CN115544310A - Marketing customer group determination method and device, storage medium and electronic equipment - Google Patents

Marketing customer group determination method and device, storage medium and electronic equipment Download PDF

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CN115544310A
CN115544310A CN202211192694.4A CN202211192694A CN115544310A CN 115544310 A CN115544310 A CN 115544310A CN 202211192694 A CN202211192694 A CN 202211192694A CN 115544310 A CN115544310 A CN 115544310A
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enterprise
data
relationship
enterprises
superior
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任天瑜
龙誉
常二莉
金桐�
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
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Abstract

The invention discloses a method and a device for determining a marketing guest group, a storage medium and electronic equipment. Relates to the technical field of financial science and technology, wherein the method comprises the following steps: acquiring superior-inferior relation data of a plurality of enterprises, wherein the superior-inferior relation data is used for representing incidence relations among the plurality of enterprises; establishing an enterprise knowledge graph according to the superior and inferior relation data; constructing an enterprise tree through a preset graph algorithm based on an enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with a preset enterprise; the enterprise tree is divided through a preset division algorithm, and a target marketing customer base is determined, wherein the target marketing customer base comprises a plurality of target enterprises to be marketed. The invention solves the technical problems of low exploration efficiency and poor accuracy of exploring the enterprise to be marketed by exploring the superior-inferior relation of the enterprise through the industrial and commercial registration information of the enterprise in the related technology.

Description

Marketing customer group determination method and device, storage medium and electronic equipment
Technical Field
The invention relates to the technical field of financial science and technology, in particular to a method and a device for determining a marketing guest group, a storage medium and electronic equipment.
Background
At present, in the process of expanding enterprise customers by taking enterprises as marketing customer groups, because the relationship among the enterprises is complicated, particularly for some large group enterprise customers, the hierarchy of lower-level subsidiaries is complicated, the resources are rich, the adherence of the subsidiaries to the group core customers is large, the hierarchy structure of the group enterprise customers is combed, the upper-level and lower-level customer groups of the enterprises with large marketing potential are searched, the advantages of the group customers are promoted, and the influence customer groups with close association relationship are searched.
In the related technology, in the process of exploring the upper and lower levels of enterprise customers, business personnel can only start from external industrial and commercial registration information, and core enterprise customers search subsidiaries step by step; the traditional relational database can only explore the data of lower-level companies step by step from the enterprise customer relational table, and the association among multiple layers of tables influences the query speed, so that the data source in the process of exploring the upper-level and lower-level maps of the enterprises is not systematic, the investigation efficiency is low, automation cannot be realized, complete upper-level and lower-level relational information of group customers cannot be formed quickly, and marketing opportunities are missed.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining a marketing customer group, a storage medium and electronic equipment, which are used for at least solving the technical problems of low exploration efficiency and poor accuracy of exploring an enterprise to be marketed by exploring the superior-inferior relation of the enterprise through the industrial and commercial registration information of the enterprise in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a method for determining a marketing customer group, including: acquiring superior and subordinate relation data of a plurality of enterprises, wherein the superior and subordinate relation data are used for representing incidence relations among the enterprises; establishing an enterprise knowledge graph according to the superior and inferior relation data; constructing an enterprise tree through a preset graph algorithm based on the enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with a preset enterprise; and dividing the enterprise tree through a preset division algorithm, and determining a target marketing customer group, wherein the target marketing customer group comprises a plurality of target enterprises to be subjected to marketing.
Further, the enterprise knowledge-graph includes at least: the method comprises the following steps that relation data among enterprise nodes and enterprise nodes are obtained, the preset graph algorithm is a full-path exploration algorithm with depth-first traversal, and an enterprise tree is constructed through the preset graph algorithm based on the enterprise knowledge graph, and the method comprises the following steps: acquiring a target enterprise node in the enterprise knowledge graph, wherein the target enterprise node is an enterprise node of the predetermined enterprise on the enterprise knowledge graph; and traversing the enterprise knowledge graph by using the target enterprise node as an initial node through the full-path exploration algorithm of depth-first traversal, and constructing the enterprise tree.
Further, the relationship data between the enterprise nodes includes at least: node attribute data and relationship attribute data; the node attribute data includes at least an enterprise identification number; the relationship attribute data is used to represent relationships between the enterprise nodes, the relationship attribute data including at least one of: relationship source, relationship proportion, the relationship proportion is the investment proportion between enterprises.
Further, the preset partition algorithm is a community partition algorithm, and the enterprise tree is partitioned through the preset partition algorithm to determine the target marketing customer base, including: dividing the enterprise tree into a plurality of modules based on the relationship proportions between the enterprise nodes; and carrying out enterprise division on a plurality of enterprises in the plurality of modules through the community division algorithm, and determining the target marketing customer group.
Further, acquiring the superior-inferior relation data of a plurality of enterprises comprises: acquiring enterprise investment data, enterprise shareholder relationship data and fund collection data, wherein the enterprise investment data is investment proportion data of an enterprise investing the enterprise, and the fund collection data is fund allocation data of the enterprise to a subordinate enterprise; and determining superior and subordinate relationship data of the plurality of enterprises based on the enterprise investment data, the enterprise shareholder relationship data and the fund collection data.
