Dynamic marketing classification method and device, electronic equipment and storage medium

Abstract

The invention discloses a dynamic marketing classification method and device, electronic equipment and a storage medium in the technical field of computers, and the method comprises the steps: obtaining the dynamic personal information of a customer, and carrying out the attribution classification of the customer according to the personal information, and obtaining customer group information. According to the invention, stage division is carried out on customer group information with the same attribution type according to the obtained group feature information to obtain corresponding group stage information, value layering is carried out on the group stage information to obtain customer value information, and dynamic personal information and the customer value information jointly obtain dynamically updated customer portrait information. Financial services and marketing activities are accurately recommended to customers according to customer portrait information, multi-dimensional division is carried out on the affiliation dimension, the stage dimension, the value dimension and the customer portrait dimension required by the customers, customer division is more accurate, dynamic adjustment and updating are adapted in the stage dimension and the value dimension, and the customer experience is improved. And marketing resources can be effectively distributed.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and specifically to a dynamic marketing classification method, device, electronic device, and storage medium. Background Art

[0002] The customer segmentation theory was first proposed by American professor Wendell Smith in the 1950s. The core idea is that customers' demands for products and services show differences. For precise marketing by banks, they should segment customers according to their own characteristics and development directions, and provide different marketing strategies for different customers to enhance the customers' experience and the bank's profitability. Currently, the marketing customer groups formed by banks based on traditional single rules or rule groups mostly adopt basic stratification methods such as total assets and card categories. The analysis is simple and the business interpretability is strong, but it is not suitable for the increasingly changeable marketing customer groups, unable to accurately locate customers, which is not conducive to subsequent precise push of marketing strategies, and the use effect is not good. Summary of the Invention

[0003] The purpose of the present invention is to provide a dynamic marketing classification method, device, electronic device, and storage medium to solve the problems of poor applicability to the increasingly changeable marketing customer groups, inability to accurately locate customers, and being not conducive to subsequent precise push of marketing strategies as mentioned above.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] In the first aspect, the present invention provides a dynamic marketing classification method, including:

[0006] Obtain the dynamic personal information of customers, and classify the customers according to the personal information to obtain customer group information;

[0007] Obtain group characteristic information according to the personal information, and divide the customer group information into stages according to the group characteristic information to obtain group stage information;

[0008] Perform value stratification on the corresponding stages in the group stage information to obtain customer value information;

[0009] Analyze and obtain dynamically updated customer portrait information according to the customer value information and personal information;

[0010] Obtain the financial services and marketing activities recommended to customers according to the customer portrait information.

[0011] As a further solution of the present invention: The obtaining of the dynamic personal information of customers and classifying the customers according to the personal information to obtain customer group information includes;

[0012] The personal information includes customer information and customer personal transaction data, wherein the customer information includes basic information, behavior information and asset information, and the customer personal transaction data includes deposit data, loan data and insurance data, wherein the customer information and customer personal transaction data are updated at the system preset time;

[0013] The customer group information includes main deposit customers, main loan customers and main insurance customers;

[0014] If the customer's deposit data is greater than or equal to the preset deposit threshold, and the deposit data is greater than the loan data and insurance data, then the customer is classified as a primary deposit customer;

[0015] If the customer's loan data is greater than or equal to the preset loan threshold, and the loan data is greater than the deposit data and insurance data, the customer is classified as a primary loan customer;

[0016] If the customer's insurance data is greater than or equal to the preset insurance threshold, and the insurance data is greater than the deposit data and loan data, the customer is classified as a primary insurance customer.

[0017] As a further solution of the present invention: the group characteristic information is obtained according to the personal information, and the customer group information is divided into stages according to the group characteristic information to obtain group stage information;

[0018] The group stage information includes the introduction stage, growth stage, maturity stage and decline stage corresponding to the customer group information;

[0019] The basic information, behavior information and asset information in the customer information are clustered to obtain data clustering information, and the customer group information is divided into the introduction stage, growth stage, maturity stage and decline stage corresponding to the customer group information according to the data clustering information.

