CN115689128A - Customer data analysis method and system based on CRM - Google Patents
Customer data analysis method and system based on CRM Download PDFInfo
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- CN115689128A CN115689128A CN202211716541.5A CN202211716541A CN115689128A CN 115689128 A CN115689128 A CN 115689128A CN 202211716541 A CN202211716541 A CN 202211716541A CN 115689128 A CN115689128 A CN 115689128A
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Abstract
The invention relates to the field of big data, and discloses a customer data analysis method and a customer data analysis system based on CRM (customer relationship management), which are used for improving the accuracy of customer data analysis. The method comprises the following steps: obtaining historical sales data, and analyzing the return rate of the historical sales data to determine a target return rate; carrying out intention customer matching according to the target return rate, and determining an intention customer set; generating a client portrait for the set of the intended clients to obtain a client portrait corresponding to each intended client in the set of the intended clients; analyzing the incidence relation of the client figures corresponding to each intention client to determine the target incidence relation among the intention clients; carrying out client classification on a plurality of intention clients through a target association relation among the intention clients to obtain at least one client group; and generating a management strategy for at least one customer group to obtain a customer management strategy, and transmitting the customer management strategy to a preset customer information management terminal.
Description
Technical Field
The invention relates to the field of big data, in particular to a customer data analysis method and system based on CRM.
Background
With the rapid development of the internet technology, an intelligent and automatic analysis scheme can be provided for application scenes of different client data, and the efficiency of client maintenance can be improved when auxiliary personnel realize client maintenance and welfare batch issuing.
The existing scheme usually collects a large amount of customer data, but does not have the capacity of analyzing based on big data, can not well understand customer needs and preferences, and can not optimize customer relationship management according to the information, so that the accuracy rate is low when customer data is analyzed.
Disclosure of Invention
The invention provides a customer data analysis method and a customer data analysis system based on a CRM (customer relationship management), which are used for improving the accuracy of customer data analysis.
The invention provides a CRM-based customer data analysis method in a first aspect, which comprises the following steps: obtaining historical sales data, and analyzing the return rate of the historical sales data to determine a target return rate; conducting intention customer matching through the target return rate, and determining an intention customer set, wherein the intention customer set comprises: a plurality of intent customers; generating a client portrait for the set of the intention clients to obtain a client portrait corresponding to each intention client in the set of the intention clients; analyzing the incidence relation of the client figures corresponding to each intention client to determine the target incidence relation among the intention clients; classifying the plurality of intention clients according to the target association relationship among the intention clients to obtain at least one client group; and generating a management strategy for the at least one customer group to obtain a customer management strategy, and transmitting the customer management strategy to a preset customer information management terminal.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the acquiring historical sales data, and performing rate-of-return analysis on the historical sales data to determine a target rate-of-return includes: inquiring historical sales data from a preset CRM system, and performing data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data; respectively calculating the return rate of each group of sales data to obtain the return rate corresponding to each group of sales data; and performing rate of return fusion on the rate of return corresponding to each group of sales data to obtain a target rate of return.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the performing of the matching of the intention customers according to the target rate of return determines a set of intention customers, where the set of intention customers includes: a plurality of intent customers, comprising: performing data label matching on the target return rate to obtain a plurality of data labels; performing customer matching through the plurality of data tags to obtain an intention customer set, wherein the intention customer set comprises: a plurality of intended customers.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the generating a client representation of the set of intended clients to obtain a client representation corresponding to each intended client in the set of intended clients includes: performing customer data acquisition on each intention customer in the intention customer set to obtain customer associated data corresponding to each intention customer; carrying out preference characteristic analysis on the client associated data corresponding to each intention client respectively to obtain preference characteristics corresponding to each intention client; and generating portrayal through the preference characteristics corresponding to each intention client to obtain the client portrayal corresponding to each intention client in the intention client set.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the analyzing an association relationship between client figures corresponding to each intended client to determine a target association relationship between the intended clients includes: carrying out correlation index analysis on the client portrait corresponding to each intention client to obtain a corresponding correlation characteristic index; and constructing an association relation through the association characteristic indexes to obtain a target association relation among the intention customers.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the generating a management policy for the at least one customer group to obtain a customer management policy, and transmitting the customer management policy to a preset customer information management terminal includes: performing cluster analysis on the customer data of the at least one customer group to obtain target cluster data; performing identification matching through the target clustering data, and determining a strategy identification corresponding to the target clustering data; generating a management strategy through the strategy identification to obtain a customer management strategy; and transmitting the client management strategy to a preset client information management terminal.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the performing cluster analysis on the customer data of the at least one customer group to obtain target cluster data includes: performing spatial point mapping on the client data of the at least one client group to obtain a corresponding spatial point set; performing clustering point analysis on the space point set to determine a target clustering point; and carrying out cluster analysis on the customer data of the at least one customer group through the target cluster point to obtain target cluster data.
