TWM593025U - Customer Behavior Prediction System - Google Patents

Abstract

A customer behavior prediction system includes a storage module and a processing module electrically connected to the storage module; the storage module stores a plurality of different behaviors related to a customer to be predicted in the past and each corresponds to one According to the previous behavior data of the previous time point, the processing module obtains a previous sequence including a plurality of previous parameters corresponding to the previous behavior data according to the previous behavior data, the previous parameters are sorted according to the previous time points, according to The previous sequence uses a customer behavior prediction model for predicting the customer's future behavior to obtain a prediction sequence that includes a plurality of prediction parameters indicating possible future behaviors of the customer to be predicted.

Description

Customer Behavior Prediction System

The present invention relates to a behavior prediction system, especially a behavior prediction system using machine learning.

The existing marketing methods in the banking industry are based on the experience of product managers, manually retrieving customer past consumer behavior data and selecting target customer groups, and verifying the selected target customer groups through subsequent marketing activities to determine Select the pros and cons of the target customer group, and the product manager finally improves his selection experience based on the feedback of the pros and cons of the target customer group.

However, this manual selection of target customer groups not only does not have a fixed standard, but also takes too much time and cost in training product managers, and the accuracy of the selected target customer groups is not sufficient to achieve the purpose of financial commodity marketing.

In view of this, it is imperative to propose a completely new solution to improve the accuracy of selecting target customer groups.

Therefore, the purpose of the new model is to provide a customer behavior prediction system based on machine learning to improve the accuracy of the selected target customer group.

Therefore, the new customer behavior prediction system includes a storage module and a processing module electrically connected to the storage module.

The storage module stores a plurality of pieces of previous behavior data related to a variety of different behaviors of a customer to be predicted in the past and each corresponding to a previous time point.

The processing module obtains a previous sequence including a plurality of previous parameters corresponding to the previous behavior data according to the previous behavior data, the previous parameters are sorted according to the previous time points, and according to the previous sequence, a The customer behavior prediction model for predicting the future behavior of the customer obtains a prediction sequence containing a plurality of prediction parameters indicating various possible future behaviors of the customer to be predicted.

The effect of the present invention lies in that: by using the previous sequence corresponding to the previous behavior of the customer to be predicted, the customer behavior prediction model used to predict the customer's future behavior is used to obtain the future prediction sequence, and the future prediction sequence includes Each forecast parameter indicates the possible future behavior of the customer to be predicted, and the bank can determine whether the customer to be predicted is the target customer according to the future behavior of the customer to be predicted, greatly improving the accuracy of selecting the target customer group degree.

Referring to FIG. 1, an embodiment of the new customer behavior prediction system 1 is connected to a client 2 via a communication network 200.

The customer behavior prediction system 1 includes a system-side communication module 11 connected to the communication network 200, a system-side storage module 12, and a system electrically connecting the system-side communication module 11 and the system-side storage module 12 End-processing module 13.

The system-side storage module 12 stores a plurality of pieces of training data corresponding to a plurality of customers, and each training data includes a plurality of pieces of previous training behavior data and a plurality of pieces of predicted training behavior data that the corresponding customer previously made these different behaviors , And multiple recommendable parameters corresponding to various target customer behaviors and a corresponding recommendation information. Wherein, each training prior behavior data corresponds to a previous training time point, each training prediction behavior data corresponds to a training prediction time point, and any training prediction time point of the same training data is later than these training previous time points . Among them, each recommendation information is financial commodity information, but not limited to this.

The client 2 includes a client communication module 21 connected to the communication network 200, a client display module 22, and a client process electrically connecting the client communication module 21 and the client display module 22 Module 23.

In this embodiment, the implementation of the customer behavior prediction system 1 is, for example, a personal computer, a server, or a cloud host, but not limited to this.

In this embodiment, the implementation of the client 2 is, for example, a personal computer, a smart phone, or a tablet computer, but not limited to this.