[0020] As a further solution of the present invention: the data clustering information includes the customer's activation status, account opening time and number of transactions, wherein the activation status includes unactivated, activated and completely closed account;

[0021] If the activation status of the primary deposit customer is not activated, it is divided into the deposit introduction stage; if the activation status of the primary deposit customer is activated, and the account opening time of the primary deposit customer is lower than the preset account opening time, or the number of transactions of the primary deposit customer is lower than the preset number of transactions, it is divided into the deposit introduction stage;

[0022] If the number of transactions of the primary deposit customer is higher than the preset number of transactions and lower than the preset number of mature transactions, it is classified into the deposit growth stage;

[0023] If the number of transactions of the primary deposit customer is higher than the preset number of mature transactions, it is classified as a deposit mature stage;

[0024] If the account opening time of the main deposit customer is greater than the preset recession time and the number of transactions is zero, it is classified into the deposit recession stage. If the enabled status of the main deposit customer is fully closed, it is classified into the deposit recession stage;

[0025] If the enabled status of the main loan customer is not enabled, it is classified into the loan introduction stage. If the enabled status of the main loan customer is enabled, and the account opening time of the main loan customer is lower than the preset account opening time, or the number of transactions of the main loan customer is lower than the preset number of transactions, it is classified into the loan introduction stage;

[0026] If the number of transactions of the main loan customer is higher than the preset number of transactions and lower than the preset mature number of transactions, it is classified into the loan growth stage;

[0027] If the number of transactions of the main loan customer is higher than the preset mature number of transactions, it is classified into the loan mature stage;

[0028] If the account opening time of the main loan customer is greater than the preset recession time and the number of transactions is zero, it is classified into the loan recession stage. If the enabled status of the main loan customer is fully closed, it is classified into the loan recession stage;

[0029] If the enabled status of the main insurance customer is not enabled, it is classified into the insurance introduction stage. If the enabled status of the main insurance customer is enabled, and the account opening time of the main insurance customer is lower than the preset account opening time, or the number of transactions of the main insurance customer is lower than the preset number of transactions, it is classified into the insurance introduction stage;

[0030] If the number of transactions of the main insurance customer is higher than the preset number of transactions and lower than the preset mature number of transactions, it is classified into the insurance growth stage;

[0031] If the number of transactions of the main insurance customer is higher than the preset mature number of transactions, it is classified into the insurance mature stage;

[0032] If the account opening time of the main insurance customer is greater than the preset recession time and the number of transactions is zero, it is classified into the insurance recession stage. If the enabled status of the main insurance customer is fully closed, it is classified into the insurance recession stage.

[0033] As a further solution of the present invention: obtaining customer value information by value stratification of the corresponding stage in the group stage information, including;

[0034] The corresponding stages in the group stage information include the deposit introduction stage, deposit growth stage, deposit maturity stage, and deposit decline stage of the primary deposit customers, the loan introduction stage, loan growth stage, loan maturity stage, and loan decline stage of the primary loan customers, and the insurance introduction stage, insurance growth stage, insurance maturity stage, and insurance decline stage of the primary insurance customers;

[0035] The corresponding stages in the group stage information are stratified by value through the RFM model to obtain customer value information, which includes high-value customers, medium-value customers, and low-value customers;

[0036] In the RFM model, R is the time of the last consumption, F is the consumption frequency, and M is the consumption amount;

[0037] Obtain R, F, and M of the corresponding stages in the group stage information according to the customer's personal transaction data, and perform weighted summation on R, F, and M of the corresponding stages to obtain value data;

[0038] If the value data is greater than or equal to the high-value threshold, it is judged as a high-value customer;

[0039] If the value data is less than or equal to the low-value threshold, it is judged as a low-value customer;

[0040] If the value data is greater than the low-value threshold and less than the high-value threshold, it is judged as a medium-value customer;

