User portraits can be simply understood as labels for massive amounts of user data. According to the differences in different user behaviors and attributes, they are divided into different types, and then typical features are extracted from each type, giving descriptions such as region, purchasing power, occupation, and gender.
User portraits were first used in the field of e-commerce. In the era of big data, user information is flooding the Internet. Each specific information of the user is abstracted into a label, and these labels are used to concretize the user image, so as to provide users with targeted services.
In the field of Amazon’s cross-border e-commerce, the platform has provided relevant data visualization functions for user portraits. For example, for brand sellers, Amazon has opened up the brand analysis function and provided the demographics (Demographics) function. Through this module, you can see the age, marital status, family income, education level and gender information of buyers who have made purchases in the store within a period of time. By analyzing the characteristics of the population, more accurate strategies can be formulated for later advertising and product selection.