A second aspect of the present invention provides a CRM-based customer data analysis system comprising: the acquisition module is used for acquiring historical sales data, analyzing the return rate of the historical sales data and determining a target return rate; a matching module, configured to perform intent customer matching according to the target rate of return, and determine an intent customer set, where the intent customer set includes: a plurality of intent customers; the generation module is used for generating client figures of the intention client set to obtain the client figures corresponding to each intention client in the intention client set; the analysis module is used for analyzing the incidence relation of the client figures corresponding to each intention client and determining the target incidence relation among the intention clients; the classification module is used for carrying out client classification on the plurality of intention clients according to the target association relation among the intention clients to obtain at least one client group; and the generating module is used for generating a management strategy for the at least one customer group to obtain a customer management strategy and transmitting the customer management strategy to a preset customer information management terminal.
With reference to the second aspect, in a first implementation manner of the second aspect of the present invention, the obtaining module is specifically configured to: inquiring historical sales data from a preset CRM system, and performing data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data; respectively calculating the return rate of each group of sales data to obtain the return rate corresponding to each group of sales data; and performing rate of return fusion on the rate of return corresponding to each group of sales data to obtain a target rate of return.
With reference to the second aspect, in a second implementation manner of the second aspect of the present invention, the matching module is specifically configured to: performing data label matching on the target return rate to obtain a plurality of data labels; performing customer matching through the plurality of data tags to obtain an intention customer set, wherein the intention customer set comprises: a plurality of intended customers.
With reference to the second aspect, in a third implementation manner of the second aspect of the present invention, the generating module is specifically configured to: performing customer data acquisition on each intention customer in the intention customer set to obtain customer associated data corresponding to each intention customer; carrying out preference characteristic analysis on the client associated data corresponding to each intention client respectively to obtain preference characteristics corresponding to each intention client; and generating portrayal through the preference characteristics corresponding to each intention client to obtain the client portrayal corresponding to each intention client in the intention client set.
With reference to the second aspect, in a fourth implementation manner of the second aspect of the present invention, the analysis module is specifically configured to: analyzing the correlation indexes of the client figures corresponding to each intention client to obtain corresponding correlation characteristic indexes; and constructing an association relation through the association characteristic indexes to obtain a target association relation among the intention customers.
With reference to the second aspect, in a fifth implementation manner of the second aspect of the present invention, the classification module further includes: the analysis unit is used for carrying out clustering analysis on the client data of the at least one client group to obtain target clustering data; the matching unit is used for performing identification matching through the target clustering data and determining a strategy identification corresponding to the target clustering data; the generating unit is used for generating a management strategy through the strategy identification to obtain a customer management strategy; and the transmission unit is used for transmitting the client management strategy to a preset client information management terminal.
With reference to the second aspect, in a sixth embodiment of the second aspect of the present invention, the analysis unit is specifically configured to: performing spatial point mapping on the client data of the at least one client group to obtain a corresponding spatial point set; performing clustering point analysis on the space point set to determine a target clustering point; and carrying out clustering analysis on the customer data of the at least one customer group through the target clustering point to obtain target clustering data.