[0041] Judge the corresponding stages in the group stage information as the corresponding high-value customers, medium-value customers, or low-value customers according to the value data;

[0042] That is, the deposit introduction stage, deposit growth stage, deposit maturity stage, and deposit decline stage of the primary deposit customers are respectively judged as the corresponding high-value customers, medium-value customers, or low-value customers, the loan introduction stage, loan growth stage, loan maturity stage, and loan decline stage of the primary loan customers are respectively judged as the corresponding high-value customers, medium-value customers, or low-value customers, and the insurance introduction stage, insurance growth stage, insurance maturity stage, and insurance decline stage of the primary insurance customers are respectively judged as the corresponding high-value customers, medium-value customers, or low-value customers.

[0043] As a further solution of the present invention: the dynamically updated customer portrait information is analyzed based on the customer value information and personal information, including;

[0044] The customer information in the personal information further includes consumption preference information, interest preference information, and abnormal risk information;

[0045] Dynamically update the customer value information and customer information, and analyze the dynamically updated customer value information and customer information to obtain dynamically updated customer portrait information.

[0046] As a further solution of the present invention: obtaining the financial services and marketing activities recommended to customers based on the customer portrait information includes;

[0047] Input the customer portrait information into a preset recommendation engine, and the recommendation engine outputs the financial services and marketing activities recommended to customers, and push the financial services and marketing activities to the corresponding customers;

[0048] The financial services include financial products and data services.

[0049] In a second aspect, a dynamic marketing classification device is provided, and the device includes;

[0050] An acquisition module, which acquires the personal information of customers and classifies the customers according to the personal information to obtain customer group information;

[0051] A group module, which obtains group characteristic information according to the personal information, and divides the customer group information according to the group characteristic information to obtain group stage information;

[0052] A value module, which hierarchically divides the corresponding stages in the group stage information to obtain customer value information;

[0053] A dynamic portrait module, which analyzes the dynamically updated customer portrait information according to the customer value information and personal information;

[0054] An output module, which obtains the financial services and marketing activities recommended to customers according to the customer portrait information.

[0055] In a third aspect, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the dynamic marketing classification method as described above.

[0056] In a fourth aspect, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the dynamic marketing classification method as described above is implemented.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] 1. In the present invention, by dividing the customer group information with the same attribution type according to the obtained group characteristic information to obtain the corresponding group stage information, and after stratifying its value, customer value information is obtained. The dynamic personal information and the customer value information together obtain the dynamically updated customer portrait information. According to the customer portrait information, financial services and marketing activities are accurately recommended to customers. The customer portrait dimensions of the attribution dimension, stage dimension, value dimension, and customer needs of the customers are divided multi-dimensionally, making the customer division more accurate and more suitable for subsequent accurate financial services and marketing activities. Moreover, it can be dynamically adjusted and updated in the stage dimension and value dimension, which can effectively allocate marketing resources and has good use effects.

[0059] 2. In the present invention, by inputting the customer portrait information into a preset recommendation engine, the recommendation engine calculates, and combines the recommendation engine with the customer portrait information to obtain matching cross-marketing products, precision marketing data services, and marketing rights and interests activities, which are adapted to the current increasingly complex, diversified, and automated precision marketing model. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a schematic flowchart of the present invention;

[0061] Figure 2 is a schematic diagram of the module connection structure of the present invention.

[0062] In the figure: 1. Acquisition module; 2. Group module; 3. Value module; 4. Dynamic portrait module; 5. Output module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Embodiment:

[0065] Please refer to Figure 1 , in the embodiment of the present invention, a dynamic marketing classification method includes:

[0066] S1: Obtain the dynamic personal information of the customer, and classify the customer according to the personal information to obtain the customer group information;

[0067] S2: Obtain the group characteristic information according to the personal information, and divide the customer group information according to the group characteristic information to obtain the group stage information;

[0068] S3: Stratify the corresponding stages in the group stage information to obtain customer value information;

[0069] S4: Analyze based on the customer value information and personal information to obtain dynamically updated customer portrait information;

[0070] S5: Obtain the financial services and marketing activities recommended to the customers according to the customer portrait information.

