CN119295194A - Credit card recommendation method, device, electronic device and computer-readable storage medium - Google Patents
Credit card recommendation method, device, electronic device and computer-readable storage medium Download PDFInfo
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Abstract
本申请公开了一种信用卡推荐方法、装置、电子设备和计算机可读存储介质。所述方法包括：通过金融机构的第一业务渠道获取借记卡存量客户的多维度客户数据；通过金融机构的第二业务渠道获取信用卡客群信息，所述信用卡客群信息包括信用卡客群分类信息和每种信用卡客群对应的信用卡推荐策略，所述第一业务渠道与所述第二业务渠道为所述金融机构的不同业务渠道；根据所述信用卡客群分类信息和所述多维度客户数据确定所述借记卡存量客户所属的信用卡客群；获取所述信用卡客群对应的信用卡推荐策略，以根据所述信用卡客群对应的信用卡推荐策略向相应的借记卡存量客户进行信用卡推荐。
The present application discloses a credit card recommendation method, device, electronic device and computer-readable storage medium. The method comprises: obtaining multi-dimensional customer data of existing debit card customers through a first business channel of a financial institution; obtaining credit card customer group information through a second business channel of the financial institution, the credit card customer group information comprising credit card customer group classification information and a credit card recommendation strategy corresponding to each credit card customer group, the first business channel and the second business channel being different business channels of the financial institution; determining the credit card customer group to which the existing debit card customer belongs based on the credit card customer group classification information and the multi-dimensional customer data; obtaining the credit card recommendation strategy corresponding to the credit card customer group, so as to recommend a credit card to the corresponding existing debit card customer based on the credit card recommendation strategy corresponding to the credit card customer group.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a credit card recommendation method, apparatus, electronic device, and computer readable storage medium.
Background
With the rapid development of financial technology, retail banking is faced with unprecedented challenges and opportunities. Credit cards are one of the important products of retail banks, and customer expansion and management of the credit cards are key to the bank's competitiveness. However, conventional credit card customer extensions often rely on extensive marketing campaigns and simple customer classification, which makes it difficult to accurately identify and meet the customer's personalized needs.
In the prior art, financial institutions such as banks typically use the following means to increase credit card customer penetration:
(1) Traditional marketing means such as telemarketing, short message marketing, mail marketing and the like are used for popularizing credit card products to potential customers in a wide spread network mode. This approach is inefficient and is prone to customer objection and interference.
(2) Data analysis and marketing part of banks begin to use big data technology to conduct customer analysis, but are often limited to simple data screening and classification, and lack deep customer portraits and personalized recommendations.
(3) Single product promotion, namely, banks tend to promote credit card products in a single product line only for a certain class of customers, and lack a collaborative sales mechanism across business channels.
Disclosure of Invention
Aiming at the defects of the credit card selling mechanism in the prior art, at least solving part of the technical problems, the embodiment of the application provides a credit card recommending method, a credit card recommending device, electronic equipment and a computer readable storage medium.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a credit card recommendation method, including:
Obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
acquiring credit card guest group information through a second business channel of a financial institution, wherein the credit card guest group information comprises credit card guest group classification information and credit card recommendation strategies corresponding to each credit card guest group, and the first business channel and the second business channel are different business channels of the financial institution;
Determining a credit card guest group to which the debit card stock customer belongs according to the credit card guest group classification information and the multidimensional customer data;
And acquiring a credit card recommendation strategy corresponding to the credit card guest group so as to recommend the credit card to the corresponding debit card stock client according to the credit card recommendation strategy corresponding to the credit card guest group.
In a second aspect, an embodiment of the present application further provides a credit card recommendation device, where the device includes:
a first obtaining unit for obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
A second obtaining unit, configured to obtain credit card guest group information through a second service channel of a financial institution, where the credit card guest group information includes credit card guest group classification information and credit card recommendation policies corresponding to each credit card guest group, and the first service channel and the second service channel are different service channels of the financial institution;
A customer classification unit for determining a credit card customer group to which the debit card stock customer belongs according to the credit card customer group classification information and the multidimensional customer data;
and the credit card recommending unit is used for acquiring the credit card recommending strategies corresponding to the credit card guest groups so as to recommend credit cards to corresponding debit card stock clients according to the credit card recommending strategies corresponding to the credit card guest groups.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a credit card recommendation method.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform a credit card recommendation method.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects:
According to the embodiment of the application, the multidimensional customer data of the debit card stock customers are acquired through the first business channel of the financial institution, the credit card customer group information is acquired through the second business channel of the financial institution, and the debit card stock customers are finely classified according to the credit card customer group classification information and the multidimensional customer data, so that the credit card recommendation is carried out on the debit card stock customers of the corresponding credit card customer groups according to the credit card recommendation strategies corresponding to each credit card customer group.