Further, the method for constructing the enterprise knowledge graph according to the superior and inferior relation data comprises the following steps: extracting enterprise nodes and relationship data among the enterprise nodes based on the superior and inferior relationship data; and constructing an enterprise knowledge graph through a graph database based on the enterprise nodes and the relationship data among the enterprise nodes.
Further, determining the superior-inferior relationship data of the plurality of enterprises based on the enterprise investment data, the enterprise shareholder relationship data, and the fund aggregation data comprises: determining investment and invested relationships among the plurality of businesses based on the business investment data; determining holding and held relationships between the plurality of businesses based on the business shareholder relationship data; determining a fund allocation relationship between the plurality of enterprises based on the fund collection data; and determining superior and subordinate relationship data of the plurality of enterprises based on the investment and invested relationship, the holdings and held relationships and the fund allocation relationship.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for determining a marketing customer base, including: the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the superior-inferior relation data of a plurality of enterprises, and the superior-inferior relation data is used for representing the incidence relation among the enterprises; the first processing unit is used for constructing an enterprise knowledge graph according to the superior and inferior relation data; the second processing unit is used for constructing an enterprise tree through a preset graph algorithm based on the enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with a preset enterprise; and the determining unit is used for dividing the enterprise tree through a preset division algorithm and determining a target marketing customer base, wherein the target marketing customer base comprises a plurality of target enterprises to be marketed.
Further, the enterprise knowledge-graph includes at least: the second processing unit comprises: a first obtaining subunit, configured to obtain a target enterprise node in the enterprise knowledge graph, where the target enterprise node is an enterprise node of the predetermined enterprise on the enterprise knowledge graph; and the first construction subunit is used for traversing the enterprise knowledge graph by using the target enterprise node as an initial node through the full-path exploration algorithm of depth-first traversal, and constructing the enterprise tree.
Further, the relationship data between the enterprise nodes includes at least: node attribute data and relationship attribute data; the node attribute data includes at least an enterprise identification number; the relationship attribute data is used to represent relationships between the enterprise nodes, the relationship attribute data including at least one of: relationship source, relationship proportion, the relationship proportion is the investment proportion between enterprises.
Further, the preset partition algorithm is a community partition algorithm, and the determining unit includes: a dividing subunit, configured to divide the enterprise tree into a plurality of modules based on the relationship proportion between the enterprise nodes; the first determining subunit is configured to perform enterprise division on the multiple enterprises in the multiple modules through the community division algorithm, and determine the target marketing guest group.
Further, the acquisition unit includes: the second acquisition subunit is used for acquiring enterprise investment data, enterprise shareholder relationship data and fund collection data, wherein the enterprise investment data is investment proportion data of enterprises investing the enterprises, and the fund collection data is fund allocation data of the enterprises to lower-level enterprises; and the second determining subunit is used for determining the superior-inferior relation data of the plurality of enterprises based on the enterprise investment data, the enterprise shareholder relation data and the fund collection data.
Further, the first processing unit includes: the extraction subunit is used for extracting the enterprise nodes and the relationship data among the enterprise nodes based on the superior and inferior relationship data; and the second construction subunit is used for constructing an enterprise knowledge graph through a graph database based on the enterprise nodes and the relationship data among the enterprise nodes.
Further, the second determining subunit includes: a first determination module for determining an investment-to-invested relationship among the plurality of businesses based on the business investment data; a second determination module for determining holding and held relationships between the plurality of enterprises based on the enterprise shareholder relationship data; a third determination module, configured to determine a fund allocation relationship between the plurality of enterprises based on the fund collection data; and the fourth determining module is used for determining the superior and inferior relation data of the plurality of enterprises based on the investment and invested relation, the holding and held relation and the fund allocation relation.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to execute the method for determining a marketing customer base of any one of the above items via executing the executable instructions.
According to another aspect of the embodiments of the present invention, there is further provided a computer-readable storage medium, in which a computer program is stored, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above methods for determining a marketing customer base.
In the invention, the superior-inferior relation data of a plurality of enterprises is obtained, wherein the superior-inferior relation data is used for representing the incidence relation among the plurality of enterprises; establishing an enterprise knowledge graph according to the superior and inferior relation data; constructing an enterprise tree through a preset graph algorithm based on an enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with a preset enterprise; the enterprise tree is divided through a preset division algorithm, and a target marketing customer base is determined, wherein the target marketing customer base comprises a plurality of target enterprises to be marketed. And the technical problems of low exploration efficiency and poor accuracy of the enterprise to be marketed determined by exploring the superior-inferior relation of the enterprise through the industrial and commercial registration information of the enterprise in the related technology are solved. According to the method and the device, the enterprise knowledge graph is constructed based on the superior-inferior relation data of a plurality of enterprises, the enterprise tree is further constructed, the enterprise tree is divided through the preset division algorithm, the target marketing customer group is determined, the condition that the superior-inferior relation of the enterprises is explored by the business registration information in the related technology, the enterprise to be marketed is determined is avoided, and the technical effect of improving the marketing customer group determination efficiency is achieved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of an alternative marketing customer base determination method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another alternative marketing customer base determination method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an alternative enterprise knowledge-graph in accordance with embodiments of the present invention;
FIG. 4 is a flow chart of an alternative method of enterprise tree and marketing customer base extraction according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an alternative marketing customer base determining apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "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.