[0071] Specifically, after obtaining the personal information of the customers, the present invention conducts attribution classification to obtain customer group information with the same attribution type, then divides the customer group information with the same attribution type according to the obtained group characteristic information to obtain the corresponding group stage information, and after stratifying its value, obtains customer value information. The dynamic personal information and customer value information together obtain the dynamically updated customer portrait information, and accurately recommend financial services and marketing activities to the customers according to the customer portrait information. This method conducts multi-dimensional division of the customer portrait dimensions of the attribution dimension, stage dimension, value dimension and customer needs of the customers, divides the customers more accurately, is more suitable for subsequent accurate financial services and marketing activities, and adapts to dynamic adjustment and update in the stage dimension and value dimension, can effectively allocate marketing resources, has good use effect, and is applicable to the increasingly complex, diversified and automated accurate marketing models.

[0072] As a further solution of the present invention: Obtain the dynamic personal information of the customers, and conduct attribution classification on the customers according to the personal information to obtain customer group information, including;

[0073] The personal information includes customer information and customer personal transaction data, where the customer information includes basic information, behavior information and asset information, and the customer personal transaction data includes deposit data, loan data and insurance data, and the customer information and customer personal transaction data are updated at the system preset time;

[0074] The customer group information includes primary deposit customers, primary loan customers and primary insurance customers;

[0075] If the deposit data of the customer is greater than or equal to the preset deposit threshold, and the deposit data is greater than the loan data and insurance data, then the attribution classification is the primary deposit customer;

[0076] If the loan data of the customer is greater than or equal to the preset loan threshold, and the loan data is greater than the deposit data and insurance data, then the attribution classification is the primary loan customer;

[0077] If the insurance data of the customer is greater than or equal to the preset insurance threshold, and the insurance data is greater than the deposit data and loan data, then the attribution classification is the primary insurance customer.

[0078] Specifically, different from the current simple classification based on the amount of customer assets, after obtaining personal information, data clustering is performed on the deposit data, loan data, and insurance data in the customer's personal transaction data, and the corresponding deposit data, loan data, and insurance data are compared with each other and compared with the preset deposit threshold, preset loan threshold, and preset insurance threshold. The customers are classified and attributed into main deposit customers, main loan customers, and main insurance customers. The main deposit customers, main loan customers, and main insurance customers after attribution classification are convenient for subsequent stage division, accurately distinguish customers, and facilitate the formulation of corresponding financial services and marketing activities in the future.

[0079] As a further solution of the present invention: group characteristic information is obtained according to personal information, and the customer group information is divided into stages according to the group characteristic information to obtain group stage information;

[0080] The group stage information includes the corresponding introduction stage, growth stage, maturity stage, and decline stage in the customer group information;

[0081] The basic information, behavior information, and asset information in the customer information are clustered to obtain data clustering information, and the customer group information is divided into stages according to the data clustering information into the corresponding introduction stage, growth stage, maturity stage, and decline stage in the customer group information.

[0082] Specifically, the customer life cycle theory refers to the whole process from the establishment of a business relationship between an enterprise and a customer to the complete termination of the relationship. It is the development track of the customer relationship level over time, and it dynamically describes the overall characteristics of the customer relationship at different stages. Customers, as important resources of an enterprise, have important value and life cycles. The customer life cycle is divided into the introduction stage, growth stage, maturity stage, and decline stage. Among them, the introduction stage is the gestation period of the customer relationship, the growth stage is the rapid development stage of the customer relationship, the maturity stage is the maturity and ideal stage of the customer relationship, and the decline stage is the stage where the customer relationship level reverses. The basic information, behavior information, and asset information in the customer information are clustered to obtain data clustering information, and the customer group information is divided into stages according to the data clustering information into the corresponding introduction stage, growth stage, maturity stage, and decline stage in the customer group information, which is convenient for accurately positioning customers.

