CN115587739A - Client list distribution method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the fields of artificial intelligence and financial science and technology, is applied to the field of client allocation for business personnel, and relates to a client name list allocation method, a client name list allocation device, computer equipment and a storage medium.
Description
Technical Field
The present application relates to the field of big data and financial technology, and in particular, to a client ticket distribution method, apparatus, computer device, and storage medium.
Background
In a large market environment, how to keep the business profit of a company maximized remains a common concern for all companies. Unlike general company business, taking insurance company as an example, insurance business relies more on manual invoicing, i.e. the sales level of the business staff and the quality of the customers determine the success or failure of the policy to a great extent. In such a context, insurance companies need to have customer names contacted by more appropriate business personnel to ensure a higher completion rate.
Taking insurance business as an example, the types of insurance business mainly include business insurance business, strong insurance business, health insurance business, unexpected insurance business and the like, and how to contact client names by more suitable business staff ensures higher completion rate of different business types becomes a technical problem which needs to be solved urgently in the current insurance industry.
Disclosure of Invention
The embodiment of the application aims to provide a client list distribution method, a client list distribution device, computer equipment and a storage medium, so that client lists are contacted by more suitable service personnel, and higher completion rates of different service types are guaranteed.
In order to solve the above technical problem, an embodiment of the present application provides a client ticket assignment method, which adopts the following technical solutions:
a client ticket assignment method comprising the steps of:
reading a service database, and searching historical transaction data of different service types of companies in the service database according to service identification;
analyzing the historical transaction data, and extracting client information and salesman information from the analysis content corresponding to the historical transaction data;
constructing a bipartite graph according to the customer information and the salesman information;
constructing a business type single matrix according to the bipartite graph;
a single matrix is generated based on the service types and a preset prediction iterative algorithm, and a client list distribution model is constructed;
receiving a client list distribution request, analyzing the distribution request, and acquiring request content, wherein the request content comprises a service type corresponding to a client to be recommended and distributed and client information in a client list;
and obtaining a customer recommendation distribution result based on the service type, the customer information in the customer list and the customer list distribution model.
Further, the customer information includes a customer number, the staff information includes a corresponding staff number when the business forms a delivery order, and the step of extracting the customer information and the staff information from the analysis content corresponding to the historical delivery data specifically includes:
traversing the analysis content to obtain a traversal result, and acquiring all client numbers in the analysis content according to the traversal result;
and taking the client number as a retrieval field, taking preset different service types as a restriction field, and searching for the corresponding operator numbers of all clients in the analysis content when the clients form a delivery list based on the different service types.
Further, the step of constructing a bipartite graph according to the customer information and the salesman information specifically includes:
taking all the obtained client numbers as elements in a first subset to construct the first subset;
taking the salesman number as an element in a second subset to construct the second subset;
through a preset display interface, performing column display on the elements in the first subset and the elements in the second subset according to different corresponding sets;
acquiring the consignment order relation between the elements in the first subset and the elements in the second subset based on different service types according to the numbers of the waiters respectively corresponding to all the clients when the clients form the consignment orders based on different service types;
according to the corresponding consignment list relation between the elements in the first subset and the elements in the second subset based on different service types, on the display interface, performing connection processing on the column values in the display columns corresponding to the first subset and the column values in the display columns corresponding to the second subset;
and acquiring an interface display diagram after the connection processing, namely finishing the construction of the bipartite diagram.
Further, the step of constructing a service type single matrix according to the bipartite graph specifically includes:
determining the number of column values in the display column corresponding to the first subset in a traversal mode, and recording the number as m;
determining the number of column values in the display column corresponding to the second subset in a traversal mode, and recording the number as n;
with A (i,j) The corresponding numerical value is used as an element in the matrix to construct an m × n business type single matrix, wherein i is a column value in a display column corresponding to the first subset, and j is a column value in a display column corresponding to the second subset;
the compound A (i,j) The step of constructing a single matrix of m × n service types by using the corresponding numerical values as elements in the matrix specifically includes:
identifying whether a connecting line exists between the column value corresponding to i and the column value corresponding to j based on the bipartite graph;
if the connection line exists, the A is (i,j) Corresponding number is 1, i.e. said A (i,j) The corresponding matrix has an element of 1;
if no connecting line exists, the A is (i,j) Corresponding to a value of 0, i.e. said A (i,j) The corresponding matrix has an element of 0.