It should be noted that the method and the device for determining the marketing customer base in the present disclosure may be used in the financial technology field for determining the marketing customer base on the context of an enterprise, and may also be used in any field other than the financial technology field for determining the marketing based on the context of an enterprise, and the application fields of the method and the device for determining the marketing customer base in the present disclosure are not limited.
It should be noted that relevant information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for presentation, analyzed data, etc.) referred to in the present disclosure are information and data that are authorized by the user or sufficiently authorized by various parties. For example, an interface is provided between the system and the relevant user or organization, before obtaining the relevant information, an obtaining request needs to be sent to the user or organization through the interface, and after receiving the consent information fed back by the user or organization, the relevant information is obtained.
The invention will now be further illustrated with reference to the following examples.
Example one
In accordance with an embodiment of the present invention, there is provided an alternative method embodiment of determining a marketing customer base, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a flowchart of an alternative marketing customer base determination method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S101, superior and inferior relation data of a plurality of enterprises are obtained, wherein the superior and inferior relation data are used for representing incidence relations among the plurality of enterprises.
The above-mentioned superior-inferior relationship data of a plurality of enterprises can be used for characterizing the relationship between a plurality of enterprises, for example, the relationship between investment and invested investment, between holding stock and held stock, between headquarters of an enterprise and branch enterprises, etc.
And S102, constructing an enterprise knowledge graph according to the superior and inferior relation data.
According to the superior-inferior relation data among a plurality of enterprises, the enterprise knowledge maps of the plurality of enterprises can be constructed.
And S103, constructing an enterprise tree through a preset graph algorithm based on the enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with the preset enterprise.
The predetermined enterprise may be an enterprise provided by a service provider initiating a marketing service, or an enterprise designated by a user, and an enterprise tree formed by each enterprise related to the predetermined enterprise may be determined through a preset graph algorithm (for example, a deep traversal-first path exploration technique) based on enterprise knowledge maps of a plurality of enterprises. The top-most business of the enterprise tree may be a predetermined business.
And step S104, dividing the enterprise tree through a preset division algorithm, and determining a target marketing customer group, wherein the target marketing customer group comprises a plurality of target enterprises to be marketed.
The target marketing customer group comprises a plurality of target enterprises to be marketed, and the superior-inferior relations of the target enterprises in the target marketing customer group are closely connected, for example: the investment ratio of the enterprise to the enterprise is larger than the preset ratio. The enterprise tree can be divided through a preset division algorithm, and a target marketing customer group with a close superior-inferior relation is obtained.
Through the steps, the enterprise knowledge graph is constructed based on the superior-inferior relation data of a plurality of enterprises, then the enterprise tree is constructed, the enterprise tree is divided through the preset division algorithm, the target marketing customer base is determined, the condition that the superior-inferior relation of the enterprises is explored by the industrial and commercial registration information in the related technology, the condition of the enterprise to be marketed is determined is avoided, and the technical effect of improving the efficiency of determining the marketing customer base is achieved. And the technical problems of low exploration efficiency and poor accuracy of the enterprise to be marketed determined by exploring the superior-inferior relation of the enterprise through the industrial and commercial registration information of the enterprise in the related technology are solved.
In order to avoid the incomplete condition of the enterprise tree determined according to the business registration information, in this embodiment, the enterprise knowledge graph at least includes: the method comprises the following steps that relational data among enterprise nodes and the enterprise nodes are obtained, a preset graph algorithm is a full-path exploration algorithm with depth-first traversal, an enterprise tree is constructed through the preset graph algorithm based on an enterprise knowledge graph, and the method further comprises the following contents: acquiring a target enterprise node in an enterprise knowledge graph, wherein the target enterprise node is an enterprise node of a preset enterprise on the enterprise knowledge graph; and traversing the enterprise knowledge graph by using the target enterprise node as an initial node through a full-path exploration algorithm of depth-first traversal, and constructing an enterprise tree.
In this embodiment, based on a full-path exploration technique of depth-first traversal (corresponding to the full-path exploration algorithm of depth-first traversal described above), a client tree of each group client (each enterprise associated with a predetermined enterprise) is generated, a subsidiary (i.e., the next-level enterprise) at each level is clearly displayed, and a configurable path expansion process (preset graph algorithm) is used to set a terminating enterprise node as an enterprise node without an OUT relationship (without the next-level enterprise) from a core client (corresponding to the predetermined enterprise), so that the purpose that the client tree (corresponding to the enterprise tree) of the group client (each enterprise associated with the predetermined enterprise) is complete and accurate by using the full-path exploration technique is achieved, and the technical effect of constructing a complete enterprise tree associated with the predetermined enterprise is achieved.