[0083] As a further solution of the present invention: the data clustering information includes the activation status, account opening time, and number of transaction records of the customer, where the activation status includes not activated, activated, and fully closed accounts;

[0084] If the activation status of the main deposit customer is not activated, it is classified into the deposit introduction stage. If the activation status of the main deposit customer is activated, and the account opening time of the main deposit customer is lower than the preset account opening time, or the number of transaction records of the main deposit customer is lower than the preset number of transaction records, it is classified into the deposit introduction stage;

[0085] If the number of transactions of the primary deposit customer is higher than the preset number of transactions and lower than the preset number of mature transactions, it is classified into the deposit growth stage;

[0086] If the number of transactions of the primary deposit customer is higher than the preset number of mature transactions, it is classified into the deposit mature stage;

[0087] If the account opening time of the primary deposit customer is greater than the preset recession time and the number of transactions is zero, it is classified into the deposit recession stage. If the activation status of the primary deposit customer is fully closed, it is classified into the deposit recession stage;

[0088] If the activation status of the primary loan customer is not activated, it is classified into the loan introduction stage. If the activation status of the primary loan customer is activated, and the account opening time of the primary loan customer is lower than the preset account opening time, or the number of transactions of the primary loan customer is lower than the preset number of transactions, it is classified into the loan introduction stage;

[0089] If the number of transactions of the primary loan customer is higher than the preset number of transactions and lower than the preset number of mature transactions, it is classified into the loan growth stage;

[0090] If the number of transactions of the primary loan customer is higher than the preset number of mature transactions, it is classified into the loan mature stage;

[0091] If the account opening time of the primary loan customer is greater than the preset recession time and the number of transactions is zero, it is classified into the loan recession stage. If the activation status of the primary loan customer is fully closed, it is classified into the loan recession stage;

[0092] If the activation status of the primary insurance customer is not activated, it is classified into the insurance introduction stage. If the activation status of the primary insurance customer is activated, and the account opening time of the primary insurance customer is lower than the preset account opening time, or the number of transactions of the primary insurance customer is lower than the preset number of transactions, it is classified into the insurance introduction stage;

[0093] If the number of transactions of the primary insurance customer is higher than the preset number of transactions and lower than the preset number of mature transactions, it is classified into the insurance growth stage;

[0094] If the number of transactions of the primary insurance customer is higher than the preset number of mature transactions, it is classified into the insurance mature stage;

[0095] If the account opening time of the primary insurance customer is greater than the preset recession time and the number of transactions is zero, it is classified into the insurance recession stage. If the activation status of the primary insurance customer is fully closed, it is classified into the insurance recession stage.

[0096] Specifically, after classifying customers by attribution, further precisely locate the deposit introduction stage, deposit growth stage, deposit maturity stage, and deposit decline stage of primary deposit customers, the loan introduction stage, loan growth stage, loan maturity stage, and loan decline stage of primary loan customers, and the insurance introduction stage, insurance growth stage, insurance maturity stage, and insurance decline stage of primary insurance customers. Further segment the customers to facilitate the subsequent derivation of customer needs, so as to provide the best financial services and marketing activities and save sales costs.

[0097] As a further solution of the present invention: stratify the corresponding stages in the group stage information by value to obtain customer value information, including;

[0098] The corresponding stages in the group stage information include the deposit introduction stage, deposit growth stage, deposit maturity stage, and deposit decline stage of primary deposit customers, the loan introduction stage, loan growth stage, loan maturity stage, and loan decline stage of primary loan customers, and the insurance introduction stage, insurance growth stage, insurance maturity stage, and insurance decline stage of primary insurance customers;

[0099] The corresponding stages in the group stage information are stratified by value through the RFM model to obtain customer value information, and the customer value information includes high-value customers, medium-value customers, and low-value customers;

[0100] In the RFM model, R is the time of the last consumption, F is the consumption frequency, and M is the consumption amount;