According to the technical scheme, historical sales data are obtained, return rate analysis is carried out on the historical sales data, and a target return rate is determined; carrying out intention customer matching according to the target return rate, and determining an intention customer set; generating a client portrait for the set of the intended clients to obtain a client portrait corresponding to each of the intended clients in the set of the intended clients; analyzing the incidence relation of the client figures corresponding to each intention client to determine the target incidence relation among the intention clients; carrying out client classification on a plurality of intention clients through a target association relation among the intention clients to obtain at least one client group; the method comprises the steps of carrying out management strategy generation on at least one client group to obtain a client management strategy, and transmitting the client management strategy to a preset client information management terminal.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a CRM-based customer data analysis method according to an embodiment of the invention;
FIG. 2 is a flow chart of data tag matching and customer matching in an embodiment of the present invention;
FIG. 3 is a flow diagram of preferred feature analysis and portrait generation in an embodiment of the present invention;
FIG. 4 is a flow chart of management policy generation in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a CRM-based customer data analysis system in an embodiment of the invention;
FIG. 6 is a schematic diagram of another embodiment of a CRM-based customer data analysis system in an embodiment of the invention.
Detailed Description
The embodiment of the invention provides a customer data analysis method and system based on CRM (customer relationship management), which are used for improving the accuracy of customer data analysis. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Moreover, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a detailed flow of an embodiment of the present invention is described below, with reference to fig. 1, an embodiment of a CRM-based customer data analysis method in an embodiment of the present invention comprises:
s101, obtaining historical sales data, analyzing the return rate of the historical sales data, and determining a target return rate;
it is understood that the execution subject of the present invention may be a customer data analysis system based on CRM, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
It should be noted that, the Customer Management system (CRM) mainly provides functions such as Management and analysis of Customer basic information, credit analysis and risk monitoring, benefit and business analysis, and personalized service, and can collect sales data generated in a process of a user passing through a preset application program to perform personalized analysis on the user.
The rate of return refers to the economic return obtained by the enterprise from the investment of a capital business activity, such as the ratio between the revenue of the sales and the cost, and in this embodiment, is the rate of return generated by the enterprise during the sales process of the financial product. Specifically, the server determines a target return rate by determining historical sales data of financial products of an enterprise and analyzing the return rate of the historical sales data, specifically by obtaining historical conversion rates corresponding to various purchase data in the historical sales data, wherein the purchase data respectively correspond to a plurality of historical financial products; and determining the corresponding target return rate according to the historical conversion rate of each purchase data, and determining the corresponding target financial product of each purchase data according to the target return rate.
S102, carrying out intention customer matching through the target return rate, and determining an intention customer set, wherein the intention customer set comprises: a plurality of intent customers;
specifically, the server acquires webpage browsing information of a client, wherein the webpage browsing information comprises personal information of the client and user demand information; predicting and matching the webpage browsing information according to a preset matching algorithm to obtain a matching result representing the intention of a client; and traversing the preset financial product information base according to the matching result to acquire the financial product information according with the intention of the customer, taking the customer corresponding to the financial product information according with the intention of the customer as an intention customer, and carrying out set conversion on the intention customers to generate an intention customer set.
S103, generating a client portrait for the intention client set to obtain a client portrait corresponding to each intention client in the intention client set;
specifically, personal information and user demand information corresponding to each intention client in an intention client set and historical trading data corresponding to the intention clients are obtained; wherein, the personal information at least comprises a mobile phone number, age, gender and the like; then, extracting online behavior data corresponding to each intention customer in the intention customer set from the CRM system according to the personal information corresponding to each intention customer and the user demand information; then, according to historical trading data corresponding to the intention customer, determining the line descending corresponding to the intention customer as data; and finally, combining the offline behavior data and the online behavior data corresponding to the intention clients to form client association data, and constructing client figures corresponding to the intention clients to obtain the client figures corresponding to each intention client in the intention client set.
S104, analyzing the incidence relation of the client figures corresponding to each intention client, and determining the target incidence relation among the intention clients;
specifically, the server extracts the associated indexes of the client figures corresponding to each intention client to obtain the associated indexes corresponding to each intention client, then establishes an associated index system, establishes the mutual relation among the associated indexes corresponding to each intention client, gives the quantified value of each associated characteristic, determines the association degree received by the associated indexes corresponding to each intention client according to the quantified value of each associated characteristic, and obtains the index association degree corresponding to each intention client; judging whether the index association degree exceeds a preset target value or not to obtain a judgment result; and constructing an association relation of the association characteristic indexes according to the judgment result to obtain a target association relation among the intention customers.