In order to avoid the situation that the relationship data between multiple enterprises is not accurate, in this embodiment, the relationship data between the enterprise nodes at least includes: node attribute data and relationship attribute data; the node attribute data includes at least an enterprise identification number; the relationship attribute data is used for representing relationships between the enterprise nodes, and the relationship attribute data at least comprises one of the following: relationship source, relationship proportion is the investment proportion between enterprises.
In this embodiment, the relationship data between the enterprise nodes may include at least: the node attribute data can contain enterprise unique identification numbers (such as business registration numbers), namely enterprise identification numbers, client numbers in organizations, enterprise types, social uniform credit codes, industry categories and the like. The relationship attribute data can be a superior-inferior relationship, and the relationship attribute data can contain a relationship source (such as enterprise external investment, enterprise shareholder relationship, enterprise institution fund collection data) and a relationship proportion (a stock holding proportion or shareholder funding proportion), so that the technical effects of accurate and complete relationship data and convenience in analysis by utilizing the superior-inferior relationship of the enterprise are achieved.
In order to avoid the situation that the relationship between enterprises in the target marketing objective group is not tight, in this embodiment, the preset partitioning algorithm is a community partitioning algorithm, and the enterprise tree is partitioned by the preset partitioning algorithm to determine the target marketing objective group, which further includes the following contents: dividing the enterprise tree into a plurality of modules based on the relation proportion among the enterprise nodes; and carrying out enterprise division on a plurality of enterprises in the modules through a community division algorithm to determine a target marketing customer group.
In this embodiment, according to the relationship proportion in the relationship attribute data, a graph algorithm based on a community division algorithm identifies a tight marketing customer group (corresponding to the target marketing customer group), the community division algorithm may use a luwen algorithm (Louvain algorithm), the luwen algorithm is an algorithm based on modularity, a network with high modularity has dense connections between nodes in modules, but sparse connections between nodes in different modules are provided, so as to realize division of a tighter marketing customer group in the enterprise tree, and realize exploration of the marketing customer group based on the upper-lower relationship of the enterprise, so as to obtain the target marketing customer group, where the computation formula of the luwen algorithm is as follows:
Figure BDA0003870109800000071
wherein M represents the maximum value of the degree of closeness of marketing objective group division, namely the convergence value of the closeness of marketing objective group division, L represents the total number of all relations in the enterprise knowledge graph, and L represents the total number of all relations in the enterprise knowledge graph c Represents the total number of relationships, k, in module c c Represents the sum of degrees of all nodes in module c and n represents the total module number. And (3) calculating: firstly, rapidly allocating an enterprise to a certain community or a certain module, and secondly, obtaining a coarse-grained network based on a marketing guest group found in the first step, repeating the two steps until the community of the enterprise cannot be reallocated, and then further increasing the modularity (maximization) of the community, wherein it needs to be noted that the division of the initial module can firstly divide the enterprise tree into a plurality of modules based on the relation proportion among enterprise nodes, for example: the enterprises with the relation proportion value between the enterprises being larger than the preset relation proportion threshold value are divided into one module, then the enterprises in the modules are divided based on the community division algorithm, the target marketing customer group with the close relation is obtained, and the technical effect of accurately obtaining the target marketing customer group with the close relation between the enterprises is achieved.
In order to avoid the inaccurate condition of screening the superior-inferior relation data of the enterprises by means of the enterprise business registration information, in this embodiment, the superior-inferior relation data of a plurality of enterprises is obtained, which further includes the following contents: acquiring enterprise investment data, enterprise shareholder relationship data and fund collection data, wherein the enterprise investment data is investment proportion data of an enterprise investing the enterprise, and the fund collection data is fund allocation data of the enterprise to a lower-level enterprise; and determining the superior-inferior relation data of the plurality of enterprises based on the enterprise investment data, the enterprise shareholder relation data and the fund collection data.
In this embodiment of the present application, data that a plurality of enterprise organizations have obvious superior and inferior relationships may be collected, and may include: the enterprise investment data, the enterprise shareholder relationship data and the fund collection data can be divided into external data and internal data, the external data comprises inter-enterprise external investment data (namely enterprise investment data) and enterprise shareholder relationship data, the internal data refers to fund collection product data in an enterprise group, namely fund allocation data of an enterprise to a subordinate enterprise, and the technical effect of improving the accuracy of the superior and subordinate relationship data of a plurality of enterprises is achieved.
In order to avoid the situation that the information of the enterprise tree is incomplete when the superior-inferior relation of the enterprise is determined directly according to the enterprise business registration information, in this embodiment, the enterprise knowledge graph is constructed according to the superior-inferior relation data, and the method further includes the following contents: extracting enterprise nodes and relationship data among the enterprise nodes based on the superior and inferior relationship data; and constructing an enterprise knowledge graph through the graph database based on the enterprise nodes and the relation data among the enterprise nodes.
In this embodiment, the enterprise may be used as a node according to the superior-inferior relationship data between the enterprises, the superior-inferior relationship between the enterprises is used as a relationship between the nodes, the superior-inferior relationship data between the enterprises is extracted as relationship data between the enterprise nodes, and the enterprise knowledge map may be constructed based on the database by storing the relationship data between the enterprise nodes and the enterprise nodes in the database. It should be noted that the enterprise nodes can also be divided into two categories, namely, clients of the target financial institution and clients outside the target financial institution, wherein the target financial institution can be a main body for marketing, and the marketing client group is also a marketing client group of the target financial institution.