The embodiment of the application uses the debit card stock customers as credit card recommendation objects, not only can widen the potential customer groups of the credit card, but also can realize cross-product line collaborative sales of debit card products and credit card products, improve the comprehensive contribution degree of the customers, finely classify the debit card stock customers through multi-dimensional data and credit card customer group classification information, improve the classification precision of the customers, pertinently recommend the credit card according to the fine classification result of the customers, and improve the matching degree of the credit card products and the customer demands.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flowchart of a credit card recommendation method according to an embodiment of the application;
FIG. 2 is a flow chart of classifying credit card customers for a debit card inventory customer in accordance with an embodiment of the application;
FIG. 3 is a schematic diagram of a credit card recommendation device according to an embodiment of the application;
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
The conventional credit card recommendation mechanism suffers from at least the following drawbacks in terms of increasing the permeability of the credit card customer:
(1) Cross-selling of traditional credit card recommendation mechanisms is often limited to the inside of a single product line, and the lack of a collaborative selling mechanism across business channels leads to poor cross-selling effects and low comprehensive contribution of customers.
(2) Customer subdivision is not accurate enough, traditional schemes are often classified based on simple customer attributes, mining and analysis of deep information such as customer financial behaviors, consumption habits, risk preferences and the like are lacked, and therefore customer subdivision is not accurate enough, and personalized requirements of customers are difficult to meet.
(3) The matching degree of the product is low, and as the subdivision of the customer is not accurate enough, financial institutions such as banks often lack pertinence when recommending credit card products, the matching degree of the product and the customer needs is low, and the purchasing desire of the customer is difficult to excite.
(4) Traditional marketing means are inefficient and easily cause customer dislike and conflict, and simple data analysis and marketing are difficult to realize accurate marketing and personalized recommendation.
As can be seen, the conventional credit card recommendation mechanism is often based on a rough customer classification and marketing strategy, so that it is difficult to effectively identify and meet the personalized requirements of customers, and the credit card service is slow to increase and the customer experience is poor.
Aiming at the problems, the embodiment of the application aims to solve the problems of expansion and fine management of credit card clients in retail business of financial institutions. In particular, embodiments of the present application aim to improve the penetration, satisfaction and loyalty of credit card customers through accurate customer segmentation, data analysis, product matching and personalized marketing schemes.
The embodiment of the application provides a credit card recommendation method, as shown in fig. 1, and provides a flow chart of the credit card recommendation method in the embodiment of the application, wherein the method at least comprises the following steps S110 to S140:
step S110, multi-dimensional customer data of the debit card stock customers is obtained through a first business channel of the financial institution.
The execution subject of the credit card recommendation method in the embodiment of the application is a computing device, and the computing device should acquire a recommendation object of the credit card when recommending the credit card. In financial institutions such as banks, debit card services and credit card services belong to different service channels (or service lines), and the traditional scheme is to acquire a target guest group of a credit card through the credit card service channels and recommend credit card products for the target guest group. Thus, cross-selling of traditional credit card recommendation mechanisms is often limited to the inside of the credit card product line, and co-selling of debit card product lines and credit card product lines across product line channels cannot be achieved, resulting in low overall contribution by customers.
Based on this, the embodiment of the application obtains the debit card stock client through the first business channel of the financial institution, and uses the debit card stock client as the target guest group of the credit card, and in practical application, the debit card stock client in a few years can be used as the target guest group of the credit card. Wherein the first transaction channel is a channel for managing debit card transactions.
It is understood that the computing device may be hardware or software. When the computing device is hardware, the computing device may be a distributed cluster formed by a plurality of servers or terminal devices, or may be a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices listed above. The computing device may also be a plurality of software or software modules for providing distributed services, or may be implemented as a single software or software module, without limitation.
Step S120, credit card guest group information is acquired through a second business channel of the financial institution, the credit card guest group information comprises credit card guest group classification information and credit card recommendation strategies corresponding to each credit card guest group, and the first business channel and the second business channel are different business channels of the financial institution.
The second business channel in the embodiment of the application is a channel for managing credit card business, the first business channel and the second business channel are different business channels of the financial institution, a communication barrier exists between the two business channels, and the second business channel is difficult to directly acquire client data from the first business channel. Therefore, the embodiment of the application acquires the multidimensional customer data of the debit card stock customers from the first business channel by using the computing equipment of the financial institution, acquires the credit card customer group information from the second business channel, and integrates the data of the first business channel and the second business channel by using the computing equipment so as to realize data sharing and strategy coordination.
The credit card guest group information of the embodiment of the application comprises credit card guest group classification information and credit card recommendation strategies corresponding to each credit card guest group, wherein the credit card guest group classification information is used for classifying debit card stock clients, and the credit card recommendation strategies are used for recommending credit cards to the debit card stock clients.