S105, carrying out client classification on the plurality of intention clients according to the target incidence relation among the intention clients to obtain at least one client group;
specifically, the server creates a client grouping model corresponding to a plurality of intended clients according to the target association relationship among the intended clients. Generating a relationship cross distribution diagram according to a target incidence relationship among a plurality of intention customers; generating a customer population distribution of a plurality of intended customers according to the relationship cross distribution map; and performing client group division on the plurality of intention clients based on the client group distribution to obtain at least one client group. Further, analyzing the characteristic points of the target association relation among the intention customers based on the association model to obtain a plurality of association relation nodes; respectively calculating the distribution weights of the plurality of association relationship nodes to obtain the distribution weight corresponding to each association relationship node; and carrying out client group division on the plurality of intention clients according to the distribution weight corresponding to each associated relationship node to obtain at least one client group.
And S106, generating a management strategy for at least one customer group to obtain a customer management strategy, and transmitting the customer management strategy to a preset customer information management terminal.
Specifically, performing cluster analysis on the customer data of at least one customer group, specifically, inputting the customer data of at least one customer group into a preset cluster model, and performing feature clustering on the customer data of at least one customer group through the cluster model to obtain target cluster data corresponding to the customer data, includes: inputting customer data for at least one customer group into a preset clustering model; clustering the client data of at least one client group through a clustering model to obtain a plurality of characteristic data clusters; and acquiring a clustering center according to the plurality of characteristic data clusters, and generating target clustering data corresponding to the client data according to the clustering center. Then, performing identification matching on the target clustering data through the target clustering data, and determining a strategy identification corresponding to the target clustering data; generating a management strategy through the strategy identification to obtain a customer management strategy; and transmitting the client management strategy to a preset client information management terminal. In addition, in the embodiment, by extracting the clustering center and the spatial point of the customer data; respectively calculating Euclidean distances of the space points and the clustering center to obtain a target Euclidean distance corresponding to each space point; and generating target clustering points according to the target Euclidean distance corresponding to each space point, and performing clustering analysis on the client data of at least one client group through the target clustering points to obtain target clustering data.
In the embodiment of the invention, historical sales data are obtained, and the return rate of the historical sales data is analyzed to determine the target return rate; matching the intention customers according to the target return rate, and determining an intention customer set; generating a client portrait for the set of the intended clients to obtain a client portrait corresponding to each of the intended clients in the set of the intended clients; analyzing the incidence relation of the client figures corresponding to each intention client to determine the target incidence relation among the intention clients; carrying out client classification on a plurality of intention clients through a target association relation among the intention clients to obtain at least one client group; the method comprises the steps of carrying out management strategy generation on at least one client group to obtain a client management strategy, and transmitting the client management strategy to a preset client information management terminal.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Inquiring historical sales data from a preset CRM system, and performing data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data;
(2) Respectively calculating the return rate of each group of sales data to obtain the return rate corresponding to each group of sales data;
(3) And performing return rate fusion on the return rate corresponding to each group of sales data to obtain a target return rate.
Specifically, the server inquires historical sales data from a preset CRM system, performs data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data, divides the historical total sales time into a plurality of time intervals, extracts sales data corresponding to each time interval respectively to obtain a plurality of groups of sales data, utilizes a pre-established return rate analysis model set, and determines weights of a conversion rate model and a return rate calculation model according to each group of sales data, calculates a return rate corresponding to each group of sales data according to the weights, and performs weighted calculation on the return rate corresponding to each group of sales data to obtain normalized return rate fusion data, namely the target return rate.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, performing data label matching on the target return rate to obtain a plurality of data labels;
s202, carrying out customer matching through a plurality of data tags to obtain an intention customer set, wherein the intention customer set comprises: a plurality of intended customers.
Specifically, the server acquires a data tag list, calculates an analysis coefficient of each candidate data tag in the data tag list, determines that the candidate data tag is a data tag required by the embodiment if the analysis coefficient is greater than or equal to a preset analysis coefficient threshold, traverses the data tag list to obtain a plurality of data tags, and sends the plurality of data tags to the cloud data platform; if the analysis coefficient is smaller than a preset analysis coefficient threshold value, determining that the candidate data label is not the data label required by the embodiment, finally matching the plurality of data labels generated after the traversal is finished with the candidate client cluster, taking the client obtained through matching as an intention client to obtain a plurality of intention clients, and generating an intention client set according to the plurality of intention clients.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, performing client data acquisition on each intention client in the intention client set to obtain client association data corresponding to each intention client;
s302, preference characteristic analysis is respectively carried out on the client associated data corresponding to each intention client to obtain preference characteristics corresponding to each intention client;
s303, generating the portrait according to the preference characteristics corresponding to each intention client, and obtaining the client portrait corresponding to each intention client in the intention client set.
