Research on the index analysis of mobile internet products

The essence of mobile Internet products is the transmission and exchange of information and data, and its business model is closely related to data flow, so it will produce many methods, such as product design oriented to operation.

Traffic statistics refers to the relevant indicators of product use statistics. Operation analysis is to obtain the basic data of traffic statistics under the premise, the relevant data statistics, analysis, to find out the rules of user access and use, and these laws and marketing strategies, so as to identify the current business activities may exist problems, and for further revision or re-establishment of operational strategies to provide a basis.

The Internet and mobile Internet operational analysis indicators are basically similar, but mobile internet products with their own characteristics.

1. Common indicators

1.1. Data Traffic KPIs

Data traffic Statistics KPI is most commonly used to evaluate the effect of mobile Internet products, the main statistical indicators include:

1) Traffic (PV): That is, the app opens or page views and clicks, each user access is recorded 1 times. The user accesses the same page more than once, and the value accumulates. It is important to note that some of the pages in the product are obtained from the server, the client is local, and the data acquisition and Fusion is noticed.  
2) Daily visits: The average number of visits per day for all users within the corresponding time range.  
3) maximum daily visits: refers to the maximum number of visits for a given day within the corresponding time range.  
4) PV percentage: Refers to the ratio of PV in a category to the total PV in the selected time range.  
5) standalone device: refers to the total number of independent devices accessing the product within one day (00:00-24:00). IP is generally used in the Internet, and the same IP address is only counted 1 times. But the mobile Internet cannot be distinguished by IP, but by using mobile device identities.  
6) Independent user (UV): The user in the product may have different types, the registered user wants to be easy to count, but for non-registered users, each individual device is generally regarded as a user, referring to the number of users that are accessed and used within one day (00:00-24:00). Access to the same device is calculated 1 times within a day.  
7) UV percent: refers to the proportion of UV in a category that is the total UV in the selection time range.  
8) Active Users: a user or mobile device is re-accessed as an active user, the number of which is the number of active users.  
9) Percentage of active Users: the percentage of active users for all users.  
10) Number of active users: refers to the number of times a user or mobile device accesses or uses a product in addition to its first visit.  
11) Number of pages per capita: refers to the average number of pages per user using the product within the corresponding time range.

1.2. User Behavior KPI

User behavior KPIs primarily reflect how users access products, how long they stay on the service, which pages they visit, and the main statistical indicators include:
12) Depth of access (Depth of Visit): The number of pages visited by the user during a complete product use. Access to more pages, the higher the depth, the depth of access can be understood as the average number of page access to another form, but also to measure the viscosity of the site index.
13) Number of new users: the first time a mobile device is accessed as a new user, the added value of the new registered user is greater.
14) Number of recently used users: Latest user statistics, the number of independent users using the product in the most recent period, in reverse order of "entry Time".
15) Number of concurrent users: the number of UV-based online use in a certain time range, such as 1 minutes, for products with long link service, can be determined by the number of long links in a certain time or period.
16) Maximum number of hours online: The number of unique users who are at the highest simultaneous online within an hour within the corresponding time frame. Note: "Days" is in 24 hours (00:00-24:00).
17) Use the portal page ratio: each time the product is used, the user enters the first page from the homepage
18) Use the Export page ratio: each time the product is used, the user ends the last page when it is used.
19. Click Density Analysis: Directly respond to where the user clicked on the product's page.
20) User dwell time: The duration of the user's use of the product.
21) Average dwell time: The average of the duration of the use process for all users.
22) Source Analysis: Analysis of the user's source type, source page statistics. You can focus on the amount of traffic the user jumps between pages within the product.
23) Total data: The sum of all data volumes since the date of launch of the product.
24) Usage Change rate: refers to the corresponding data item in the current time period, compared with the previous time period, the use of the product's rate of change. For example, last week's change in traffic rate was ↓21.1%, indicating that last week's visit was down 21.1% from last week's visit. As another example, today 10:00-11:00 's traffic change rate is ↑1.3%, which means that today 10:00-11:00 's traffic rose 1.3% from yesterday's 10:00-11:00.
25) visited page: Analysis of the product in addition to the first page and landing page of the flow of the various pages, and its time-varying trend.
26) Access path: Each user from entering the first page, until the last left, the entire process has been browsed by the pages called the Access path.
27) Frequency of access: refers to how often the user is visited, to reveal how attractive the product is to the user.
28) Clicks: Refers to the number of times the user clicks the Function button on the page.

