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How to refine user tags
You can simplify the refinement of labels through static labels, dynamic labels and prediction labels. Static labels judge users' basic needs, dynamic labels enhance users' experience, and predictive labels enhance users' transformation and product value.

The first static label: data voluntarily provided by users: refers to the basic information of users, mostly fixed data of users, such as name, gender, age, height, weight, occupation, region, equipment information, source channel, etc.

The second kind of dynamic tag: platform-related data: refers to the unique tag of users in the platform, which means that the platform labels users according to their behaviors for easy management. User behavior refers to all the operating behaviors of the user in the APP from the start of the APP to the close of the APP, such as the user's clicking, browsing behavior, interaction (comment, like, forward, favorite) behavior, etc.

The third prediction tag: platform-related data refers to predicting the future behavior or preferences of users according to their behavior data in the platform; It is the key to designing thousands of people and operating strategies.

user portrait

User portraits, also known as user roles, have been widely used in various fields as an effective tool for delineating target users and connecting user needs and design directions. In the actual operation process, we often use the simplest and most close-to-life words to link the user's attributes, behaviors and expected data transformation.

As the virtual representatives of actual users, the user roles formed by user portraits are not established outside the products and markets, and the formed user roles need to be representative to represent the main audiences and target groups of products.

Refer to the above? Baidu Encyclopedia-User Portrait