Specifically, the server acquires personal information and user demand information corresponding to each intention client in an intention client set and historical transaction data corresponding to the intention clients; the personal information at least comprises a mobile phone number, age, gender and the like; then, extracting online behavior data corresponding to each intention customer in the intention customer set from the CRM system according to the personal information corresponding to each intention customer and the user demand information; then, according to historical trading data corresponding to the intention customer, determining the line descending corresponding to the intention customer as data; finally, combining the online descending data and the online behavior data corresponding to the intention clients to form client association data, and constructing client figures corresponding to the intention clients to obtain the client figures corresponding to each intention client in the intention client set; the method comprises the steps of analyzing preference characteristics of client association data corresponding to each intention client to obtain preference characteristics corresponding to each intention client, specifically, calculating interest vectors of the client association data corresponding to each intention client by a server to obtain interest vectors corresponding to each intention client, calculating distance values of the interest vectors corresponding to each intention client and each standard preference characteristic in a preset database to obtain a plurality of distance values, calculating through a preset cosine similarity calculation formula to obtain the plurality of distance values, and taking the preference corresponding to the standard preference characteristic meeting preset conditions as the preference characteristics corresponding to each intention client according to each distance value after calculating the distance values; specifically, the server performs standard client portrait matching on the preference characteristics corresponding to each intended client to obtain a standard client portrait corresponding to each intended client, uses the matched standard client portrait as the client portrait corresponding to each intended client, and respectively obtains the client portrait corresponding to each intended client in the intended client set.
In a specific embodiment, the process of executing step S104 may specifically include the following steps:
(1) Carrying out correlation index analysis on the client portrait corresponding to each intention client to obtain a corresponding correlation characteristic index;
(2) And constructing an association relation through the association characteristic indexes to obtain a target association relation among the intention customers.
Specifically, the server analyzes the correlation indexes of the client figures corresponding to each intention client to obtain corresponding correlation characteristic indexes, extracts the correlation indexes of the client figures corresponding to each intention client to obtain the correlation indexes corresponding to each intention client, then constructs a correlation index system, constructs the mutual relation between the correlation indexes corresponding to each intention client, gives the quantified value of each correlation characteristic, determines the correlation degree of the correlation indexes corresponding to each intention client according to the quantified value of each correlation characteristic, and obtains the index correlation degree corresponding to each intention client; judging whether the index association degree exceeds a preset target value or not to obtain a judgment result; and if the judgment result is that the target association relationship between the intention clients is exceeded, constructing the association relationship through the association characteristic indexes to obtain the target association relationship between the intention clients. Obtaining a dynamic segmentation result corresponding to the associated characteristic indexes, and constructing a mapping relation between the associated characteristic indexes; and based on each dynamic segmentation result and the mapping relation, respectively carrying out association relation construction on the association characteristic indexes in a preset storage mode and a preset integer list storage mode to obtain a target association relation between the intention customers.
In a specific embodiment, as shown in fig. 4, the process of executing step S105 may specifically include the following steps:
s401, carrying out clustering analysis on the client data of at least one client group to obtain target clustering data;
s402, performing identification matching through the target clustering data, and determining a strategy identification corresponding to the target clustering data;
s403, generating a management strategy through the strategy identification to obtain a customer management strategy;
s404, transmitting the client management strategy to a preset client information management terminal.
