Understanding these key indicators requires at least half the effort of data analysis

According to the user life cycle , We can basically decompose user behavior indicators into the following types ：

• Users get class indicators
• User activity indicators
• User retention indicators
• Product revenue indicators
• User communication indicators

01 Users get class indicators

（1） Channel arrivals ： Also known as exposure , Refers to the number of people who see product promotion clues , It also includes ad hits CTR.

（2） Channel conversion rate ： Conversion rate is the most widely used index , Include ：

• CPM： Cost per thousand people , Charge according to how many people see the advertisement .
• CPC： Cost per user click , Price per click .
• CPA： Cost per action , Pricing by user behavior .
• CPT： Cost per time period , Time based pricing .
• CPS and CPS Count in CPA Within the scope of .
• eCPM： Every 1000 shows can earn income , An indicator for advertisers to estimate their own revenue .

channel ROI： The return on investment ratio （ profits / investment *100%）

When operating activities ROI Greater than 1, It shows that this activity is successful , Can make money .

ROI It can also be extended to other indicators , For example, the number of registered users , That is, the cost of getting customers . Share a ROI Some screenshots of data analysis ：

 （3） Daily app downloads ： Just clicking download does not mean that the download is complete .

Download the third-party platform to the user registration App, The data in this step is prone to error , The main reason is that the users are not right . Technically through the only device ID matching .

（4） Daily new users ： Based on the user registration submission . New users can be further divided into ：

• Natural growth ： User invitation , User search, etc
• Promotion growth ： The number of users growing under the strong control of operators

（5） Customer acquisition cost ： To get the cost of a user

（6）CAC： The cost of getting an effective user

（7） Proportion of users in one session ： It means that new users have downloaded APP, Open it only once and use it for a period of time 2 Within minutes,

Such users are likely to be hackers or robots , Brush amount through various technologies , Get false hits for revenue . This indicator is a risk control indicator , Used to regulate .

02 User activity indicators

User activity is the core stage of operation , Regardless of the mobile end 、 Web or wechat , There are relevant indicators , It mainly includes ：

（1） Diurnal activity / Weekly activity / Monthly living ： The standard is that the user has used the product , Not limited to opening APP.

Activity rate ： The proportion of active users in the total number of users in a certain period of time . According to the time dimension, there are ：

• Daily activity rate DAU
• Weekly activity rate WAU
• Monthly activity rate MAU

Sometimes active users are subdivided ： new user 、 Active users 、 Loyal users 、 Inactive users 、 Lost users 、 Return to users, etc .

Picture source logo, Invasion and deletion

（2）PV and UV

• PV： Page views , A user's request for access to a web page can be regarded as a PV.
• UV： Number of unique visitors , That is, the number of times a user visits a page within a certain period of time .

Be careful ： Wechat browser will not be retained for a long time cookie, Mobile phone terminal IP And always changing , Based on this statistic UV There will be errors

It can be provided through wechat openid replace cookie As uv The benchmark , Need additional technical support .

（3） Proportion of active users ： Active users / Total users , Used to measure the health of products

（4） The user's session session frequency ： The user opens the product operation and use , The entire cycle to exit the product .

The default of web page is 30 Within minutes ,30 Minutes belong to one session , exceed 30 Minutes belong to the second session . The time window of the mobile terminal is... By default 5 minute .

The number of user sessions is combined with the number of active users , Be able to judge the stickiness of users .

For example, if the number of daily active users is 100, The number of daily sessions is 120, It means that most users have only visited the product once , Product is not sticky .

（5） User access time ： The duration of a conversation

If analysts find that most users of content products only visit for tens of seconds , Then you'd better analyze the reason .

03 User retention indicators

Users use the product for a certain period of time , After a while , Users who continue to use , They are called retained users .

Retention = Users who are still using / The original total number of users

（1） User retention indicators can be broken down into ： The next day 、 Three days 、 Seven days 、 For 30 days .

Facebook There is a famous 40-20-10 The laws of , That is, the retention rate of new users in the next day is 40%, The seven day retention rate is 20%, The 30 day retention rate is 10%, The products with this performance belong to those with good data .

 （2） User churn rate , Be able to predict the development of products .

The churn rate of users should be specifically analyzed according to the problems , For example, tourism applications , Users can not open it several times a year , But it can still develop .

（3） Exit rate ： Number of page visits exited from this page / Number of page visits to the page

For example, a product page enters PV1000, The number of accesses to this page that are closed directly is 300, Then the exit rate 30%.

Jump rate is a special form of exit rate , The number of times you have to exit after browsing only one page / Number of visits , Browsing only one page means that this is the first page for the user to enter the website , Commonly known as landing page LandingPage.

Exit rate is used for web page structure optimization , Content optimization . Jump out rate is often used in the analysis of promotion and operation activities , It's easy to confuse the two .

04 Revenue data indicators

It mainly includes ：

1. Number of paying users
2. Proportion of paid users ： Daily paying users / Ratio of active users perhaps Total paying users / Total users
3. ARPU： Average income of users in a certain period of time
4. ARPPU. The average revenue per paying user in a certain period of time , Get rid of unpaid
5. Customer unit price ： The average amount of goods purchased by each user . Total sales / Total number of customers （ Mainly used in e-commerce and retail ）
6. Repeat purchase rate ： Number of purchases ＞1 People who / All the people who have bought
7. LTV： User life cycle value .LTV=ARPU*1（ Use less because you can't see the effect in the short term ）

05 User communication indicators

（1）K factor ： Each user can bring several new users

K factor = The number of users * The average number of invitations * Invitation conversion rate

When K factor ＞1 when , Each user can bring at least one new user , The number of users will snowball , Finally, self propagation is achieved . When K When the factor is large enough , It is the viral marketing that is passed down from mouth to mouth .

（2） Share rate of users ： A function or page , Share users as a percentage of the number of page views

Finally, I would like to share with you a classic management model of the traditional retail industry ：

Purchase, sale and deposit

It is about to split the enterprise commodity management purchase 、 Put in storage 、 There are three links in sales , And establish a full link data system . In real business , Many scenarios are closely related to purchase, sales and inventory .

  Figure source Jian Daoyun

Just briefly ：

• Into the ： Purchase order 、 Purchase and warehousing 、 Purchase return
• pin ： offer 、 Sign the contract 、 Return shipment
• save ： In and out , Stock transfer , Stock taking

Some will have another layer ：

• goods ： Payment collection 、 Billing revenue and expenditure 、 Statistical reconciliation

The business logic of the whole model is like this ：

above .