The following will describe the system-side communication module 11, the system-side storage module 12, the system-side processing module 13, and the components of the client 2 by using the new customer behavior prediction system 1 to execute a customer behavior prediction method Operation details. The customer behavior prediction method includes a model training program and a prediction recommendation program.

Referring to FIG. 2, the model training procedure includes steps 51 to 53.

In step 51, for each training data, the system-side processing module 13 obtains a training previous containing a plurality of training previous parameters corresponding to the training previous behavior data according to the training previous behavior data in the training data sequence. Wherein, the previous training parameters are sorted according to the previous training time points.

In step 52, for each training data, the system-side processing module 13 obtains a training prediction including a plurality of training prediction parameters corresponding to the training prediction behavior data according to the training prediction behavior data in the training data Sequence, the training prediction parameters are ordered according to the training prediction time points.

It is worth noting that for each training data, when the training previous behavior data and the training prediction behavior data are customer credit card consumption information, then the training previous parameters (input of training data) and these The training prediction parameters (the output of training data) are all merchant category codes (MCC CODE, Merchant Category Code, used to indicate the type of goods or services provided by the merchant); and, when such training prior behavior data and such training When the predicted behavior data are all the information browsed by the customer on the Internet, the training previous parameters (input of training data) and the training prediction parameters (output of training data) are all website codes, but not limited to the above examples . Wherein, the web address in each web browsing information can correspond to a web site code.

In step 53, the system-side processing module 13 uses a recurrent neural network based on the training data to obtain a customer behavior prediction model for predicting the customer's future behavior. Among them, the recurrent neural network is a recurrent neural network (RNN, Recurrent Neural Networks) or a long short-term memory model (LSTM, Long Short-Term Memory), but not limited to this.

Referring to FIG. 3, the prediction recommendation procedure includes steps 61 to 65.

In step 61, the system-side processing module 13 obtains a plurality of previous behavior data related to a plurality of different behaviors of a customer to be predicted in the past and each corresponding to a previous time point. The previous sequence of the previous parameters of the behavioral data. Among them, the previous parameters are sorted according to the previous time points.

In step 62, the system-side processing module 13 uses the trained customer behavior prediction model according to the previous sequence to obtain a prediction sequence including a plurality of prediction parameters indicating possible future behaviors of the customer to be predicted.

It is worth noting that when the previous behavior data is customer credit card consumption information, and the previous parameters are merchant category codes, then the prediction parameters are merchant category codes, and then the prediction parameters represent The type of store that the customer may consume in the future; and when the previous behavior data is the customer's Internet browsing information and the previous parameters are the website code, then the prediction parameters are the website code The prediction parameters mean that in the future, the customer may click on the website to browse, but not limited to the above example. Wherein, the web address in each web browsing information can correspond to a web site code.

In step 63, the system-side processing module 13 determines whether there is at least one parameter to be recommended that is the same as any of the recommendable parameters among the prediction parameters of the prediction sequence. When the system-side processing module 13 determines that there is no parameter to be recommended, the prediction recommendation process is ended; when the system-side processing module 13 determines that the at least one parameter to be recommended exists, process step 64 is performed.

In step 64, the system-side processing module 13 transmits the recommendation information corresponding to each parameter to be recommended to the client 2 through the system-side communication module 11.

In step 65, after receiving the recommendation information corresponding to each parameter to be recommended through the client communication module 21, the client processing module 23 displays the recommendation information corresponding to each parameter to be recommended to the client端Displaymodule 22.