[0101] Obtain R, F, and M of the corresponding stages in the group stage information according to the customer's personal transaction data, perform weighted summation on R, F, and M of the corresponding stages to obtain value data;

[0102] If the value data is greater than or equal to the high-value threshold, it is judged as a high-value customer;

[0103] If the value data is less than or equal to the low-value threshold, it is judged as a low-value customer;

[0104] If the value data is greater than the low-value threshold and less than the high-value threshold, it is judged as a medium-value customer;

[0105] Judge the corresponding high-value customers, medium-value customers, or low-value customers according to the value data for the corresponding stages in the group stage information;

[0106] That is, the deposit introduction stage, deposit growth stage, deposit maturity stage, and deposit decline stage of the primary deposit customers are respectively judged as corresponding high-value customers, medium-value customers, or low-value customers. The loan introduction stage, loan growth stage, loan maturity stage, and loan decline stage of the primary loan customers are respectively judged as corresponding high-value customers, medium-value customers, or low-value customers. The insurance introduction stage, insurance growth stage, insurance maturity stage, and insurance decline stage of the primary insurance customers are respectively judged as corresponding high-value customers, medium-value customers, or low-value customers.

[0107] Specifically, after classifying and dividing the stages of customers, further classify the customers again through the RFM model to improve the accuracy of customer classification and meet the current diverse and complex marketing needs, with accurate customer classification.

[0108] Furthermore, R, F, and M are respectively divided into several levels according to the corresponding value sizes, preferably five levels (such as from 1 to 5), and each level represents the relative ranking of customers. The levels of the three dimensions of R, F, and M are weighted and summed to obtain the comprehensive value data of each customer regarding R, F, and M.

[0109] Furthermore, according to the obtained R, F, and M, cluster the customers. Based on the clustering results, analyze the customer characteristics of each cluster, and respectively label them as high-value, medium-value, and low-value customers to obtain a preliminary stratification result. Then, according to the value data obtained from the comprehensive RFM score, adjust the customers with the value data of the comprehensive RFM score at the 80th percentile and above to high-value customers, adjust the customers with the value data of the comprehensive RFM score between the 30th - 80th percentile to medium-value customers, and adjust the customers with the value data of the comprehensive RFM score at the 30th percentile and below to low-value customers, making the classification of customer value more accurate, with good classification effects and wide applicability.

[0110] As a further solution of the present invention: Analyze the customer value information and personal information to obtain dynamically updated customer portrait information, including;

[0111] The customer information in the personal information also includes consumption preference information, interest preference information, and abnormal risk information;

[0112] Dynamically update the customer value information and customer information, and analyze the dynamically updated customer value information and customer information to obtain dynamically updated customer portrait information.

[0113] Specifically, update the customer's basic information, behavioral information, asset information, fee preference information, interest preference information, abnormal risk information, and customer value information, and combine the customer's basic information, behavioral information, asset information, fee preference information, interest preference information, abnormal risk information, and customer value information to create a customer profile, obtaining customer profile information, which is convenient for determining the needs of customers after precise segmentation and has good usage effects.

[0114] As a further solution of the present invention: Obtain the financial services and marketing activities recommended to customers based on the customer profile information, including;

[0115] Input the customer profile information into a preset recommendation engine. The recommendation engine outputs the financial services and marketing activities recommended to customers, and pushes the financial services and marketing activities to the corresponding customers;

[0116] The financial services include financial products and data services.

[0117] Specifically, the recommendation engine includes a real-time recommendation engine built based on RisingWave (an open-source streaming database developed by RisingWave Labs), Kafka (an open-source stream processing platform developed by the Apache Software Foundation), and Redis (an open-source log-based key-value database). The recommendation engine can match cross-marketing products, precision marketing data services, and marketing rights and interests activities according to the customer profile information;

[0118] By inputting the customer profile information into a preset recommendation engine, the recommendation engine performs calculations, combines the recommendation engine with the customer profile information, and obtains matching cross-marketing products, precision marketing data services, and marketing rights and interests activities, which are suitable for the current increasingly complex, diverse, and automated precision marketing model.