In order to avoid the inaccurate superior-inferior relationship among multiple enterprises, in this embodiment, the superior-inferior relationship data of multiple enterprises is determined based on the enterprise investment data, the enterprise shareholder relationship data, and the fund collection data, and further includes the following contents: determining investment and invested relationships among a plurality of enterprises based on enterprise investment data; determining holding and held relationship among a plurality of enterprises based on enterprise shareholder relationship data; determining a fund allocation relationship between a plurality of enterprises based on the fund collection data; and determining superior and subordinate relationship data of the plurality of enterprises based on the relationship between investment and invested, the relationship between holdings and the relationship between capital allotment.
In this embodiment, in the enterprise investment data, the enterprise shareholder relationship data, and the fund collection data, the data index uniquely marking the enterprise may be the business registration number of the enterprise, and first, the upper-level enterprise of each invested enterprise is determined according to the size of the investment proportion of the enterprise to the external investment; because most stockholders of the enterprise are higher-level units of the investment enterprise, the superposed part of the relationship between the enterprise and the superordinate enterprise determined by the external investment of the enterprise can be removed, namely the superposed superordinate and subordinate relationship between the stockholder relationship data of the enterprise and the investment data of the enterprise is removed, and the superordinate enterprise of each held enterprise is determined on the basis of the size of the stock holding ratio; and finally, according to the product data of the fund collection in the enterprise institution, adding the enterprise investment data and the superior and subordinate relationship data determined by the enterprise shareholder relationship data. Therefore, the superior-inferior relation data among the enterprises can be determined, the superior-inferior relation data of the enterprises is determined based on the enterprise investment data, the enterprise shareholder relation data and the fund collection data, and the technical effect of improving the integrity and the accuracy of the superior-inferior relation data of the enterprises is achieved.
Example two
The embodiment provides another optional method for determining a marketing customer base, which comprises the following steps:
(1) And integrating the superior-subordinate relationship among a plurality of enterprises from the superior-subordinate information such as the industrial and commercial registration information (enterprise external investment), the inter-enterprise stockholder relationship information, the fund collection product information and the like in the data warehouse to construct an enterprise knowledge graph of the superior-subordinate relationship among the enterprises.
(2) Vertex customers (corresponding to the predetermined enterprises in the first embodiment) are extracted from the enterprise customer base, and the hierarchical structure of each vertex customer is searched by a path exploration technology (graph algorithm) based on depth traversal priority, so as to form an enterprise tree of the preliminary vertex customers.
(3) According to the community division algorithm, the enterprise trees of the top clients are grouped based on the proportional coefficient of the superior-inferior relation, and the superior-inferior marketing client group (corresponding to the target marketing client group in the first embodiment) with close relation is divided in the enterprise trees.
FIG. 2 is a flow chart of another alternative marketing customer base determination method according to an embodiment of the present invention; as shown in figure 2 of the drawings, in which,
step S201: and collecting the superior and inferior relation data of the enterprises from different sources, and determining the unique superior and inferior relation among the enterprises.
In the embodiment of the application, data with obvious superior-subordinate relationship between the inside and the outside of the organization is collected, and mainly comprises external data and internal data, wherein the external data comprises inter-enterprise external investment data (corresponding to the enterprise investment data in the first embodiment) and enterprise shareholder relationship data, and the internal data refers to fund collection product data (corresponding to the fund collection data in the first embodiment) in the enterprise organization (for example, fund allocation data of a head office to a subsidiary company).
In the external investment data, the enterprise shareholder relationship data and the fund collection product data among the enterprises, the only marked enterprise data indexes are all the industrial and commercial registration numbers (corresponding to the enterprise identification numbers in the first embodiment) of the enterprises, and the upper-level enterprises of each invested enterprise are determined according to the external investment proportion of the enterprises; because most stockholders of the enterprise are higher-level units of the investment enterprise, the superposed part of the relationship between the enterprise and the superordinate enterprise determined by the external investment of the enterprise can be removed, namely the superposed superordinate and subordinate relationship between the stockholder relationship data of the enterprise and the investment data of the enterprise is removed, and the superordinate enterprise of each held enterprise is determined on the basis of the size of the stock holding ratio; and finally, according to the product data of the fund collection in the enterprise institution, adding the enterprise investment data and the superior and subordinate relationship data determined by the enterprise shareholder relationship data. Therefore, the superior-inferior relation data among a plurality of enterprises can be determined, and the superior-inferior relation data of the plurality of enterprises can be determined based on the enterprise investment data, the enterprise shareholder relation data and the fund collection data.
Step S202: and forming nodes and relationship data of the knowledge graph among the enterprises aiming at the determined superior and inferior relationship data sources among the enterprises, and constructing the enterprise knowledge graph.