It should be understood that the embodiment of the present application is not limited to the execution sequence of the step S110 and the step S120, and in other embodiments of the present application, the step S120 may be executed first and then the step S110 may be executed, or the step S110 and the step S120 may be executed simultaneously.
And step S130, determining the credit card customer group to which the debit card stock customer belongs according to the credit card customer group classification information and the multidimensional customer data.
Step S140, obtaining a credit card recommendation policy corresponding to the credit card guest group, so as to recommend credit cards to corresponding debit card stock clients according to the credit card recommendation policy corresponding to the credit card guest group.
As can be seen from the credit card recommendation method shown in fig. 1, in the embodiment of the present application, multi-dimensional customer data of a debit card stock customer is obtained through a first service channel of a financial institution, credit card stock customer group information is obtained through a second service channel of the financial institution, and the debit card stock customer is finely classified according to the credit card stock group classification information and the multi-dimensional customer data, so as to recommend credit cards to the debit card stock customer of the corresponding credit card stock group according to a credit card recommendation policy corresponding to each credit card stock group.
The embodiment of the application uses the debit card stock customers as credit card recommendation objects, not only can widen the potential customer groups of the credit card, but also can realize cross-product line collaborative sales of debit card products and credit card products, improve the comprehensive contribution degree of the customers, finely classify the debit card stock customers through multi-dimensional data and credit card customer group classification information, improve the classification precision of the customers, pertinently recommend the credit card according to the fine classification result of the customers, and improve the matching degree of the credit card products and the customer demands.
In some embodiments of the application, the multi-dimensional customer data includes at least personal attribute data, asset attribute data, risk attribute data, consumption attribute data, and product attribute data, wherein:
the personal attribute data mainly comprises client basic information such as client codes, gender, age, academic, address, marital, job position and the like;
the asset attribute data mainly comprises client asset information such as personal income status of clients, deposit amount of clients, whether the clients have cars, investment sum, liability sum and the like;
the risk attribute data mainly comprise whether a customer has overdue conditions, overdue times, whether the customer has cashing behaviors, the number of charge repayment and the like;
the consumption attribute data mainly comprises customer consumption preference, stage condition, cash taking condition, bank business handling condition, whether third party payment service (such as payment treasury service, weChat service and the like) is bound, credit card use frequency, debit card and consumption habit information of the credit card and the like;
the product attribute data includes information such as credit card amount, number of credit card sub-cards, credit card grade, credit card type, type of held credit, date of opening an account, etc.
The customer data of the embodiment of the application not only comprises the traditional personal attribute data and the asset attribute data, but also comprises various dimensions such as consumption attribute data, risk attribute data, product attribute data and the like, so that the characteristics and the requirements of the customer can be accurately identified.
In some embodiments of the present application, the credit card group classification information includes credit card marketing object portrayal information for coarse classification and category of credit card groups for fine classification, classification characteristics of each credit card group, and classification priority of each credit card group.
The credit card marketing object portrait information is to roughly classify the customers with the credit card stock based on the credit card marketing potential, and comprises the customer authorization information of the credit card marketing, the credit card holding information and the age statistical information with the credit card marketing potential.
When the credit card customer group classification information is configured through the second service channel, the embodiment of the application obtains the multidimensional customer data of the credit card stock customers through the second service channel, and the multidimensional customer data of the credit card stock customers is subjected to data analysis, so that the customers with certain repayment capacity and the credit card have specific age distribution characteristics (for example, the ages of the holding credit cards are mainly 22-50 years old), and therefore, the ages are taken as one customer characteristic when the image information of the credit card marketing objects is configured.
The credit card marketing client authorization information is related to the client experience, for example, the cross-selling client information provided by the first business channel is not authorized by the client, so that when the financial institution touches the client with the credit card, the client is possibly inspired or contradicted, and the user experience is affected, therefore, the credit card marketing client authorization information is taken as another client feature of the credit card marketing object portrait information. In addition, the mechanism can also remarkably shorten the approval time, improve the approval efficiency and reduce the waiting time of clients.
When a debit card stock client already holds the credit card of the present invention, then the debit card stock client has a lower need to hold other credit cards of the present invention, and therefore the present embodiment uses the credit card holding information as the third client feature of the credit card marketing object representation information.
Three customer features through the credit card marketing object portrayal information enable the screening of target debit card stock customers with credit card marketing potential from debit card stock customers. The classification accuracy of the credit card groups can be improved by further classifying the target debit card stock customers by the kinds of the credit card groups, the classification characteristics of each credit card group, and the classification priority of each credit card group.
