Voice dialing user classification method based on data analysis and related equipment

Abstract

The invention relates to the technical field of data analysis, in particular to a voice call user classification method based on data analysis and related equipment, and the method comprises the steps: obtaining original information of all corresponding target users after obtaining service classification demands, and generating an original information data set after summarizing the original information; performing data cleaning and summarizing on user original information in the original information data set to obtain a user information data set, and aligning and classifying user classification results; extracting a corresponding question from a preset question library according to the user classification result and the service classification demand, calling the corresponding user and obtaining a response result, analyzing the response result to generate a to-be-maintained record, and sending the to-be-maintained record to the original information source party; and updating the user information data set after receiving the updated data of the source party, and obtaining a corrected classification result accordingly. According to the method, the effects of automatically classifyingthe to-be-called users, automatically calling and automatically correcting the classification result are achieved, the manpower and time cost is saved, and the classification precision and accuracy are high.

Description

technical field [0001] The invention relates to the technical field of intelligent recommendation, in particular to a method for classifying voice dialing users based on data analysis and related equipment. Background technique [0002] The traditional customer service business is carried out by manual calling. Before making a call, the target user is generally pre-classified according to the business classification requirements corresponding to the service items by manual means. For example, when the business classification requirements are certain types of financial wealth management insurance products or life insurance products, the classification strategy can be formulated according to age Divide users of different ages into several age groups after different classification conditions, and then target users in several age groups to push products, or divide users into male and female according to gender and then send them to one of the categories Users push specific prod...

AI Technical Summary

Problems solved by technology

[0004] 1) Large investment in manpower and time;

[0005] 2) The manual processing method has a high error rate and insufficient classification accuracy;

[0006] 3) Due to the quality and skill limitations of the staff involved in the classification, the classification results may be biased, resulting in a reduction in the accuracy of the classification. For example, different staff may eventually present different results when they classify the same group of target users. result;

[0007] 4) When making a call, it is still subject to the limitations of manual operation, such as fatigue, emotional control, professional skills and other factors, which lead to different attitudes and processing results for the target user, so that the user feedback presents certain In addition, when the manual operation is highly fatigued or the dialing task is heavy, the real-time and accuracy of the dialing information record cannot be guaranteed, and it is easy to miss the record, resulting in incomplete information recording and content distortion. of customer maintenance work brings hidden dangers

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