In the embodiment of the application, the superior-inferior relation data among enterprises is extracted into the relation data among nodes, and the enterprise knowledge graph is constructed based on the graph database. The enterprise nodes on the enterprise knowledge graph can be further divided into two categories, namely, a client in the current row (a client of a target financial institution) and a client in the other row (a client of other financial institution), and the node attribute data of the enterprise nodes on the enterprise knowledge graph comprises an enterprise unique identification number (a business registration number, which corresponds to the enterprise identification number in the first embodiment), an enterprise client number, an enterprise type, a social unified credit code, an industry category and the like. The relationship attribute data can be a superior-inferior relationship, and the relationship attribute data comprises a relationship source (enterprise external investment, enterprise shareholder relationship, and mechanism fund collection data) and a relationship proportion (stock holding proportion or shareholder funding proportion).
Step S203: based on the knowledge graph, a group customer tree is constructed through graph traversal and graph algorithm, and a close marketing customer group (corresponding to a target marketing customer group in the first embodiment) in each tree is explored.
In the embodiment of the present application, a full-path exploration technique based on depth-first traversal generates a client tree (corresponding to the enterprise tree in the first embodiment) for each group client, clearly shows a sub-company at each level in the enterprise tree, and sets a termination node list as an enterprise node without OUT relationship (without lower nodes) starting from a core client (i.e., a vertex client, corresponding to a predetermined enterprise in the first embodiment) by using a configurable path expansion process (graph algorithm), so as to achieve the purpose of ensuring the integrity and accuracy of the enterprise tree of the vertex client and no omission of the sub-company by using the full-path exploration technique.
According to the relation proportion in the relation attribute data, a graph algorithm based on a community division algorithm is used for identifying the tight marketing customer groups in each enterprise tree, the community division algorithm can use a Luwen algorithm (Louvain algorithm), the Luwen algorithm is an algorithm based on modularity, a network with high modularity has dense connection among nodes in the modules, but sparse connection is provided among nodes in different modules, so that the tight marketing customer group division in the same enterprise tree is realized, and exploration of upper and lower marketing customer groups of an enterprise is realized.
Figure BDA0003870109800000111
Wherein M represents the maximum value of the compactness degree of the marketing guest group division, namely the convergence value of the compactness marketing guest group division, L represents the total number of all the relations in the enterprise knowledge graph, and L represents the total number of all the relations in the enterprise knowledge graph c Represents the total number of relationships, k, in module c c Representing the sum of the degrees of all nodes in module c. The calculation process includes that firstly, the enterprise is quickly allocated to a certain community (or a certain module) in the first step, secondly, a coarse-grained network is obtained based on the marketing customer group found in the first step, and the two steps are repeated until the community (or the module) of the enterprise cannot be reallocated, and then the modularity (maximization) of the community cannot be further increased, it should be noted that the division of the initial module can firstly divide the enterprise tree into a plurality of modules based on the relation proportion among the enterprise nodes, for example: the method comprises the steps that enterprises with relationship proportion values among the enterprises larger than a preset relationship proportion threshold value are divided into one module, then the enterprises in the modules are divided based on a community division algorithm, and a target marketing customer group with close relationship is obtained. An alternative schematic diagram of an enterprise knowledge graph is shown in fig. 3, wherein A, A, A2, a11, a12, a13 in fig. 3 represent the number of an enterprise.
Fig. 4 is a flowchart of an alternative method for extracting an enterprise tree and a marketing customer base according to an embodiment of the present invention, as shown in fig. 4, including steps 401-403. Step 401: and defining a vertex customer list, and selecting an enterprise of which the enterprise property is a target enterprise property in the enterprise business registration information as a vertex customer, wherein the target enterprise property can be a nationalized enterprise. Step 402; according to a full-path exploration technology based on depth-first traversal, a vertex client is used as a core, all nodes in a traversal path are explored, and an enterprise tree is formed. Step 403: the tightly connected businesses that define the business tree form a marketing inventory.
In the embodiment, the enterprise tree of the preset enterprise is systematically constructed through the knowledge map technology, and the clustering marketing strategy among the upper and lower-level enterprises is realized through the graph algorithm of the community division algorithm, so that the completeness of the upper and lower-level relation data of the enterprise and the marketing accuracy are improved.
The strategy of the superior and inferior enterprise marketing customer base provided by the embodiment provides an interpretable marketing customer base mechanism by associating the superior and inferior relations among enterprises to enterprise clustering marketing, and ensures that a target financial institution determines a complete marketing customer base with a close association relation with a vertex customer.
EXAMPLE III
The embodiment provides a selectable marketing customer group determining device, and each implementation unit in the determining device corresponds to each implementation step in the first embodiment.
Fig. 5 is a schematic diagram of an alternative marketing customer base determining apparatus according to an embodiment of the present invention, as shown in fig. 5, including: an acquisition unit 51, a first processing unit 52, a second processing unit 53, and a determination unit 54.
Specifically, the obtaining unit 51 is configured to obtain superior-inferior relation data of multiple enterprises, where the superior-inferior relation data is used to represent an association relation between the multiple enterprises;
the first processing unit 52 is configured to construct an enterprise knowledge graph according to the superior-inferior relation data;
a second processing unit 53, configured to construct an enterprise tree through a preset graph algorithm based on the enterprise knowledge graph, where the enterprise tree is used to represent association relationship information of each enterprise associated with a predetermined enterprise;
the determining unit 54 is configured to divide the enterprise tree by using a preset division algorithm, and determine a target marketing customer base, where the target marketing customer base includes a plurality of target enterprises to be marketed.