The credit card guest group sequentially comprises a credit card guest group, a credit guest group, an AUM guest group and a potential guest group according to the order of the classification priority from high to low, and the classification characteristics of each credit card guest group are obtained through the following steps:
Obtaining multidimensional customer data of credit card stock customers through the second service channel;
determining a generation reference guest group, a credit reference guest group, an AUM reference guest group and a potential reference guest group with asset potential and repayment potential according to the multidimensional client data of the credit card stock clients;
and carrying out customer characteristic statistics on the issuing reference customer group, the individual credit reference customer group, the AUM reference customer group and the potential reference customer group through a set algorithm (such as a quantile screening algorithm), and determining issuing amount characteristics corresponding to the issuing reference customer group, loan and overdue characteristics of the financial structure corresponding to the individual credit reference customer group in a first time window, month and day average AUM characteristics of the AUM reference customer group in a second time window, and third party payment service quantity characteristics, held financial product quantity characteristics and debit card activity characteristics of the potential reference customer group according to customer characteristic statistics results.
And the characteristic of the average AUM of the AUM reference guest group in the second time window is used as the characteristic of the average AUM of the AUM guest group in the second time window, and the third party payment service quantity characteristic of the potential reference guest group, the quantity characteristic of the held financial products and the active characteristic of the debit card are used as the third party payment service quantity characteristic of the potential guest group, the quantity characteristic of the held financial products and the active characteristic of the debit card.
When determining the generation reference guest group, the individual credit reference guest group, the AUM reference guest group and the potential reference guest group with the asset potential and the repayment potential according to the multidimensional client data of the credit card stock clients, the embodiment can screen out the high-quality clients with the asset potential and the repayment potential according to the multidimensional client data of the credit card stock clients, and divide the guest groups of the high-quality clients to obtain sample data of the generation reference guest group, the individual credit reference guest group, the AUM reference guest group and the potential reference guest group.
For example, the supply and repayment units and the affiliated positions of credit card deposit customers are higher in property and liability to a certain extent in terms of personal attributes, such as the property and repayment capability of customers engaged in state-owned enterprise, individual enterprise and the like, and the credit card deposit customers can be used as sample data for carrying out customer feature statistics, the credit card deposit customers are higher in capability of holding a plurality of loan products and overdue times of 0 in terms of loan attributes, the credit card deposit customers can be used as sample data for carrying out customer feature statistics, and the AUM distribution is higher in capability of carrying out customer refund of the AUM more than a set limit value (for example, more than 40000 yuan) in the month of the last half year, and the credit card deposit customers belong to good customers.
Thus, by classifying the high-quality clients, sample data of each reference guest group can be obtained, and by performing the client feature statistics on the sample data, the client feature of each reference guest group can be obtained, and the client feature of each reference guest group is used as the classification feature of the classified guest group of the debit card stock clients.
In configuring the classification priority of each credit card group, the classification priority of the credit card group may be configured with reference to the overdue risk, for example, considering that the relative risk of the alternate distribution group is low and the relative risk of the potential distribution group is high, the alternate distribution group is configured as the highest classification priority, and the potential distribution group is configured as the lowest classification priority.
It can be appreciated that when the classification priority configuration of the credit card guest group is performed, those skilled in the art can flexibly configure the classification priority configuration according to the requirements.
In some embodiments of the present application, the determining the credit card group to which the debit card stock client belongs in the step S130 according to the credit card group classification information and the multidimensional client data specifically includes:
coarse classification is carried out on the debit card stock clients according to the credit card marketing object portrait information and the multidimensional client data, so that target debit card stock clients with credit card marketing potential are obtained;
And classifying the target debit card stock clients according to the types of the credit card stock clients, the classification characteristics of each credit card stock client, the classification priority of each credit card stock client and the multidimensional client data to obtain the credit card stock clients to which the target debit card stock clients belong.
In some possible implementations of the present embodiments, the rough classification of the debit card stock customers according to the credit card marketing object representation information and the multidimensional customer data, obtaining target debit card stock customers with credit card marketing potential, specifically includes:
acquiring the credit card marketing client authorization information of the debit card stock client according to a pre-established risk pre-credit whitelist;
Acquiring credit card holding information and age information of the debit card stock customers according to the multi-dimensional customer data;
Matching the credit card marketing customer authorization information, the credit card holding information and the age information of the debit card stock customers with the credit card marketing target portrait information, and determining whether the debit card stock customers have credit card marketing potential according to the information matching result;
When it is determined that the debit card stock client has credit card marketing potential, the debit card stock client is treated as a target debit card stock client.
Specifically, a pre-established risk pre-credit white list is queried according to a client code, whether the debit card stock client belongs to the risk pre-credit white list client is determined according to a query result, age information of the debit card stock client is obtained according to personal attribute data, and credit card holding information of the debit card stock client is obtained according to product attribute data. When the debit card stock client belongs to the risk pre-credit white list client, the credit card holding information is not holding a credit card and the age information corresponds to the age statistics information, the debit card stock client is determined to be the target debit card stock client with credit card marketing potential, otherwise, the debit card stock client is determined not to have credit card marketing potential, and the debit card stock client is not recommended to the credit card.