[0119] Please refer to Figure 2 , a dynamic marketing classification device, which includes;

[0120] Acquisition module 1, which acquires the personal information of customers and classifies the customers according to the personal information to obtain customer group information;

[0121] Group module 2, which obtains group characteristic information according to the personal information and divides the customer group information into stages according to the group characteristic information to obtain group stage information;

[0122] Value module 3, which stratifies the corresponding stages in the group stage information to obtain customer value information;

[0123] Dynamic portrait module 4, which analyzes and obtains dynamically updated customer portrait information according to the customer value information and personal information;

[0124] An output module 5, which obtains financial services and marketing activities recommended to customers based on customer portrait information.

[0125] An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the dynamic marketing classification method as described above.

[0126] A computer-readable storage medium storing a computer program, which implements the dynamic marketing classification method as described above when executed by a processor.

[0127] As mentioned above, it is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A dynamic marketing classification method, characterized in that: include: Obtaining dynamic personal information of customers, and classifying customers according to the personal information to obtain customer group information; Obtaining group characteristic information based on the personal information, and dividing the customer group information into stages based on the group characteristic information to obtain group stage information; Performing value stratification on the corresponding stages in the group stage information to obtain customer value information; Dynamically updated customer profile information is obtained based on the customer value information and personal information analysis; Financial services and marketing activities are recommended to customers based on the customer profile information.

2. The dynamic marketing classification method according to claim 1, characterized in that: The obtaining of dynamic personal information of customers and classifying customers according to the personal information to obtain customer group information includes: The personal information includes customer information and customer personal transaction data, wherein the customer information includes basic information, behavior information and asset information, and the customer personal transaction data includes deposit data, loan data and insurance data, wherein the customer information and customer personal transaction data are updated at the system preset time; The customer group information includes main deposit customers, main loan customers and main insurance customers; If the customer's deposit data is greater than or equal to the preset deposit threshold, and the deposit data is greater than the loan data and insurance data, then the customer is classified as a primary deposit customer; If the customer's loan data is greater than or equal to the preset loan threshold, and the loan data is greater than the deposit data and insurance data, the customer is classified as a primary loan customer; If the customer's insurance data is greater than or equal to the preset insurance threshold, and the insurance data is greater than the deposit data and loan data, the customer is classified as a primary insurance customer.

3. The dynamic marketing classification method according to claim 2, characterized in that: The group characteristic information is obtained according to the personal information, and the customer group information is divided into stages according to the group characteristic information to obtain group stage information; The group stage information includes the introduction stage, growth stage, maturity stage and decline stage corresponding to the customer group information; The basic information, behavior information and asset information in the customer information are clustered to obtain data clustering information, and the customer group information is divided into the introduction stage, growth stage, maturity stage and decline stage corresponding to the customer group information according to the data clustering information.