Specifically, the server performs clustering analysis on the client data of at least one client group by inputting the client data of at least one client group into a preset clustering model; clustering the client data of at least one client group through a clustering model to obtain a plurality of characteristic data clusters; and acquiring a clustering center according to the plurality of characteristic data clusters, and generating target clustering data corresponding to the client data according to the clustering center. Then, performing identification matching on the target clustering data through the target clustering data, and determining a strategy identification corresponding to the target clustering data; generating a management strategy through the strategy identification to obtain a customer management strategy; and transmitting the client management strategy to a preset client information management terminal. In addition, in the embodiment, the clustering center and the spatial point of the customer data are extracted; respectively calculating Euclidean distances of the space points and the clustering center to obtain a target Euclidean distance corresponding to each space point; and generating target cluster points according to the Euclidean distance of the target corresponding to each space point, and performing cluster analysis on the client data of at least one client group through the target cluster points to obtain target cluster data. And performing identification matching through the target clustering data, determining a strategy identification corresponding to the target clustering data, wherein the target clustering data and the strategy are mapped in advance, the strategy identification is adopted for one-to-one correspondence, the strategy identification is used for performing management strategy generation to obtain a customer management strategy, and finally the customer management strategy is transmitted to a preset customer information management terminal.
In a specific embodiment, the process of executing step S401 may specifically include the following steps:
(1) Performing spatial point mapping on client data of at least one client group to obtain a corresponding spatial point set;
(2) Performing clustering point analysis on the space point set to determine a target clustering point;
(3) And carrying out cluster analysis on the client data of at least one client group through the target cluster point to obtain target cluster data.
Specifically, the server obtains client data of each client group to be clustered, vectorizes data information in the client data of at least one client group to form a vector data set, selects a plurality of vectors from the vector data set to serve as initial clustering centers respectively, clusters the users according to the initial clustering centers to obtain a plurality of characteristic data clusters, updates the clustering centers according to clustered results or clustered results, and continues clustering the users according to the clustering centers to generate target clustering data corresponding to the characteristic data. Specifically, the server extracts a clustering center and a space point of the client data; respectively calculating Euclidean distances of the space points and the clustering center to obtain a target Euclidean distance corresponding to each space point; generating target clustering points according to the target Euclidean distance corresponding to each space point; judging whether the Euclidean distance of the target is greater than the target clustering point; if yes, determining the judgment result as the determined target clustering point. Performing clustering analysis on client data of at least one client group through a target clustering point to obtain target clustering data, wherein the server performs numerical preprocessing on the client data of at least one client group according to the target clustering point to obtain a data sample, performing dimension reduction and feature extraction on the data sample through an automatic encoder, obtaining a clustering result through the data processed by the automatic encoder, calculating the weight of attribute features of the data sample processed by the automatic encoder by adopting a variation coefficient method, calculating the distance between the samples by adopting a weighted Euclidean distance formula, calculating the average distance between all the data samples, traversing the data sample to search each sample point and a neighboring point of which the distance is smaller than the average distance, judging whether the target Euclidean distance is larger than the target clustering point, and if so, outputting the target clustering data.
Referring to fig. 5, the customer data analysis method based on CRM in the embodiment of the present invention is described above, and in the following, a customer data analysis system based on CRM in the embodiment of the present invention is described, and an embodiment of the customer data analysis system based on CRM in the embodiment of the present invention includes:
an obtaining module 501, configured to obtain historical sales data, perform rate of return analysis on the historical sales data, and determine a target rate of return;
a matching module 502, configured to perform intent customer matching according to the target rate of return, and determine a set of intent customers, where the set of intent customers includes: a plurality of intent customers;
a generating module 503, configured to generate a client portrait for the set of intended clients to obtain a client portrait corresponding to each of the intended clients in the set of intended clients;
an analysis module 504 for analyzing the relationship of the client figures corresponding to each intended client to determine the target relationship between the intended clients;
a classification module 505, configured to perform customer classification on the plurality of intended customers according to a target association relationship between the intended customers to obtain at least one customer group;
a generating module 506, configured to generate a management policy for the at least one customer group to obtain a customer management policy, and transmit the customer management policy to a preset customer information management terminal.
Acquiring historical sales data through the cooperative cooperation of the components, and analyzing the return rate of the historical sales data to determine a target return rate; matching the intention customers according to the target return rate, and determining an intention customer set; generating a client portrait for the set of the intended clients to obtain a client portrait corresponding to each of the intended clients in the set of the intended clients; analyzing the incidence relation of the client figures corresponding to each intention client to determine the target incidence relation among the intention clients; carrying out client classification on a plurality of intention clients through a target association relation among the intention clients to obtain at least one client group; the method comprises the steps of carrying out management strategy generation on at least one client group to obtain a client management strategy, and transmitting the client management strategy to a preset client information management terminal.