In other possible implementations of this embodiment, the classifying the target debit card stock client according to the type of the credit card stock client, the classification characteristic of each credit card stock client, the classification priority of each credit card stock client, and the multidimensional client data, to obtain the credit card stock client to which the target debit card stock client belongs, specifically includes:
determining a classification feature matching order according to the classification priority of each credit card guest group, wherein the credit card guest groups with high classification priorities have high classification feature matching orders;
matching the multi-dimensional customer data with the classification features of the corresponding credit card customer groups according to the classification feature matching sequence, and determining the credit card customer groups of the target debit card stock customers according to the matching result, wherein the specific matching classification process is as follows:
Obtaining a matching result of the multi-dimensional client data and the classification characteristics of the current credit card customer group;
Dividing target debit card stock customers matched with the classification characteristics of the current credit card customer group into the current credit card customer group, and matching the classification characteristics of the rest other target debit card stock customers and the next-stage credit card customer group according to the classification characteristic matching sequence until the classification of the final credit card customer group is completed.
Taking the scenario shown in fig. 2 as an example, in the scenario shown in fig. 2, the credit card group sequentially includes a sending agent group, a credit agent group, an AUM group and a potential agent group according to the order of the classification priority from high to low, wherein:
The classification features of the agent group comprise agent amount features, such as agent amount greater than 5000 yuan;
The classification characteristic of the individual credit group includes loan and overdue characteristics of the financial structure within a first time window, such as having a record of loans and no record of overdue during the past year;
The classification features of the AUM (Asset Under Management, managing asset size) group of guests include a month and day average AUM feature within a second time window, e.g., a month and day average AUM value of greater than 40000 yuan for the last half year;
The classification features of the potential guest group comprise at least one of authorized third party payment service number features, held financial product number features and debit card activity features, for example, the number of third party payment services is more than 2, the number of held financial products is more than 2, and the number of checkout times of the check-out card analyzed by MCC (Merchant Category Code ) in the last year is more than 6.
It should be understood that each credit card customer group may further divide a plurality of sub-customer groups, taking the issuing customer group as an example, the issuing customer group may divide the sub-customer groups according to the amount level of the issuing amount and the issuing enterprise information (enterprise scale, enterprise area, enterprise property), in practical application, by analyzing the issuing enterprise quality and the regional concentration, the preferential test point area is definitely determined, for example, 200 persons (or the number of persons with other orders of magnitude) are taken as the median of the enterprise scale, and the sub-customer groups of the issuing customer group are formulated according to the first line city, the second line city and other cities, so as to perform differential cross-selling exploration according to the actual issuing enterprise volume, thereby improving the credit card permeability.
Taking individual credit groups as an example, analyzing the preference of the credit groups to large-amount classified products through the loan information held by the clients, scoring the response preference of each client in each channel according to the historical responsivity and the behavior feedback, dividing the sub-groups of the individual credit groups according to the scoring condition, and matching credit card services for the corresponding sub-groups.
Taking the potential guest group as an example, the potential guest group is further divided into a high binding sub-guest group (debit card stock customers authorize binding a plurality of third party payment services), a multi-product sub-guest group (debit card stock customers hold a plurality of financial products, financial product profits include consumer loan products, house mortgage products, financial products and issuing products), a high active sub-guest group (debit card checkout times are more), and the like.
As shown in fig. 2, a debit card stock client is obtained through a first business channel of a financial institution as a credit card recommendation object, the debit card stock client is roughly classified based on the image information of the credit card marketing object, and a target debit card stock client with credit card marketing potential is screened out of the debit card stock clients through the rough classification; when sorting target debit card stock customers, screening out debit card stock customers with the instead-issued finance greater than 5000 yuan from the target debit card stock customers according to the instead-issued monetary amount characteristics and adding the debit card stock customers to the instead-issued customer group, screening out debit card stock customers with loan records and without overdue records in the past year from the rest target debit card stock customers according to the loan and overdue characteristics of the financial structure in a first time window and adding the debit card stock customers to the credit customer group, screening out debit card stock customers with the month-day average AUM value greater than 40000 yuan from the rest target debit card stock customers according to the month-day average AUM characteristics in a second time window and adding the debit card stock customers with the month-day average AUM value greater than 40000 yuan in the last half year to the AUM customer group, and finally screening out debit card stock customers with the third party payment service number greater than 2 or the number of the held financial products greater than 2 or the number of times of the debit card stock potential of 6 times of the last year from the rest target debit card stock customers according to the authorized third party payment service number characteristics and debit card activity characteristics. Wherein the same debit card stock customers are not included in the issuing, individual credit, AUM and potential customers, i.e., each debit card stock customer is divided into at most one credit card customer group, and a debit card stock customer is not located in different credit card customer groups at the same time.