In the apparatus for determining a marketing customer group provided in the third embodiment of the present application, the obtaining unit 51 may obtain superior-inferior relation data of a plurality of enterprises, where the superior-inferior relation data is used to represent an association relationship between the plurality of enterprises, the first processing unit 52 constructs an enterprise knowledge graph according to the superior-inferior relation data, the second processing unit 53 constructs an enterprise tree based on the enterprise knowledge graph and through a preset graph algorithm, where the enterprise tree is used to represent association relationship information of each enterprise associated with a predetermined enterprise, and the determining unit 54 divides the enterprise tree through a preset division algorithm to determine a target marketing customer group, where the target marketing customer group includes a plurality of target enterprises to be marketed. And the technical problems of low exploration efficiency and poor accuracy of the enterprise to be marketed determined by exploring the superior-inferior relation of the enterprise through the industrial and commercial registration information of the enterprise in the related technology are solved. In the embodiment, based on the superior-inferior relation data of a plurality of enterprises, the enterprise knowledge map is constructed, the enterprise tree is further constructed, the enterprise tree is divided through the preset division algorithm, the target marketing customer group is determined, the condition that the superior-inferior relation of the enterprises is explored by the business registration information in the related technology, the condition of the enterprise to be marketed is determined, and the technical effect of improving the marketing customer group determination efficiency is achieved.
Optionally, in the apparatus for determining a marketing customer base provided in the third embodiment of the present application, the enterprise knowledge graph at least includes: the second processing unit 53 includes: the system comprises a first acquisition subunit, a second acquisition subunit and a third acquisition subunit, wherein the first acquisition subunit is used for acquiring a target enterprise node in an enterprise knowledge graph, and the target enterprise node is an enterprise node of a preset enterprise on the enterprise knowledge graph; and the first construction subunit is used for traversing the enterprise knowledge graph by using the target enterprise node as an initial node through a full-path exploration algorithm with depth-first traversal, and constructing an enterprise tree.
Optionally, in the apparatus for determining a marketing customer group provided in the third embodiment of the present application, the relationship data between the enterprise nodes at least includes: node attribute data and relationship attribute data; the node attribute data includes at least an enterprise identification number; the relationship attribute data is used for representing relationships between the enterprise nodes, and the relationship attribute data at least comprises one of the following: relationship source, relationship proportion is the investment proportion between enterprises.
Optionally, in the apparatus for determining a marketing customer group provided in the third embodiment of the present application, the preset partition algorithm is a community partition algorithm, and the determining unit 54 includes: the enterprise tree planning unit is used for dividing the enterprise tree into a plurality of modules based on the relation proportion among the enterprise nodes; and the first determining subunit is used for carrying out enterprise division on a plurality of enterprises in the modules through a community division algorithm to determine the target marketing customer group.
Optionally, in the device for determining a marketing customer group provided in the third embodiment of the present application, the obtaining unit 51 includes: the second acquisition subunit is used for acquiring enterprise investment data, enterprise shareholder relationship data and fund collection data, wherein the enterprise investment data is investment proportion data of enterprises investing the enterprises, and the fund collection data is fund allocation data of the enterprises to lower-level enterprises; and the second determining subunit is used for determining the superior-inferior relation data of the plurality of enterprises based on the enterprise investment data, the enterprise shareholder relation data and the fund collection data.
Optionally, in the device for determining a marketing customer group provided in the third embodiment of the present application, the first processing unit 52 includes: the extraction subunit is used for extracting the enterprise nodes and the relationship data among the enterprise nodes based on the superior and inferior relationship data; and the second construction subunit is used for constructing the enterprise knowledge graph through the graph database based on the enterprise nodes and the relation data among the enterprise nodes.
Optionally, in the apparatus for determining a marketing customer group provided in the third embodiment of the present application, the second determining subunit includes: a first determining module for determining an investment and invested relationship among a plurality of enterprises based on enterprise investment data; the second determination module is used for determining holding and held relations among the plurality of enterprises based on the enterprise shareholder relation data; a third determining module, configured to determine a fund allocation relationship between the plurality of enterprises based on the fund collection data; and the fourth determining module is used for determining the superior and subordinate relationship data of the plurality of enterprises based on the relationship between investment and invested stock, the relationship between held stock and the fund allocation relationship.
The aforementioned determination device for the marketing client group may further include a processor and a memory, where the aforementioned acquiring unit 51, the first processing unit 52, the second processing unit 53, the determination unit 54, and the like are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to implement corresponding functions.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can be set to be one or more than one, the enterprise knowledge graph is constructed based on the superior-inferior relation data of a plurality of enterprises by adjusting the kernel parameters, then the enterprise tree is constructed, the enterprise tree is divided through a preset division algorithm, the target marketing customer group is determined, the condition that the superior-inferior relation of the enterprises is explored by the worker registration information in the related technology is avoided, the condition of the enterprises to be marketed is determined, and the technical effect of improving the efficiency of determining the marketing customer group is achieved.
