sign up and sign in games per day
functions:
a. reflect the new-user contribution of each channels
b. check the channel cheating
c. visualize the macro trend and decide whether need advertisement injection
note:
similar metrics: WNU, MNU for week and month
According to requirements, category as users in natural growth and users in promotion
Daily One Session Users，DOSU: user who only has one session and session time is less than the regulated threshold
functions:
a. detect click farming in promotion channels
b. check the quality of channels
c. check obstacles during importing users: network situation, loading time
Customer Acquisition Cost，CAC = promotion cost / number of efficient new sign-in users
functions:
a. determine to choose the right channel to optimize advertisement injection
b. estimate the cost of channels promotion
note:
CAC is calculated by segmenting channels
New Users Conversion Rate: Clicks->Install->Register->Login
2. Activation
Daily Active Users，DAU: number of sign-in users per day
functions:
a. kernel user scale
b. measure the trend of the game life time
c. compare user churn rate and active rate
d. active user life time in channels
e. user stickiness/retention (with MAU)
note:
similar metrics: WAU, MAU for week and month
MAU is also for user scale stability, and estimating promotion effectiveness
Daily Engagement Count，DEC：the number of opening games for users per day
functions:
a. user stickiness (average DEC)
b. channel-oriented, check frequency
c. user-oriented, check frequency
note:
behaviors in 30 seconds as 1 DEC
average DEC = DEC / daily engagement user count
analyze performance after updating version by different DEC distribution
Daily Avg.Online Time，DAOT/AT: online time per active users each day
functions:
a. degree of paticipation
b. game quality metric
c. channel quality metric
d. combine with Average Online Time per sign-in to analysis retention and user churn
note:
help analyze cheating, version stickiness and effectiveness
3. Retention & Churn
Users Retention: case of using for each new sign-in user in regulated periods: day1, day3, day7, day30
functions:
a. users' adaptability to game
b. evaluate user quality in channels
c. channel quanlity
d. user stickiness
e. detect steep-loss stage for new users
note:
retention is metric reflecting users' satisfaction
retention is talked along with churn
Users Churn: case of leaving in regulated periods: day1, day7, day30
functions:
a. active user life time
b. channel quality
c. detect influence of version update
d. detect period with high churn rate
4. Revenue
a. revenue from download
b. revenue from ad in games
c. revenue from in-app purchase
Daily Payment Ratio，DPR = APA / DAU, APA is Active Payment Account
a. check the rationality of the paying lead
b. reflect users paying intention
c. check the conversion of paying
Active Payment Account，APA
functions：
a. scale of paying users
b. portion of APA: whales, dolphins, minnows
c. stability of paying users
Average Revenue per Uers，ARPU
eg: for months, ARPU = Revenue / MAU
Average Revenue per Paying User，ARPPU
ARRPU = Revenue / APA
ARPPU is easily affected by whales and minnows.
ARPPU, APA and MPR are combined to analyze retention of paying users.
Life Time Value，LTV
LTV = ARPULT, by month
Value from the first time that users join in games to the last time.
5. Referral
K-Factor：describe the growth rate of websites
K-Factor= number of shares * conversion rate
K > 1, fast growth
Others:
Peak Concurrent Users, PCU
Average Concurrent Users, ACU