1.3. User access Mode KPI

The user accesses the way KPI mainly reflects the user to use the region, the device, the operating system version, the software version, the operating system and so on, the main statistic index includes:
29) Geographical location: the user from which province, city, autonomous region or abroad.
30) Network Service provider: The network that the user uses, is the mobile, the telecommunication, the Unicom or the WiFi.
IP segment: The IP segment where the user resides.
32) Mobile Device type: The type of mobile device the user is using.
33) Screen Resolution: the various screen resolutions of the device used by the user.
34) Operating system: the type and version of the operating system used by the user.
35) Software version: The version of the product software used by the user.

2. Key indicators

User data is the most important reference indicator, divided into the following two categories:

2.1. User and traffic growth KPIs

36) User growth percentage: that is, the percentage of UV growth (typically compared to last month or previous week).
37) Percentage increase in traffic: the percentage of PV growth (IBID.).
38) channel Promotion user ratio: Get the percentage of users from each app store or channel.
39) New user ratio: the proportion of new users to all users.

2.2. Function and content Efficiency KPI

40) Average number of pages per use: Total visits/visits. The average page access number represents the viscosity of the product, the higher the viscosity, the more pages the user sees, the higher the average page access number.
41) Average number of uses per individual user
42) Return Rate: Active users account for the proportion of all users, to reveal the user's loyalty.
43) Ratio of new users to old users
44) Number of users in different stay times (0-30 seconds, 30 seconds-2 minutes, etc.)
45) Number of users with different access depths
46) Home Bounce Rate (Main page Bounce rates): Refers to the percentage of users who have only browsed the home page to leave the product.

3. Special indicators

If the product has search or push functionality, these metrics are used:

3.1. Search efficiency KPIs

47) Percentage of users using Search
48) Average number of searches per visit
49) percent of search for "0 results"
50) Percentage of "0 hits" from the search results

3.2 Push efficiency KPIs

51) arrival rate of message pushes
52) Timely arrival rate of message push
53) Open rate of message push
54) Effective conversion rate of message push

4. Marketing Type indicators

4.1. KPI of marketing efficiency

55) Average Cost per user
56) Average revenue per user
57) Comparison of earnings between new users and old users
58) Percentage of new users ' returns to old customers

4.2. For mobile e-commerce, the shopping cart KPI

59) Average number of items per shopping cart
60) Average number of items per shopping cart
61) Average value and average cost of orders per conversion
62) The abandonment rate of the shopping cart: The percentage of abandonment during the shopping process.
63) Start shopping rate: The number of users who added the first item to the shopping cart divided by the total number of users.
64) Start Checkout rate: The number of users who clicked the Settle Account button divided by the total number of users.
65) Complete Checkout rate: Total number of users who completed the payment purchase/Click the total number of users who clicked the Settle Account button.

4.3. Conversion rate KPI

66) Active conversion rate (activity Conversions Rates): The amount of traffic/total traffic that is corresponding to the action.
67) conversion rate for registered users
68) conversion rate for active users
K-Factor: Average number of invitations per user/percentage of incoming invitations converted to new users

Head a bit dizzy, put aside the mobile e-commerce, there are 63 statistical indicators, this is just their own exploration, but also can further expand, if the timing of data analysis, is probably the category of big data analysis.

Does your product have access to this data? What do you think of the data? Have you considered these operational aspects of non-functional requirements in product design?

Research on the index analysis of mobile internet products