Referring to FIG. 6, another embodiment of a CRM-based customer data analysis system according to embodiments of the invention includes:
an obtaining module 501, configured to obtain historical sales data, perform rate of return analysis on the historical sales data, and determine a target rate of return;
a matching module 502, configured to perform intent customer matching according to the target rate of return, and determine a set of intent customers, where the set of intent customers includes: a plurality of intent customers;
a generating module 503, configured to generate a client representation for the set of intended clients to obtain a client representation corresponding to each intended client in the set of intended clients;
an analysis module 504, configured to perform association analysis on the client figures corresponding to each intended client, and determine a target association between the intended clients;
a classification module 505, configured to perform customer classification on the plurality of intended customers according to a target association relationship between the intended customers to obtain at least one customer group;
a generating module 506, configured to generate a management policy for the at least one customer group to obtain a customer management policy, and transmit the customer management policy to a preset customer information management terminal.
Optionally, the obtaining module 501 is specifically configured to:
inquiring historical sales data from a preset CRM system, and performing data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data; respectively calculating the return rate of each group of sales data to obtain the return rate corresponding to each group of sales data; and performing rate of return fusion on the rate of return corresponding to each group of sales data to obtain a target rate of return.
Optionally, the matching module 502 is specifically configured to:
performing data label matching on the target return rate to obtain a plurality of data labels; performing customer matching through the plurality of data tags to obtain an intention customer set, wherein the intention customer set comprises: a plurality of intended customers.
Optionally, the generating module 503 is specifically configured to:
performing customer data acquisition on each intention customer in the intention customer set to obtain customer associated data corresponding to each intention customer; respectively carrying out preference characteristic analysis on the client association data corresponding to each intention client to obtain preference characteristics corresponding to each intention client; and generating portrayal through the preference characteristics corresponding to each intention client to obtain the client portrayal corresponding to each intention client in the intention client set.
Optionally, the analysis module 504 is specifically configured to:
analyzing the correlation indexes of the client figures corresponding to each intention client to obtain corresponding correlation characteristic indexes; and constructing an association relation through the association characteristic indexes to obtain a target association relation among the intention customers.
Optionally, the classification module 506 further includes:
the analysis unit 5061 is configured to perform cluster analysis on the customer data of the at least one customer group to obtain target cluster data;
a matching unit 5062, configured to perform identifier matching on the target cluster data, and determine a policy identifier corresponding to the target cluster data;
a generating unit 5063, configured to generate a management policy according to the policy identifier, so as to obtain a customer management policy;
a transmission unit 5064, configured to transmit the customer management policy to a preset customer information management terminal.
Optionally, the analysis unit 5061 is specifically configured to:
performing spatial point mapping on the client data of the at least one client group to obtain a corresponding spatial point set; performing clustering point analysis on the space point set to determine a target clustering point; and carrying out cluster analysis on the customer data of the at least one customer group through the target cluster point to obtain target cluster data.
In the embodiment of the invention, historical sales data are acquired, and the return rate analysis is carried out on the historical sales data to determine the target return rate; carrying out intention customer matching according to the target return rate, and determining an intention customer set; generating a client portrait for the set of the intended clients to obtain a client portrait corresponding to each intended client in the set of the intended clients; analyzing the incidence relation of the client figures corresponding to each intention client to determine the target incidence relation among the intention clients; classifying the plurality of intention clients according to a target association relation among the intention clients to obtain at least one client group; the method comprises the steps of carrying out return rate analysis on historical sales data to further match a plurality of intention clients, then carrying out client group classification according to client figures of the intention clients, and further generating a client management strategy of each client group.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media that can store program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A CRM-based customer data analytics method, comprising:
obtaining historical sales data, and analyzing the return rate of the historical sales data to determine a target return rate;
conducting intention customer matching through the target return rate, and determining an intention customer set, wherein the intention customer set comprises: a plurality of intent customers;
generating client figures of the intention client set to obtain the client figures corresponding to all intention clients in the intention client set;
analyzing the incidence relation of the client figures corresponding to each intention client, and determining the target incidence relation among the intention clients;
carrying out client classification on the plurality of intention clients according to the target association relationship among the intention clients to obtain at least one client group;
and generating a management strategy for the at least one customer group to obtain a customer management strategy, and transmitting the customer management strategy to a preset customer information management terminal.