Further, the step of constructing a client list distribution model based on the business type single matrix and a preset prediction iterative algorithm specifically includes:
based on the business type single matrix, screening out the A corresponding to each salesman (i,j) When the number is 1, all client numbers corresponding to the operator numbers, constructing a client number set corresponding to the operator numbers, and representing the set by using in (j);
based on the predictive iterative algorithm:
obtaining the recommendation probability of each operator to different service types, wherein PR (j) represents the output of the recommendation of each operator to different service typesProbability, alpha represents the probability that a client corresponding to the current salesman is continuously searched starting from a column value corresponding to the current salesman, i belongs to in the client number in the corresponding in (j) set for limiting the client which is continuously searched starting from the column value corresponding to the current salesman to belong to, PR (i) represents the probability that the current salesman recommends the same service type as the client number i to an uncertain new client when the last iteration is completed, out (i) represents the probability that different salesmen are respectively selected starting from the column value corresponding to the client number i searched by the current salesman, namely the departure degree of the client, j = u represents that the salesman j selected by the client in the set in (j) after the iteration starting from the initial salesmen is still u, and j ≠ u represents that the salesmen selected by the client in (j) after the iteration starting from the initial salesmen is not u;
and sequencing the recommendation probabilities of different service types by each salesman in a descending order, taking the sequencing result corresponding to each salesman as a recommendation form corresponding to the salesman, setting an association relationship for the salesman and the recommendation form corresponding to the salesman, and completing the construction of the client list distribution model.
Further, the step of obtaining a customer recommendation distribution result based on the service type, the customer information in the customer list and the customer list distribution model specifically includes:
acquiring a service type corresponding to a client to be recommended and distributed and client information in the client list;
screening out a corresponding recommendation form when the recommendation probability corresponding to the service type is the maximum value based on the service type and the recommendation form;
and recommending and allocating clients for different operators according to the incidence relation between the recommendation form and the operators and the client information to finish the client recommendation and allocation.
Further, after the step of obtaining a customer recommended allocation result based on the service type, the customer information in the customer list and the customer list allocation model is executed, the method further includes:
obtaining a client recommendation distribution result;
and executing a step of updating a client list distribution model according to the client recommended distribution result, and performing incremental updating on the client list distribution model.
In order to solve the foregoing technical problem, an embodiment of the present application further provides a device for allocating a client list, where the following technical solutions are adopted:
a customer list distribution apparatus comprising:
the historical transaction data acquisition module is used for reading a service database and searching historical transaction data of different service types of companies in the service database according to the service identification;
the historical transaction data analysis module is used for analyzing the historical transaction data and extracting client information and salesman information from the analysis content corresponding to the historical transaction data;
the bipartite graph building module is used for building a bipartite graph according to the client information and the salesman information;
the matrix construction module is used for constructing a business type single matrix according to the bipartite graph;
the distribution model building module is used for building a client list distribution model based on the business type single matrix and a preset prediction iteration algorithm;
the client list distribution request module is used for receiving a client list distribution request, analyzing the distribution request and acquiring request content, wherein the request content comprises a service type corresponding to a client to be recommended and distributed and client information in a client list;
and the client list distribution processing module is used for obtaining a client recommendation distribution result based on the service type, the client information in the client list and the client list distribution model.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer device comprising a memory and a processor, wherein the memory has stored therein computer readable instructions, and the processor when executing the computer readable instructions implements the steps of the above-mentioned method for distributing a client list.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of a method of client list distribution as described above.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
according to the client list distribution method, through reading a preset database, historical transaction data of different business types of a company are obtained according to a company identification in the database; analyzing the historical transaction data, and extracting client information and serviceman information in the analysis content corresponding to the historical transaction data; constructing a bipartite graph according to the customer information and the salesman information; constructing a business type single matrix according to the bipartite graph; establishing a client list distribution model based on the business type single matrix and a preset prediction iterative algorithm; receiving a client list distribution request, analyzing the distribution request and acquiring request content; and recommending and distributing clients for different operators based on the request content and the client list distribution model. When a new service exists, the client list distribution model is directly used, the client list is automatically distributed to the service staff, intelligent and automatic prediction is realized by using artificial intelligence, and the client list is contacted by more suitable service staff, so that higher completion rates of different service types are ensured.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram to which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for client list assignment according to the present application;
FIG. 3 is a flow diagram for one embodiment of step 202 shown in FIG. 2;
FIG. 4 is a flow diagram of one embodiment of step 203 shown in FIG. 2;
FIG. 5 is a graph illustrating the results of one embodiment of step 203 shown in FIG. 2;
FIG. 6 is a graph illustrating the results of one embodiment of step 204 shown in FIG. 2;
FIG. 7 is a flowchart of one embodiment of step 207 of FIG. 2;
FIG. 8 is a schematic diagram illustrating an embodiment of a client list assignment mechanism according to the present application;
FIG. 9 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof in the description and claims of this application and the description of the figures above, are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the foregoing drawings are used for distinguishing between different objects and not for describing a particular sequential order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein may be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the client list distribution method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the client list distribution apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flowchart of one embodiment of a method of client list assignment according to the present application is shown. The client list distribution method comprises the following steps:
In this embodiment, taking insurance services as an example, the types of the different service types include business insurance services, forced insurance services, health insurance services, accident insurance services, and the like.