In summary, in the new customer behavior prediction system 1, the client processing module 23 performs pre-processing on each training data obtained in advance to obtain the training corresponding to the training data and including the previous training parameters The previous sequence, and the training prediction sequence corresponding to the training data and containing the training prediction parameters, and using a recurrent neural network, using the training previous sequence as input and the training prediction sequence as the expected output to train the client Behavior prediction model; then, the client processing module 23 obtains the future prediction sequence using the trained customer behavior prediction model according to the previous sequence corresponding to the previous behavior of the customer to be predicted, and the future prediction sequence Each prediction parameter included indicates the possible future behavior of the customer to be predicted, and recommends corresponding financial products based on the future behavior of the customer to be predicted, and the bank can make a judgment based on the future behavior of the customer to be predicted Whether the customer to be predicted is a target customer, thereby greatly improving the accuracy of selecting the target customer group, so that the recommended financial products can better meet the needs of the customer to be predicted. Therefore, it can indeed achieve the purpose of new cost.

However, the above are only examples of the new model. When the scope of the new model cannot be limited by this, any simple equivalent changes and modifications made according to the patent application scope and patent specification content of the new model are still regarded as Within the scope of this new patent.

200: communication network 1: Customer behavior prediction system 11: System-side communication module 12: System-side storage module 13: System-side processing module 2: client 21: Client communication module 22: Client display module 23: Client processing module 51~53: steps 61~65: steps

Other features and functions of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: FIG. 1 is a block diagram illustrating an embodiment of the new customer behavior prediction system; FIG. 2 is a flowchart illustrating a model training procedure of a customer behavior prediction method executed by the embodiment; and FIG. 3 is a flowchart illustrating a prediction recommendation procedure of the customer behavior prediction method.

200: communication network

1: Customer behavior prediction system

11: System-side communication module

12: System-side storage module

13: System-side processing module

2: client

21: Client communication module

22: Client display module

23: Client processing module

Claims (5)

A customer behavior prediction system, including: A storage module that stores multiple pieces of previous behavior data related to a variety of different behaviors of a customer to be predicted in the past and each corresponding to a previous time point; and A processing module, electrically connected to the storage module, and according to the previous behavior data, obtain a previous sequence including a plurality of previous parameters corresponding to the previous behavior data, the previous parameters are sorted according to the previous time points, according to The previous sequence uses a customer behavior prediction model for predicting the customer's future behavior to obtain a prediction sequence that includes a plurality of prediction parameters indicating possible future behaviors of the customer to be predicted. The customer behavior prediction system according to claim 1, further comprising a communication module connected to a client via a communication network and electrically connected to the processing module, the storage module further stores a plurality of corresponding customer behaviors The recommendable parameter and its corresponding recommendation information, the processing module determines whether there is at least one parameter to be recommended that is the same as any of the recommendable parameters among the predictive parameters of the prediction sequence, when the processing When the module determines that the at least one parameter to be recommended exists, the processing module transmits the recommendation information corresponding to each parameter to be recommended to the client through the communication module. The customer behavior prediction system according to claim 1, wherein the storage module further stores a plurality of pieces of training data corresponding to a plurality of customers, and each training data includes the number of times the corresponding customer has previously performed these different behaviors Pen training previous behavior data and multiple training prediction behavior data, each training previous behavior data corresponds to a previous training time point, each training prediction behavior data corresponds to a training prediction time point, any training prediction of the same training data The time points are later than the previous training time points. For each training data, the processing module obtains a training previous containing multiple corresponding training previous behavior data according to the training previous behavior data in the training data The training previous sequence of the parameters, the training previous parameters are sorted according to the training previous time points, and for each training data, the processing module obtains a plurality of correspondences according to the training prediction behavior data in the training data The training prediction sequence of training prediction parameters of the training prediction behavior data, the training prediction parameters are sorted according to the training prediction time points, the processing module obtains the customer behavior prediction model based on the training data, using a recurrent neural network . The customer behavior prediction system according to claim 1, wherein the previous behavior data is customer credit card consumption information, the previous parameters are merchant category codes, and the prediction parameters are merchant category codes. The customer behavior prediction system according to claim 1, wherein the previous behavior data is customer web browsing information, the previous parameters are website codes, and the prediction parameters are website codes.

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