User behavior cultivation system and method based on BP neural network

Abstract

The invention relates to a user behavior cultivation system and a method based on a BP neural network, and the method comprises the steps: establishing a BP neural network model for evaluating a userbehavior, and carrying out the training based on a user behavior sample; sending a readable message for cultivating user behaviors to the user through a set channel, and collecting the user behaviors;and evaluating the collected user behaviors by adopting a BP neural network model, and giving an evaluation result of the user; judging the user type based on the evaluation result, resetting the corresponding channel type based on the user type, updating the set channel for sending the readable message to the user based on the reset channel type, and / or changing the frequency of sending the message based on the user type. According to the user behavior cultivation system based on the BP neural network provided by the invention, the user behavior cultivation mode is changed based on the evaluation result, and the user cultivation effect is improved. According to the user behavior cultivation subsystem, clues are automatically screened, and the clue screening efficiency and quality are improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a user behavior cultivation system and method based on BP neural network. Background technique [0002] At present, enterprises mainly obtain clues through search engines, screen media advertisements, social advertisements, etc. The quality of clues brought by these customer acquisition channels is uneven, and the conversion efficiency is low. When enterprises follow up these clues, the following problems will occur: [0003] 1. The quality of clues is unstable, quantitative data cannot be obtained, and accurate follow-up cannot be done. [0004] 2. It is easy to cause high-quality leads to be lost. During the follow-up process of leads, it is impossible to accurately distinguish between low-quality leads and high-quality leads, which will cause high-quality leads to be lost. [0005] 3. Lead screening cannot be automated, and the entire process requires manual processin...

AI Technical Summary

Problems solved by technology

[0003] 1. The quality of clues is unstable, quantitative data cannot be obtained, and accurate follow-up cannot be done

[0004] 2. It is easy to cause high-quality leads to be lost. During the follow-up process of leads, it is impossible to accurately distinguish between low-quality leads and high-quality leads, which will cause high-quality leads to be lost

[0005] 3. Lead screening cannot be automated, and the entire process requires manual processing

[0006] The current clues are not differentiated, and are directly handed over to the marketing department for further judgment

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