By acquiring the historical transaction data of the insurance business, the distribution model can be conveniently constructed by combining the historical transaction data of the company in the later period, so that the optimal salesman is automatically recommended for the new client or the new client for the business type is recommended for the corresponding salesman by directly using the distribution model.
In this embodiment, the customer information includes a customer number, the salesman information includes a salesman number corresponding to a service delivery order, and the step of extracting the customer information and the salesman information from the analysis content corresponding to the historical delivery data specifically includes: traversing the analysis content to obtain a traversal result, and acquiring all client numbers in the analysis content according to the traversal result; and taking the client number as a retrieval field, taking preset different service types as a restriction field, and searching for the corresponding operator numbers of all clients in the analysis content when the clients form a delivery list based on the different service types.
With continued reference to FIG. 3, FIG. 3 is a flowchart of one embodiment of step 202 shown in FIG. 2, including the steps of:
The historical transaction data in the insurance service is analyzed to obtain the client number, the client number is used as a retrieval field, different service types are used as a limiting field, and the corresponding salesman numbers of the client when the client generates a transaction list based on different service types are searched, so that the client number, the service types and the salesman numbers are associated, the construction of the bipartite graph is subjected to preprocessing work, and the construction of the bipartite graph between the client and the salesman is conveniently and rapidly carried out according to the historical transaction data.
And step 203, constructing a bipartite graph according to the customer information and the salesman information.
In this embodiment, the step of constructing a bipartite graph according to the customer information and the salesman information specifically includes: taking all the obtained client numbers as elements in a first subset to construct the first subset; taking the salesman number as an element in a second subset to construct the second subset; through a preset display interface, performing column display on the elements in the first subset and the elements in the second subset according to different corresponding sets; acquiring the consignment-of-order relation between the elements in the first subset and the elements in the second subset based on different service types according to the waiter numbers respectively corresponding to all the clients when the clients compose the consignment-of-order based on different service types; according to the corresponding consignment list relation between the elements in the first subset and the elements in the second subset based on different service types, on the display interface, performing connection processing on the column values in the display columns corresponding to the first subset and the column values in the display columns corresponding to the second subset; and acquiring an interface display diagram after the connection processing, namely finishing the construction of the bipartite diagram.
With continuing reference to FIG. 4, FIG. 4 is a flowchart of one embodiment of step 203 shown in FIG. 2, comprising the steps of:
404, acquiring a consignment order relation between elements in the first subset and elements in the second subset based on different service types according to the numbers of the waiters respectively corresponding to all the clients when the clients form a consignment order based on different service types;
Referring to fig. 5, fig. 5 is a diagram illustrating the result of an embodiment of step 203 shown in fig. 2, and continuing to use the insurance service as an example, assuming that the client numbers in the historical trading data are 1 to 5 respectively, and the corresponding salesman numbers are 6 to 10, assuming that the salesman number 6 is only responsible for the business insurance policy, the salesman number 7 is only responsible for the health insurance policy, the salesman number 8 is only responsible for the health insurance policy, the salesman number 9 is responsible for the health insurance policy and the accident insurance policy, the salesman number 10 is responsible for the business insurance, the business insurance policy of 6 and the accident policy of 7 are fulfilled by the salesman number 1, the business insurance policy of 7 and the health insurance policy of 8 are fulfilled by the salesman number 2, the business insurance policy of 10 and the accident policy of 8 are fulfilled by the salesman number 10, the health insurance policy of 4 and the accident policy of 5 are fulfilled by the salesman number 5 and the accident policy of 10.
By adopting a bipartite graph construction mode, the business-to-business-out list information between the client and the business officer is graphically represented, so that not only can business statistical personnel of a company conveniently carry out visual examination and statistics, but also secondary processing of data through graphs is facilitated.
And step 204, constructing a business type single matrix according to the bipartite graph.
