A good business is data-driven, not operations-driven. Data analysis is the most basic operation skill, how to give full play to the value of data? Do you know all these operational data and formulas?


Basic data
DNU: Daily New Users
DAU: Daily Active Users
DOSU: Daily One Session Users
Number of Users logged in within 7 days (WAU: Weekly Active Users)
Monthly Active Users within 30 days
Monthly churn = users logged in 30 days ago, users not logged in within 30 days/MAU
Weekly churn = 7 days before login, 7 days after login users/WAU
Daily churn = login on the statistical day, number of users not logged in the next day/DAU on the statistical day
Retention rate = Number of newly added users/Number of newly added users (General statistical period is days)
Next-day retention rate = The number of new users on the second day of registration/the number of new users on the first day
Retention rate on day 3 = Number of users added on day 1 / Number of users added on day 1
Retention rate on day 7 = Number of users added on day 1 / total number of users added on day 1
Retention rate on day 30 = Number of users added on day 1 / number of users added on day 1
Recharge amount: the total recharge amount within a certain period
Amount spent: The total amount spent by a player in the game store
Advanced data
Average Concurrent Users (ACU) : Captures data once within a certain period of time. Average data of ACU within a cycle
PCU:Peak Concurrent Users: indicates the highest number of online users captured at a specified time.
ARPPU: Average Revenue Per Paying User (ARPPU) : Similar to the percentage spent on downloaded games, this is a measure of Revenue from Paying users
ARPPU = Total monthly Revenue/Average Revenue Per active User (ARPU: Average Revenue Per User) : Measures the overall contribution of a game; In addition to paid revenue, active users can also generate revenue. Generally, this data is the core in China, and different algorithms are different
ARPU = Total monthly revenue/Average Monthly Active User Life Cycle: the number of days between the first time a new account enters the game and the last time it participates in the game
Average life cycle = Sum of life cycles of each new user/MAU
LTV: Life Time Value
Life cycle value = total recharge amount/number of conditional accounts (accounts meeting agreed cycle conditions)
Cost data
Input/operating costs: The amount of marketing and marketing spent to promote the game this month
Output/Dollar amount spent: The total amount spent in the game during the player’s period (day/week/month)
Input-output ratio (ROI) = output of the month/input of the month
Promotion cost per active user = monthly investment/monthly new active users
Promotion cost per paying user = monthly investment/monthly new paying users
User state
New active users: specifies the first online users
Lost active users: the number of users who logged in in the previous period (7-14 days) and did not log in in the current period (the last 14 days)
Backflow active users: the number of users who are not logged in in the previous period (7-14 days) and are logged in in the current period (the last 7 days)
Active user Churn rate = Lost users this month/Active users last month
Active user top-up rate = monthly active paying users/Monthly active users
Active User Online duration (unit/hour) = Total online duration of all active users in the current period/Active users in the current period (7 days)
Paid user Online duration (unit/hour) = Total online duration of all paid users in the current period/Paid users in the current period
Recharge rate of Newly active users = Newly recharged login users within this month/Total newly added login users in this month
High Number of Newly active users = High number of newly active users in this month/Number of newly logged users in this month
…
Knowing these statistics, how would you count them?
Of course, Excel is an indispensable tool for business students to sort out data every day. However, big data, group portrait, user tag, accurate analysis and other related words are frequently used in the workplace of business practitioners. If you want to stand out in the field of data analysis, only one Excel is obviously a little thin.
Where does all this data come from? Is it the program that provides the data extraction function. How does the data provided by the programmer show up?
Like this?



The or so?



Operations always put forward various requirements to other departments, for users, for products… In fact, now you can ask for your own needs for a job you’re more comfortable with. Let Mober teach you how to convince your app monkey brother to voluntarily use MobSDK’s AnalySDK, with which you can run several streets without other data.
AnalySDK developers, are big data team gods, they make SDK products, stable, reliable and comprehensive layout, to provide users with the following eight data statistics functions, comprehensive coverage of the required data. Let mobile developers edit code to own data capabilities without having to build their own backend.



No longer need to save one formula after another into Excel, just the corresponding data in the background operation, you can easily obtain the results, from a small white to become a data giant in the near future.
Here’s an example:
If you use your own table to calculate the recent user retention rate, you need to manually add all the relevant data to Excel, and then follow the retention rate formula:
Retention rate = Number of newly added users/number of newly added users
And the retention formula for different days of statistics:
Next-day retention rate = The number of new users on the second day of registration/the number of new users on the first day
Retention rate on day 3 = Number of users added on day 1 / Number of users added on day 1
And so on… To the table for data processing, design, collation and then output results



In the background provided by AnalySDK, we have written these formulas into the program, you can directly select the data you want to statistics, such as “user retention rate”, select the time period to view, and user group, you can view the corresponding data of the user group during the day:



If from ape to hand took the back-end data are as has good PPT, beautiful, intuitive, and everything, can also according to different requirements to operate a variety of grouping data refinement analysis, no longer just a thin text data, also can also don’t have to manually edit form, automatically generate data statistics, presenting a variety of suitable icon… Is it a surprise?
AnalySDK also has a lot of subdivided data statistics auxiliary functions, welcome to enjoy, and put forward valuable opinions for us in use. After that, Mober will continue to update the introduction of our products, looking forward to your continuous attention.