Telecom customer loss probability prediction method and system based on end-to-end model

A technology for customer loss and probability prediction, applied in character and pattern recognition, other database retrieval, marketing, etc., can solve problems such as difficult to accurately identify customers, large differences, and difficult analysis

Pending Publication Date: 2020-08-14
ZHEJIANG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

To achieve this, companies need to have a clear judgment and understanding of customer behavior. In reality, each user has their own behavior habits, and the differences are large. It is difficult to analyze and it is di

Method used

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  • Telecom customer loss probability prediction method and system based on end-to-end model
  • Telecom customer loss probability prediction method and system based on end-to-end model
  • Telecom customer loss probability prediction method and system based on end-to-end model

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Embodiment 1

[0086] Such as figure 1 As shown, it is only one of the embodiments of the present invention. The present invention provides a method for predicting the loss probability of telecom customers based on the end-to-end model. The method includes the following steps:

[0087] S1: Obtain customer data in the telecommunications industry and mark the acquired data;

[0088] Obtain customer data in the telecommunications industry legally and compliantly through the data collection module, and mark the acquired data, mark customers as churn or non-churn according to requirements, and divide customers into new users and old users according to network access time.

[0089] S2: Preprocess the data, handle outliers and missing values, standardize the data, and train the customer probability prediction sub-model;

[0090] Preprocess the data, deal with outliers and missing values, convert string data into floating-point type for convenient operation, standardize the data, divide the trainin...

Embodiment 2

[0097] Such as Figures 2 to 10 as well as Figure 12 As shown, it is only one of the embodiments of the present invention. The present invention is a method for predicting the loss probability of telecom customers based on the end-to-end model. The steps of the method specifically have the following designs:

[0098] First, refer to Figure 4 , step S1 specifically includes:

[0099] S11: Collect customer data located on the server to the local system through Hive, and store in buckets and blocks according to the customer's ref_id hash;

[0100]Here, the customer data located on the server is collected to the local system through Hive, and stored in buckets and blocks according to the customer's ref_id hash; the collected data categories include monthly data, daily data and static data. Monthly data includes: unique ID of the number, call charges for this month, account balance, arpu value, voice usage, traffic usage, days without voice in the last 30 days, days without tr...

Embodiment 3

[0140] Such as Figure 11 , 12 As shown, the present invention also provides an end-to-end model-based churn probability prediction system for telecom customers, the system comprising:

[0141] data collection module;

[0142] Submodel training module;

[0143] Model fusion module;

[0144] Customer Churn Prediction Module;

[0145] The data collection module acquires customer data in the telecommunications industry, and marks the acquired data. The sub-model training module preprocesses the data, handles outliers and missing values, standardizes the data, trains the customer probability prediction sub-model, and model fusion module The results of the sub-models are fused, and the fusion model is trained to obtain the final customer churn probability prediction model, and the customer churn prediction module obtains the probability value of customer churn.

[0146] The data collection module includes a data collection unit, a data calibration unit and a new and old custom...

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Abstract

The invention provides a telecom customer loss probability prediction method and system based on an end-to-end model, relates to the technical field of data security, and the telecom customer loss probability prediction method based on the end-to-end model comprises the following steps: S1, obtaining telecom customer data, and marking the obtained data; S2, preprocessing the data, processing abnormal values and missing values, standardizing the data, and training a customer probability prediction sub-model; S3, fusing results of the sub-models, training a fusion model, and obtaining a customerloss probability prediction model; and S4, obtaining a customer loss probability value. According to the telecom customer loss probability prediction method and system based on the end-to-end model,big data and artificial intelligence technologies are applied to predict the customer loss probability of the telecom industry; and the method based on ensemble learning is applied to probability prediction, has good prediction accuracy, reduces the cost and time of manual judgment, and provides important reference data for enterprise maintenance customers.

Description

technical field [0001] The present invention relates to the technical field of data prediction, [0002] In particular, the present invention relates to a method and system for predicting the churn probability of telecom customers based on an end-to-end model. Background technique [0003] In recent years, with the popularization of mobile phones and the continuous development of the telecommunications industry, the entire industry has gradually become saturated. In this environment, it is becoming more and more difficult to add new customers. Therefore, in addition to launching more attractive services, it is more important for major telecommunications companies to promptly discover users who are about to lose from existing users. , that is, to ensure the stability of existing customers. To achieve this, companies need to have a clear judgment and understanding of customer behavior. In reality, each user has their own behavior habits, and the differences are large. It is ...

Claims

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Application Information

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IPC IPC(8): G06F16/903G06K9/62G06Q30/02G06Q50/32
CPCG06Q30/0202G06F16/90335G06Q50/32G06F18/214
Inventor 刘勇赵锡成马新强周雪杨建党钟保全张可鑫沈立凯张少华杨曦徐晋鸿
Owner ZHEJIANG UNIV
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