The embodiment of the application analyzes the client requirement corresponding to each credit card customer group in advance so as to formulate a credit card matching strategy for the corresponding credit card customer group according to the client requirement, for example:
The method comprises the steps of configuring various credit cards capable of binding payroll discount coupons, exclusive mall discount coupons and the like for a payroll group, configuring credit card products suitable for overdraft consumption for each payroll group, configuring credit card products with high-value equity services for AUM groups, such as medical equity and fitness equity, configuring credit card products with fund application discount and insurance policy renewal discount for potential guest groups holding multiple products, configuring credit card products with scene consumption equity for more active potential guest groups, wherein the scene consumption equity comprises video members, catering discount coupons, game recharge coupons and the like, and configuring common credit card products for other potential guest groups, such as third party payment service vertical reduction and the like.
In addition, a personalized credit card marketing strategy can be formulated for each credit card customer group, for example, an electronic business ticket for promoting the consumption rights and setting the amount is customized for the issuing customer group, an electronic business ticket for classifying the preferential rights and setting the amount is customized for the individual credit customer group, an electronic business ticket for high-value rights and setting the amount is customized for the AUM customer group, and an electronic business ticket for promoting the consumption rights, classifying the preferential rights and setting the amount is customized for the potential customer group.
The embodiment can increase the added value and the attractive force of credit card products through the customized credit card matching strategy and the personalized credit card marketing scheme, thereby being beneficial to improving the permeability and the loyalty of the credit card.
As can be seen from the above embodiments of the present application, the credit card recommendation method according to the embodiments of the present application has at least the following advantages:
Firstly, the embodiment of the application not only pays attention to the data analysis and marketing strategy formulation of a single business channel, but also emphasizes the data integration and marketing strategy cooperation of cross business channels, breaks through barriers among business channels in the traditional marketing mode, and forms comprehensive customer portraits and the marketing strategy of the cross business channels by integrating data resources from different business channels, thereby realizing a multi-channel and comprehensive credit card recommendation mechanism.
Second, the embodiment of the application accurately subdivides the debit card stock client by extracting multidimensional data such as personal attribute data, asset attribute data, risk attribute data, consumption attribute data, product attribute data and the like of the debit card stock client and applying a data analysis technology, the client classification method of the embodiment of the application not only considers the basic information of the client, but also combines a plurality of aspects of financial behavior, consumption habit and the like of the client, thereby being capable of more accurately identifying the potential high-quality credit card client group, forming more accurate client portrait and providing powerful support for the subsequent marketing strategy formulation.
Third, the embodiment of the application can increase the added value and the attractive force of credit card products through a customized credit card matching strategy and a personalized credit card marketing scheme, is beneficial to improving the permeability of credit cards and the loyalty degree, can more effectively allocate marketing resources, reduces the operation cost, is beneficial to reducing the credit risk and improving the business safety through the implementation of a risk pre-credit mechanism, and can better mine potential customer groups and expand market share through accurate customer subdivision and personalized marketing strategies.
The embodiment of the application also provides a credit card recommendation device, as shown in fig. 3, and provides a schematic structure diagram of the credit card recommendation device in the embodiment of the application, the credit card recommendation device 300 includes a first obtaining unit 310, a second obtaining unit 320, a client classifying unit 330 and a credit card recommendation unit 340, wherein:
A first obtaining unit 310 for obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
a second obtaining unit 320, configured to obtain credit card guest group information through a second business channel of a financial institution, where the credit card guest group information includes credit card guest group classification information and credit card recommendation policies corresponding to each credit card guest group, and the first business channel and the second business channel are different business channels of the financial institution;
a customer classification unit 330 for determining a credit card customer group to which the debit card stock customer belongs based on the credit card customer group classification information and the multi-dimensional customer data;
And the credit card recommending unit 340 is configured to obtain a credit card recommending policy corresponding to the credit card guest group, so as to recommend credit cards to corresponding debit card stock clients according to the credit card recommending policy corresponding to the credit card guest group.
In some embodiments of the present application, the credit card group classification information includes credit card marketing object portrayal information for coarse classification and credit card group categories for fine classification, classification characteristics of each credit card group and classification priorities of each credit card group, and the multi-dimensional customer data includes at least personal attribute data, asset attribute data, risk attribute data, consumption attribute data and product attribute data.
In some embodiments of the present application, the customer classification unit 330 includes a coarse classification module and a fine classification module;
the rough classification module is used for rough classification of the debit card stock clients according to the credit card marketing object portrait information and the multidimensional client data to obtain target debit card stock clients with credit card marketing potential;
And the fine classification module is used for carrying out fine classification on the target debit card stock clients according to the types of the credit card stock clients, the classification characteristics of each credit card stock client, the classification priority of each credit card stock client and the multi-dimensional client data to obtain the credit card stock clients to which the target debit card stock clients belong.
