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This article will explain in detail the example analysis of the data collection mechanism and strategy under WeChat Mini Programs's business scenario. The editor finds it very practical, so I share it with you for reference. I hope you can get something after reading this article.

I. scene description

To make a product for C-end users, it is very dependent on the collection of user data. Below, we have seen such a data analysis chart, which analyzes and compares the transaction volume of the exposed products through the data collection of each link:

Through the browsing of goods-click-transaction page-payment purchase, etc., analyze the transaction scenario of the product, here is to observe the data link from the large business, in fact, a lot of details need to be considered in the analysis.

II. Data sources

User data to measure the latitude of users or products is the most persuasive, so in the late product development and optimization process of the Internet, data collection and management has always been a very important operation.

Now the common client end of the product is the entrance of PC, H5, app, Mini Program and other scenarios, as well as some Internet of things devices or special data collection mechanism, the data types in different scenarios should be distinguished. Through all kinds of data burying points under different ports, obtain the data of different events in each scene to analyze the advantages and disadvantages of the product, and obtain constructive analysis results.

For example, the case in module 1: through the analysis of the port, if the recommendation and transaction rate of commodity An on the APP side is the highest, and the recommendation effect on the Mini Program side is not good, then we can consider using different recommendation mechanisms for APP and Mini Program.

III. Classification of event types

Data needs to be collected, and to distinguish the data from different ports is only the basic level of consciousness, thinking about the event types of data collection is the most basic operation. Here, we should consider the characteristics of the product and generalize the differences. The following provides some basic collection data and some common cases, about the core business data are relatively fine and complete, basically have the conditions to read the database for direct analysis.

Basic information

Attribute field type description terminal app_clientStringAndroid/IOS/ Mini Program / H5 and other terminal versions app_versionString version number identification user ID user_idInteger user ID network address ip_addressString user IP information

This information exists in any acquisition point data, through these basic information collection, used to analyze the characteristics of users under different ports, so that differentiated management and operation can be carried out.

Login information

Property field type describes the login time login_timeDate user login time online length of time online_timeLong online use of the system

Through the login and online time, as well as some usage information, determine whether this kind of user activity, whether the need for key operations or marketing activation.

Business foundation

Attribute field type describes service type service_idInteger different business service modules divide model_typeInteger such as order / payment / logistics, etc.

Use this as the basic information of business data collection, which is used to divide and analyze the business data as a whole, and the specific details need to be designed according to the specific scene.

Commodity case

Attribute field type describes commodity information product_idInteger commodity information display location position_idInteger for example: list / recommendation / advertising space shop information shop_idInteger shop information search information key_wordString search keywords current unit price current unit price current sales volume sales_numLong goods current sales volume

Here is a simple data collection information according to the user's browsing behavior. This mechanism is very common in the actual e-commerce APP, and the products that generate clicks or searches will be recommended. If there is no such action, the recommendation mechanism will be made according to the daily browsing information. In the actual development, the data collected is far more complex than here, which needs to be considered according to the actual business needs.

Marketing case

Property field type description activity location location_idInteger entry bit / guide page / recommendation bit / sharing link and other marketing products product_idLong marketing campaign main product type product detail flow detail_numLong activity product pageview statistics order confirmation page detail_numLong activity pageview statistics transaction statistics trade_numLong activity final conversion statistics

Conduct product marketing through operational activities, review and statistics the data after the end of the activity, and then balance the value and cost generated by marketing according to the analysis of activity trajectory data, constantly adjust activity strategies and optimize operation ideas.

IV. Mode of implementation

1. Business level

From a business point of view, in addition to some collection operations that users do not perceive, they can also be based on questionnaires. For example, many APP will pop up similar scoring systems or comments and comments after using them for a period of time to collect user feedback more directly.

2. Technical level

The most common is the SDK embedding technology, which captures, handles and sends to the server related technologies and implementation processes for specific user behaviors or events. It is very common to deal with some non-core business in this way. If it is some core business, you may need to collect data in a custom way to avoid the problem of data leakage.

3. Data accumulation

With the continuous development of business, the scenarios that need to be analyzed will become more and more complex, and when the amount of data collected reaches a certain scale, the difficulty of data management and analysis will become greater, and professional processes and intelligent tools will be needed, such as BI tools, visualization components, large data screens, joint analysis of multiple scenarios, and so on.

This is the end of the article on "example Analysis of data Collection Mechanism and Strategy in WeChat Mini Programs's Business scenario". I hope the above content can be helpful to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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