4. The dynamic marketing classification method according to claim 3, characterized in that: The data clustering information includes the customer's activation status, account opening time and number of transactions, where the activation status includes unactivated, activated and completely closed account; If the activation status of the primary deposit customer is not activated, it is divided into the deposit introduction stage; if the activation status of the primary deposit customer is activated, and the account opening time of the primary deposit customer is lower than the preset account opening time, or the number of transactions of the primary deposit customer is lower than the preset number of transactions, it is divided into the deposit introduction stage; If the number of transactions of the primary deposit customer is higher than the preset number of transactions and lower than the preset number of mature transactions, it is classified into the deposit growth stage; If the number of transactions of the primary deposit customer is higher than the preset number of mature transactions, it is classified as a deposit mature stage; If the account opening time of the main deposit customer is greater than the preset decay time and the number of transactions is zero, it is classified as the deposit decay stage; if the activation status of the main deposit customer is full account cancellation, it is classified as the deposit decay stage; If the activation status of the main loan customer is not activated, it is divided into the loan introduction stage; if the activation status of the main loan customer is activated, and the account opening time of the main loan customer is lower than the preset account opening time, or the number of transactions of the main loan customer is lower than the preset number of transactions, it is divided into the loan introduction stage; If the number of transactions of the master loan customer is higher than the preset number of transactions and lower than the preset number of mature transactions, the loan is classified into the growth stage; If the number of transactions of the main loan customer is higher than the preset number of mature transactions, the loan is classified into the mature stage; If the account opening time of the main loan customer is greater than the preset decay time and the number of transactions is zero, it is classified into the loan decay stage; if the activation status of the main loan customer is full account cancellation, it is classified into the loan decay stage; If the activation status of the primary insurance customer is not activated, it is divided into the insurance introduction stage; if the activation status of the primary insurance customer is activated, and the account opening time of the primary insurance customer is lower than the preset account opening time, or the number of transactions of the primary insurance customer is lower than the preset number of transactions, it is divided into the insurance introduction stage; If the number of transactions of the main insurance customer is higher than the preset number of transactions and lower than the preset number of mature transactions, it is classified as the insurance growth stage; If the number of transactions of the main insurance customer is higher than the preset number of mature transactions, it is classified as the insurance mature stage; If the account opening time of the main insurance customer is greater than the preset decay time and the number of transactions is zero, it is classified into the insurance decay stage; if the activation status of the main insurance customer is full account cancellation, it is classified into the insurance decay stage.

5. The dynamic marketing classification method according to claim 4, characterized in that: The step of performing value stratification on the corresponding stages in the group stage information to obtain customer value information includes: The corresponding stage in the group stage information is value-stratified by the RFM model to obtain customer value information, wherein the customer value information includes high-value customers, medium-value customers and low-value customers; In the RFM model, R is the most recent consumption time, F is the consumption frequency, and M is the consumption amount; According to the customer's personal transaction data, obtain the R, F and M of the corresponding stage in the group stage information, perform weighted summation on the R, F and M of the corresponding stage and obtain value data; If the value data is greater than or equal to the high value threshold, the customer is judged to be a high-value customer; If the value data is less than or equal to the low value threshold, the customer is judged to be a low-value customer; If the value data is greater than the low value threshold and less than the high value threshold, it is determined to be a medium value customer; According to the value data, the corresponding stage in the group stage information is judged to obtain the corresponding high-value customer, medium-value customer or low-value customer.

6. The dynamic marketing classification method according to claim 5, characterized in that: The dynamically updated customer portrait information obtained by analyzing the customer value information and personal information includes: The customer information in the personal information also includes consumption preference information, interest preference information and abnormal risk information; The customer value information and customer information are dynamically updated, and dynamically updated customer portrait information is obtained based on the dynamically updated customer value information and customer information analysis.

7. The dynamic marketing classification method according to claim 6, characterized in that: The financial services and marketing activities recommended to the customer based on the customer profile information include: The customer profile information is input into a preset recommendation engine, and the recommendation engine outputs financial services and marketing activities recommended to the customer, and pushes the financial services and marketing activities to the corresponding customer; The financial services include financial products and data services.

8. A dynamic marketing classification device, using the dynamic marketing classification method according to any one of claims 1 to 7, characterized in that: The device comprises: An acquisition module, wherein the acquisition module acquires the personal information of the customer, and classifies the customer according to the personal information to obtain customer group information; A group module, wherein the group module obtains group characteristic information according to the personal information, and divides the customer group information into stages according to the group characteristic information to obtain group stage information; A value module, wherein the value module performs value stratification on the corresponding stages in the group stage information to obtain customer value information; A dynamic portrait module, wherein the dynamic portrait module obtains dynamically updated customer portrait information based on the customer value information and personal information analysis; An output module, wherein the output module obtains financial services and marketing activities recommended to customers based on the customer portrait information.

9. An electronic device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the dynamic marketing classification method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the dynamic marketing classification method according to any one of claims 1 to 7 is implemented.

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