In some embodiments of the present application, the credit card marketing object representation information includes credit card marketing customer authorization information, credit card holding information and age statistics with credit card marketing potential, the coarse classification module is specifically configured to obtain the credit card marketing customer authorization information of the debit card marketing customer according to a pre-established risk pre-credit white list, obtain the credit card holding information and age information of the debit card marketing customer according to the multidimensional customer data, match the credit card marketing customer authorization information, the credit card holding information and the age information with the credit card marketing object representation information, determine whether the debit card marketing potential of the debit card marketing customer is provided according to the information matching result, and when the debit card marketing potential of the debit card marketing customer is determined, regard the debit card marketing customer as a target debit card marketing potential customer.
In some embodiments of the present application, the fine classification module is specifically configured to determine a classification feature matching order according to a classification priority of each credit card customer group, match the multi-dimensional customer data with classification features of the corresponding credit card customer group according to the classification feature matching order, and determine the credit card customer group to which the target debit card stock customer belongs according to a matching result.
In some embodiments of the present application, the fine classification module is specifically configured to obtain a matching result of the multi-dimensional customer data and classification features of the current credit card customer group, divide the target debit card stock customers matched with the classification features of the current credit card customer group into the current credit card customer group, and match the remaining other target debit card stock customers with classification features of the next stage credit card customer group according to the classification feature matching order until the classification of the final credit card customer group is completed.
In some embodiments of the present application, the credit card clusters include, in order of higher classification priority, a generation cluster, a credit cluster, an AUM cluster, and a potential cluster, wherein:
the classification characteristics of the agent group comprise agent amount characteristics;
the classification characteristics of the individual credit groups include loan and overdue characteristics of the financial structure within a first time window;
the classification features of the AUM guest group comprise month and day average AUM features in a second time window;
The classification characteristic of the potential guest group comprises at least one of an authorized third party payment service quantity characteristic, a held financial product quantity characteristic and a debit card activity characteristic.
It can be understood that the above-mentioned credit card recommendation device can implement the steps of the credit card recommendation method provided in the foregoing embodiment, and the relevant explanation about the credit card recommendation method is applicable to the credit card recommendation device, which is not repeated herein.
Fig. 4 is a schematic structural view of an electronic device according to an embodiment of the present application. Referring to fig. 4, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the credit card recommendation device on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
Obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
acquiring credit card guest group information through a second business channel of a financial institution, wherein the credit card guest group information comprises credit card guest group classification information and credit card recommendation strategies corresponding to each credit card guest group, and the first business channel and the second business channel are different business channels of the financial institution;
Determining a credit card guest group to which the debit card stock customer belongs according to the credit card guest group classification information and the multidimensional customer data;
And acquiring a credit card recommendation strategy corresponding to the credit card guest group so as to recommend the credit card to the corresponding debit card stock client according to the credit card recommendation strategy corresponding to the credit card guest group.
The method performed by the credit card recommendation device disclosed in the embodiment of fig. 1 of the present application may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor including a central processing unit (Central Processing Unit, CPU), a network Processor (Network Processor, NP), etc., or may be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory, the processor reads the information in the memory, and the steps of the credit card recommendation method are completed by combining the hardware of the processor.
The electronic device may also execute the method executed by the credit card recommendation device in fig. 1, and implement the functions of the credit card recommendation device in the embodiment shown in fig. 1, which is not described herein again.
The embodiment of the present application also proposes a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by an electronic device including a plurality of application programs, enable the electronic device to perform a method performed by the credit card recommendation apparatus in the embodiment shown in fig. 1, and specifically configured to perform:
Obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
acquiring credit card guest group information through a second business channel of a financial institution, wherein the credit card guest group information comprises credit card guest group classification information and credit card recommendation strategies corresponding to each credit card guest group, and the first business channel and the second business channel are different business channels of the financial institution;
Determining a credit card guest group to which the debit card stock customer belongs according to the credit card guest group classification information and the multidimensional customer data;
And acquiring a credit card recommendation strategy corresponding to the credit card guest group so as to recommend the credit card to the corresponding debit card stock client according to the credit card recommendation strategy corresponding to the credit card guest group.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1.A credit card recommendation method, characterized in that the credit card recommendation method comprises:
Obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
acquiring credit card guest group information through a second business channel of a financial institution, wherein the credit card guest group information comprises credit card guest group classification information and credit card recommendation strategies corresponding to each credit card guest group, and the first business channel and the second business channel are different business channels of the financial institution;
Determining a credit card guest group to which the debit card stock customer belongs according to the credit card guest group classification information and the multidimensional customer data;
And acquiring a credit card recommendation strategy corresponding to the credit card guest group so as to recommend the credit card to the corresponding debit card stock client according to the credit card recommendation strategy corresponding to the credit card guest group.