2. The CRM-based customer data analysis method of claim 1, wherein the obtaining historical sales data and performing rate of return analysis on the historical sales data to determine a target rate of return comprises:
inquiring historical sales data from a preset CRM system, and performing data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data;
respectively calculating the return rate of each group of sales data to obtain the return rate corresponding to each group of sales data;
and performing rate of return fusion on the rate of return corresponding to each group of sales data to obtain a target rate of return.
3. The CRM-based customer data analysis method of claim 1, wherein the intent customer matching is performed via the target rate of return to determine a set of intent customers, wherein the set of intent customers comprises: a plurality of intent customers, comprising:
performing data label matching on the target return rate to obtain a plurality of data labels;
performing customer matching through the plurality of data tags to obtain an intention customer set, wherein the intention customer set comprises: a plurality of intended customers.
4. The CRM-based customer data analysis method of claim 1, wherein the generating a customer representation of the set of intended customers to obtain a customer representation corresponding to each of the set of intended customers comprises:
performing customer data acquisition on each intention customer in the intention customer set to obtain customer associated data corresponding to each intention customer;
carrying out preference characteristic analysis on the client associated data corresponding to each intention client respectively to obtain preference characteristics corresponding to each intention client;
and generating portrayal through the preference characteristics corresponding to each intention client to obtain the client portrayal corresponding to each intention client in the intention client set.
5. The CRM-based customer data analysis method of claim 1, wherein the analyzing the relationship of the customer figures corresponding to each intended customer to determine the target relationship between the intended customers comprises:
carrying out correlation index analysis on the client portrait corresponding to each intention client to obtain a corresponding correlation characteristic index;
and constructing an association relation through the association characteristic indexes to obtain a target association relation among the intention customers.
6. The CRM-based customer data analysis method of claim 1, wherein the generating of the management policy for the at least one customer group to obtain the customer management policy and transmitting the customer management policy to a preset customer information management terminal comprises:
performing clustering analysis on the customer data of the at least one customer group to obtain target clustering data;
performing identification matching through the target clustering data, and determining a strategy identification corresponding to the target clustering data;
generating a management strategy through the strategy identification to obtain a customer management strategy;
and transmitting the client management strategy to a preset client information management terminal.
7. The CRM-based customer data analysis method of claim 6, wherein the performing cluster analysis on the customer data of the at least one customer population to obtain target cluster data comprises:
performing spatial point mapping on the client data of the at least one client group to obtain a corresponding spatial point set;
performing clustering point analysis on the space point set to determine a target clustering point;
and carrying out clustering analysis on the customer data of the at least one customer group through the target clustering point to obtain target clustering data.
8. A CRM-based customer data analysis system, comprising:
the acquisition module is used for acquiring historical sales data, analyzing the return rate of the historical sales data and determining a target return rate;
a matching module, configured to perform intent customer matching according to the target rate of return, and determine an intent customer set, where the intent customer set includes: a plurality of intent customers;
the generation module is used for generating client figures of the intention client set to obtain the client figures corresponding to each intention client in the intention client set;
the analysis module is used for analyzing the incidence relation of the client figures corresponding to each intention client and determining the target incidence relation among the intention clients;
the classification module is used for carrying out client classification on the plurality of intention clients according to the target association relation among the intention clients to obtain at least one client group;
and the generating module is used for generating a management strategy for the at least one customer group to obtain a customer management strategy and transmitting the customer management strategy to a preset customer information management terminal.
9. The CRM-based customer data analysis system of claim 8, wherein the acquisition module is specifically configured to:
inquiring historical sales data from a preset CRM system, and performing data grouping on the historical sales data according to a plurality of preset time intervals to obtain a plurality of groups of sales data;
respectively calculating the return rate of each group of sales data to obtain the return rate corresponding to each group of sales data;
and performing rate of return fusion on the rate of return corresponding to each group of sales data to obtain a target rate of return.
10. The CRM-based customer data analysis system of claim 8, wherein the matching module is specifically configured to:
performing data label matching on the target return rate to obtain a plurality of data labels;
performing customer matching through the plurality of data tags to obtain an intention customer set, wherein the intention customer set comprises: a plurality of intended customers.
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