Table of contents
3. Design the buried point plan
1. Clarify the goal of buried point
2. Confirm the reported variable
4. Construction method: OSM model, UJM model, AARRR model
1. Data buried point
Data buried points can be divided into two categories, one is page statistics , and the other is behavior statistics.
Page statistics can help us know how many times a certain page has been visited by how many people. Behavior statistics refer to the user's operation behavior on the interface, and the most widely used is the number of button clicks.
There is also a user attribute burying point, which mainly records user-related information.
Burying points is to collect user behavior on key pages and paths for statistical analysis. The front desk is written in advance in the code.
Buried point refers to adding the program code of data collection to the function program code at the "operating node" where data needs to be collected;
Operation node: Generally, important events are triggered: button click, page opening, page sharing, etc.
The way of burial:
The first type: R&D personnel of their own company inject code into the product for statistics, usually there are client-side buried points and background buried points.
The second type: third-party statistical tools, such as Youmeng, Sensors, Talkingdata, GrowingIO, etc.
2. The role of data embedding
The point of burying is to continuously track the product in an all-round way , and continuously guide and optimize the product through data analysis. The quality of data embedding directly affects the quality of data, products, operations, etc.
The C-side burying point mainly focuses on the user's behavior path, continuously optimizes to find problems, improves retention, and conversion;
The B-side focuses on the usage of functions, and judges the usage scenarios of a certain function in a certain period of time. If the number of usage gradually decreases, it can be considered to kill him.
3. Design the buried point plan
A brief summary is:
1. Clarify the target of buried point 2. Confirm the reporting variable 3. Clarify the timing of reporting 4. Clarify the priority.
1. Clarify the goal of buried point
The requirement of each buried point is to solve the needs of a certain business, so when writing the buried point plan, it is necessary to clarify what specific data this plan is for reporting. This data is to solve the specific needs of the business side, and what business benefits can be brought by the reporting of these data.
2. Confirm the reported variable
The reporting variable is mainly composed of events and other parameter variables, among which events are required to identify this specific operation. Event reporting is mainly used to capture the user's behavior, to know which button the user has operated, which page has been visited, which page has been slid, etc.
3. Clear reporting timing
The trigger timing of the event is often an important factor affecting the accuracy of the data. Taking the user's click behavior as an example, if we want to count the user's like behavior, whether we report when the user clicks the like button or after the user successfully likes the button, the result is biased.
4. Clarify priorities
Under normal circumstances, we will propose multiple buried point requirements at the same time. At this time, we need to plan the priority of these buried point requirements. After submitting to developers, they can adjust according to priority. The priority of buried points is mainly determined according to the importance and urgency of the reported data. For example, the requirements of the version function are buried, and the priority of the buried point is the statistics of the basic indicators, and the details of the user's operation behavior are slightly lower. Because every time a new function is released, the business side first pays attention to the basic indicators, such as how many people use this function, the frequency of use, etc. We need to ensure that the basic data indicators are complete, followed by the data required for in-depth analysis .
4. Construction method: OSM model, UJM model, AARRR model
A brief summary is:
1. Analyze requirements, 2. Split indicators according to requirements, 3. Determine embedding points according to indicators, 4. Design data requirements documents; 5. Develop indicators.
The following are different steps, and the steps of different companies are not necessarily the same. For example, the product, operation, data analyst, and development and testing teams have different divisions of labor.
The establishment of a data indicator system is the first step in data-driven development.
1. OSM model
The OSM model can match business goals with corresponding measurement and evaluation indicators.
When formulating business goals, in addition to reaching agreement with the business side, it is also necessary to ensure that the business goals conform to the following four principles, namely the DUMB principle : (1) Doable (2) Understandable (Understandable) (3) Intervention , Manageable (4) positive and beneficial (Beneficial).
The data index framework of the OSM model can connect the goal with the final evaluation data system , so that the setting of each index knows which strategy effect of a specific business is to be evaluated, and how the strategic effect of each business is the overall goal Services.
2. UJM model
The UJM model is to sort out the user's behavior journey map, because the data indicator system is directly related to the user's behavior, and only after a complete and scientific sorting of the user's entire behavior journey can the corresponding indicators be designed in each link.
UJM (User Journey Map, User Journey Map) refers to the user's operation path in the APP. Taking the e-commerce platform as an example, a user purchases a complete UJM of a product.
Let’s take cross-border e-commerce stand-alone as an example. First, sort out the user’s journey: understand the product → shop on the platform → favorite product → register and log in → pay to place an order → repurchase. Then analyze the user behavior of each journey node according to the user's journey, what is the business goal, what are the contact points with the user, and what problems are there in the journey, and find opportunities for these problems, and finally seek opportunities based on the opportunities Appropriate metrics. The end result is shown in the image above.
3. AARRR model
The AARRR model mainly describes an APP from five aspects, including user acquisition, user activity, user retention, user monetization, and user recommendation. Users of any APP will go through these 5 stages, and these 5 stages are also the focus of the business side.
User acquisition: Refers to the use of external advertising to acquire users through social forwarding fission, user recommendation, and big V forwarding. User acquisition is the initial step when a user arrives at an app and starts using it. Taking Pinduoduo as an example, user acquisition means that Pinduoduo publishes many advertisements or activities to let users download the app.
Active user: When a new user is obtained, the user must be active in the APP.
User retention: retention means that users can continue to stay in our APP. After the user is active on the APP, the next step is to enable the user to continue to be active, which is the definition of retention.
User Monetization: Refers to using users to generate revenue. When users have a certain degree of stickiness to the APP, our ultimate goal is to increase revenue while maintaining user growth, such as purchasing goods in e-commerce apps.
User recommendation: users forward and spread APP. The user's forwarding behavior indicates that the user has a high recognition of the APP, and the forwarding behavior can bring more new user growth.
Reference content:
Planning data index system method
Data burying point - what is burying point? - Short book
"Data Analysis Method and Business Practice"
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