2. The credit card recommendation method according to claim 1, wherein,
The credit card guest group classification information includes credit card marketing object portrait information for coarse classification and the kind of credit card guest group for fine classification, classification characteristics of each credit card guest group and classification priority of each credit card guest group;
the multi-dimensional customer data includes at least personal attribute data, asset attribute data, risk attribute data, consumption attribute data, and product attribute data.
3. The credit card recommendation method according to claim 2, wherein said determining the credit card group to which the debit card stock client belongs based on the credit card group classification information and the multidimensional client data comprises:
coarse classification is carried out on the debit card stock clients according to the credit card marketing object portrait information and the multidimensional client data, so that target debit card stock clients with credit card marketing potential are obtained;
And classifying the target debit card stock clients according to the types of the credit card stock clients, the classification characteristics of each credit card stock client, the classification priority of each credit card stock client and the multidimensional client data to obtain the credit card stock clients to which the target debit card stock clients belong.
4. The credit card recommendation method according to claim 2, wherein the credit card marketing object representation information includes credit card marketing customer authorization information, credit card holding information, and age statistics with credit card marketing potential, the rough classification of the debit card stock customers based on the credit card marketing object representation information and the multidimensional customer data to obtain target debit card stock customers with credit card marketing potential, comprising:
acquiring the credit card marketing client authorization information of the debit card stock client according to a pre-established risk pre-credit whitelist;
Acquiring credit card holding information and age information of the debit card stock customers according to the multi-dimensional customer data;
Matching the credit card marketing customer authorization information, the credit card holding information and the age information of the debit card stock customers with the credit card marketing target portrait information, and determining whether the debit card stock customers have credit card marketing potential according to the information matching result;
When it is determined that the debit card stock client has credit card marketing potential, the debit card stock client is treated as a target debit card stock client.
5. The credit card recommendation method of claim 3, wherein said categorizing said target debit card stock customers according to the category of said credit card stock, categorization characteristics of each credit card stock and categorization priority of each credit card stock and said multi-dimensional customer data to obtain the credit card stock to which said target debit card stock customers belong comprises:
determining a classification feature matching order according to the classification priority of each credit card guest group;
And matching the multi-dimensional customer data with the classification features of the corresponding credit card customer groups according to the classification feature matching sequence, and determining the credit card customer groups of the target debit card stock customers according to the matching result.
6. The credit card recommendation method of claim 5, wherein said matching the classification features of the multi-dimensional customer data and the corresponding credit card clusters according to the classification feature matching order, determining the credit card cluster to which the target debit card stock customer belongs according to the matching result, comprises:
Obtaining a matching result of the multi-dimensional client data and the classification characteristics of the current credit card customer group;
Dividing target debit card stock customers matched with the classification characteristics of the current credit card customer group into the current credit card customer group, and matching the classification characteristics of the rest other target debit card stock customers and the next-stage credit card customer group according to the classification characteristic matching sequence until the classification of the final credit card customer group is completed.
7. The credit card recommendation method of claim 2, wherein the credit card clusters include, in order of classification priority from high to low, a credit card cluster, an AUM cluster, and a potential cluster, wherein:
the classification characteristics of the agent group comprise agent amount characteristics;
the classification characteristics of the individual credit groups include loan and overdue characteristics of the financial structure within a first time window;
the classification features of the AUM guest group comprise month and day average AUM features in a second time window;
The classification characteristic of the potential guest group comprises at least one of an authorized third party payment service quantity characteristic, a held financial product quantity characteristic and a debit card activity characteristic.
8. A credit card recommendation device, characterized in that the credit card recommendation device comprises:
a first obtaining unit for obtaining multidimensional customer data of a debit card stock customer through a first business channel of a financial institution;
A second obtaining unit, configured to obtain credit card guest group information through a second service channel of a financial institution, where the credit card guest group information includes credit card guest group classification information and credit card recommendation policies corresponding to each credit card guest group, and the first service channel and the second service channel are different service channels of the financial institution;
A customer classification unit for determining a credit card customer group to which the debit card stock customer belongs according to the credit card customer group classification information and the multidimensional customer data;
and the credit card recommending unit is used for acquiring the credit card recommending strategies corresponding to the credit card guest groups so as to recommend credit cards to corresponding debit card stock clients according to the credit card recommending strategies corresponding to the credit card guest groups.
9. An electronic device, comprising:
processor, and
A memory arranged to store computer executable instructions that, when executed, cause the processor to perform the credit card recommendation method of any of claims 1 to 7.
10. A computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the credit card recommendation method of any of claims 1-7.
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| CN202411275553.8A CN119295194A (en) | 2024-09-12 | 2024-09-12 | Credit card recommendation method, device, electronic device and computer-readable storage medium |
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| CN202411275553.8A CN119295194A (en) | 2024-09-12 | 2024-09-12 | Credit card recommendation method, device, electronic device and computer-readable storage medium |
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