Lost member customer group data prediction method and model training method and device

Abstract

The invention discloses a lost member customer group data prediction method and device and a model training method and device, and belongs to the technical field of machine learning and artificial intelligence. The method comprises the following steps: counting and analyzing member loss data to obtain predefined member loss sample data; performing feature construction operation including data preprocessing and variable screening on the member loss sample data to obtain feature variable data; and inputting the feature variable data into a preset machine learning classification model for training to obtain a trained machine learning classification model, with the machine learning classification model being used for predicting a lost member customer group. By combining big data mining, feature data construction and machine learning, intelligent prediction of lost user data is achieved, the prediction precision is high, the learning and generalization capacity is high, and powerful data support is provided for user loss early warning.

Description

technical field [0001] The invention relates to the technical fields of machine learning and artificial intelligence, in particular to a data prediction method, model training method and device for lost member customer groups. Background technique [0002] In the era of rapid Internet development, compared with the traditional physical store marketing model, the advantages of online platform marketing are becoming more and more obvious. Membership marketing is an important part of online strategic marketing. The purpose is to retain users, maintain systematic, continuous, and periodic communication with users, and at the same time open up information channels between buyers and sellers to carry out comprehensive marketing activities for all. However, the fierce competition on the online platform makes the majority of users face more comparisons and choices. Therefore, in the case of limited resources, improving the quality of products and services while expanding the market,...

AI Technical Summary

Problems solved by technology

[0003] In the past, member loss predictions were mostly based on analysis and comparison of relevant information in the industry. However, with the development of the Internet, traditional statistical methods lack detailed and standardized processing when faced with massive data, resulting in low prediction accuracy and cannot meet Learning, Generalization Capabilities, and Intelligence Requirements

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