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), and the memory includes at least one memory chip.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to execute the method for determining a marketing customer base of any one of the above items via executing the executable instructions.
According to another aspect of the embodiments of the present invention, there is further provided a computer-readable storage medium, in which a computer program is stored, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above methods for determining a marketing customer base.
Fig. 6 is a schematic diagram of an alternative electronic device according to an embodiment of the present invention, and as shown in fig. 6, an embodiment of the present invention provides an electronic device 60, which includes a processor, a memory, and a program stored in the memory and running on the processor, and when the processor executes the program, the processor implements the method for determining a marketing object group according to any one of the above items.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
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: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A method for determining a marketing customer base, comprising:
acquiring superior and subordinate relation data of a plurality of enterprises, wherein the superior and subordinate relation data are used for representing incidence relations among the enterprises;
establishing an enterprise knowledge graph according to the superior and inferior relation data;
constructing an enterprise tree through a preset graph algorithm based on the enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with a preset enterprise;
and dividing the enterprise tree through a preset division algorithm, and determining a target marketing customer group, wherein the target marketing customer group comprises a plurality of target enterprises to be subjected to marketing.
2. The method of determining according to claim 1, wherein the enterprise knowledge-graph comprises at least: the method comprises the following steps that relation data among enterprise nodes and enterprise nodes are obtained, the preset graph algorithm is a full-path exploration algorithm with depth-first traversal, and an enterprise tree is constructed through the preset graph algorithm based on the enterprise knowledge graph, and the method comprises the following steps:
acquiring a target enterprise node in the enterprise knowledge graph, wherein the target enterprise node is an enterprise node of the predetermined enterprise on the enterprise knowledge graph;
and traversing the enterprise knowledge graph by using the target enterprise node as an initial node through the full-path exploration algorithm of depth-first traversal, and constructing the enterprise tree.
3. The method of claim 2, wherein the relationship data between the enterprise nodes comprises at least: node attribute data and relationship attribute data;
the node attribute data includes at least an enterprise identification number;
the relationship attribute data is used to represent relationships between the enterprise nodes, the relationship attribute data including at least one of: relationship source, relationship proportion, the relationship proportion is the investment proportion between enterprises.
4. The method of claim 3, wherein the preset partition algorithm is a community partition algorithm, and the step of partitioning the enterprise tree by the preset partition algorithm to determine the target marketing objective group comprises:
dividing the enterprise tree into a plurality of modules based on the relationship proportions between the enterprise nodes;
and carrying out enterprise division on a plurality of enterprises in the modules through the community division algorithm, and determining the target marketing guest group.
5. The method of claim 1, wherein obtaining contextual data for a plurality of enterprises comprises:
acquiring enterprise investment data, enterprise shareholder relationship data and fund collection data, wherein the enterprise investment data is investment proportion data of an enterprise investing the enterprise, and the fund collection data is fund allocation data of the enterprise to a subordinate enterprise;
and determining superior and subordinate relationship data of the plurality of enterprises based on the enterprise investment data, the enterprise shareholder relationship data and the fund collection data.
6. The method of claim 3, wherein constructing an enterprise knowledge graph from the context data comprises:
extracting enterprise nodes and relationship data among the enterprise nodes based on the superior and inferior relationship data;
and constructing an enterprise knowledge graph through a graph database based on the enterprise nodes and the relationship data among the enterprise nodes.
7. The method of claim 5, wherein determining the superior-inferior relationship data for the plurality of businesses based on the business investment data, the business stakeholder relationship data, the fund aggregation data, comprises:
determining investment and invested relationships among the plurality of businesses based on the business investment data;
determining holding and held relationships between the plurality of businesses based on the business shareholder relationship data;
determining a fund allocation relationship between the plurality of enterprises based on the fund collection data;
and determining superior and subordinate relationship data of the plurality of enterprises based on the investment and invested relationship, the holdings and held relationships and the fund allocation relationship.
8. An apparatus for determining a marketing customer base, comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the superior-inferior relation data of a plurality of enterprises, and the superior-inferior relation data is used for representing the incidence relation among the enterprises;
the first processing unit is used for constructing an enterprise knowledge graph according to the superior and inferior relation data;
the second processing unit is used for constructing an enterprise tree through a preset graph algorithm based on the enterprise knowledge graph, wherein the enterprise tree is used for representing incidence relation information of each enterprise associated with a preset enterprise;
and the determining unit is used for dividing the enterprise tree through a preset division algorithm and determining a target marketing customer base, wherein the target marketing customer base comprises a plurality of target enterprises to be marketed.
9. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program runs, the computer-readable storage medium is controlled by a device to execute the method for determining a marketing customer base according to any one of claims 1 to 7.
10. An electronic device comprising one or more processors and memory storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of determining a marketing objective group of any of claims 1-7.
CN202211192694.4A 2022-09-28 2022-09-28 Marketing customer group determination method and device, storage medium and electronic equipment Pending CN115544310A (en)

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