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Logistics customer loss prediction method and system, and medium

A technology of customer churn and prediction method, which is applied in logistics, forecasting, biological neural network models, etc., can solve problems such as difficult estimation of customer loss, and achieve the effect of high prediction speed and accuracy, and good fault tolerance performance

Active Publication Date: 2018-11-30
YTO EXPRESS CO LTD
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Problems solved by technology

Therefore, the loss caused by customer churn is difficult to estimate.

Method used

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  • Logistics customer loss prediction method and system, and medium
  • Logistics customer loss prediction method and system, and medium
  • Logistics customer loss prediction method and system, and medium

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Embodiment Construction

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the aspects described below in conjunction with the drawings and specific embodiments are only exemplary, and should not be construed as limiting the protection scope of the present invention.

[0036] figure 1 The overall process of the embodiment of the logistics customer churn prediction method of the present invention is shown, please refer to figure 1 , the following is a detailed description of the method steps of this embodiment.

[0037]Step 1: Collect big data on customer behavior preferences in the logistics industry, such as prices, products, technologies, and services. Data can also be collected from some characteristics of customers in the logistics industry, such as customers’ preferences for express delivery time, and the distribution of logistics outlets for convenience. Impact, the impact of logistics transportation...

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Abstract

The invention discloses a logistics customer loss prediction method and system, which has high fault tolerance, accuracy and timeliness, is rapid and reliable in prediction, and is suitable for predicting customer loss of dynamic data flow. According to the technical scheme, the method comprises the steps of collecting logistics industry customer behavior preference big data; extracting conditionfactors with relatively strong correlation with the customer loss from the collected logistics industry customer behavior preference big data by adopting a rough set theory, thereby forming an original decision table; performing information entropy-based discretization processing on continuous attribute values in the formed original decision table to obtain an initial decision table; reducing redundant condition attributes in the initial decision table to obtain a secondary decision table, which serves as an input of a BP neural network; and by using an incremental learning algorithm, adaptively determining the number of neurons in a hidden layer by using an ELM as a basic classifier, and verifying the accuracy of the decision table; and adjusting a weight and a threshold value of an output layer, and training a data set until an optimal solution is output.

Description

technical field [0001] The invention relates to a method, medium and system for predicting customer loss in the logistics industry, in particular to a method, medium and system for customer loss prediction using variable precision rough sets and BP (Back Propagation) neural networks. Background technique [0002] Customer churn is a nonlinear chaotic and complex system affected by multiple factors such as technology, market, customer, culture and regulation. Studies have shown that customer loyalty and switching costs are positively correlated. If customers are dissatisfied with the company's service, 8-10 people will receive the information that the company's service is lacking. On the contrary, if the customer appreciates the service or quality of a certain company, only 2-3 people will receive the message. Therefore, the loss caused by customer loss is difficult to estimate. If the company takes relevant measures to retain customers when they foresee signs of imminent t...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/08G06Q30/02G06N3/04
CPCG06Q10/04G06Q10/083G06Q30/0202G06N3/045
Inventor 英春谭书华花曼鞠晶孙知信孙哲宫婧
Owner YTO EXPRESS CO LTD
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