In this embodiment, the step of constructing a business type single matrix according to the bipartite graph specifically includes: determining the number of column values in the display column corresponding to the first subset in a traversal mode, and recording the number as m; determining the number of column values in the display column corresponding to the second subset in a traversal mode, and recording the number as n; with A (i,j) Using the corresponding numerical value as an element in a matrix, and constructing an mxn business type single matrix, wherein i is a column value in a display column corresponding to the first subset, and j is a column value in a display column corresponding to the second subset; the compound A (i,j) The step of constructing a single matrix of m × n service types by using the corresponding numerical values as elements in the matrix specifically includes: identifying whether a connecting line exists between the column value corresponding to i and the column value corresponding to j based on the bipartite graph; if the connection line exists, the A is (i,j) Corresponding number is 1, namely said A (i,j) The corresponding matrix has an element of 1; if no connecting line exists, the step A (i,j) Corresponding to a value of 0, i.e. said A (i,j) The corresponding matrix has an element of 0.
Referring to fig. 6, fig. 6 is a diagram illustrating the result of an embodiment of step 204 shown in fig. 2, continuing to take the bipartite graph shown in fig. 5 corresponding to the insurance service as an example, by traversing, it can be known that the number of column values in the display columns in the first subset and the second subset are both 5, then a 5 × 5 matrix is constructed, the column values of the first subset include 1,2, 3, 4, and 5, the column values of the second subset include 6, 7, 8, 9, and 10, through matrix conversion, 25 matrix elements are generated, the values of the 25 matrix elements are determined according to the connection relationship in the bipartite graph, if a connection exists, the corresponding value is 1, otherwise, the corresponding value is 0.
By performing matrix conversion on the bipartite graph, a computer can conveniently perform data processing according to graph characteristics, and model construction is facilitated.
And step 205, establishing a client list distribution model based on the business type single matrix and a preset prediction iterative algorithm.
In this embodiment, the step of constructing a client list distribution model based on the service type single matrix and a preset prediction iterative algorithm specifically includes: based on the business type single matrix, screening out the A corresponding to each salesman (i,j) When the number is 1, all client numbers corresponding to the operator numbers, constructing a client number set corresponding to the operator numbers, and representing the set by using in (j); based on the predictive iterative algorithm:
obtaining recommendation probabilities of each salesman to different service types, wherein PR (j) represents that the recommendation probability of each salesman for different service types is output, alpha represents the probability of starting from a column value corresponding to a current salesman to continuously search a next client corresponding to the current salesman, i belongs to in the client number in a corresponding in (j) set for limiting the next client which is started from the column value corresponding to the current salesman to continuously search, PR (i) represents the probability that the current salesman recommends the service type which is the same as the client number i to an uncertain new client when the previous iteration is completed, out (i) represents the probabilities that different salesmen are respectively selected starting from the corresponding column value of the client number i when the current salesmen searches the client number i, i = u represents that the salesman j selected by the client in (j) is still u after the initial salesmen starts the iteration is not u, and j ≠ u represents that the salesmen selected by the client in (j) in the set in (j) after the initial salesmen u iteration is not selected as u; sorting the recommendation probabilities of different service types by each salesman in a descending order, taking the sorting result corresponding to each salesman as the recommendation form corresponding to the salesmanAnd setting an incidence relation for the corresponding recommendation form to complete the construction of the client list distribution model.
Continuing with the insurance service as an example, the single matrix corresponding to the service type is shown in fig. 6, and by screening data in the matrix, the customer number corresponding to the operator with the number 6 is 1, that is, in (6) = {1}, and the customer numbers corresponding to the operator with the number 7 are 1 and 2, that is, in (7) = {1,2}, and in the same way, the in (j) corresponding to the operators with the numbers 8, 9, and 10 are screened out, and assuming that the operator with the number 6 is the initial operator, it is obvious that there is only one customer number corresponding to the operator, then the corresponding alpha value, that is, the probability of finding the next customer is 0, the customer number is 1, the number of finding the corresponding operator with the customer as the starting point can be the operator with the number 6 and the number 7, that is, out (1) =50%, assuming that the operator with the number of 7 is the initial operator, the number of the client which is found for the first time is 1, obviously, the number of the corresponding client is only two, then the corresponding alpha value, that is, the probability of finding the next client is 50%, assuming that the client which is currently found is the client with the number of 2, and the operator corresponding to the client with the number of 2 also has two bits, that is, the number of 7 and the operator with the number of 8, then, taking the client with the number of 2 as the starting point, finding the operator with the number of 6 and the number of 7, that is, out (2) =50%, and similarly, taking the client with the number of 3 as the starting point, finding the number of the corresponding operator can only be 10, that is, out (3) =100%.
Obtaining and obtaining the recommendation probability of each salesman to different service types through the service type list matrix and a preset prediction iterative algorithm, performing descending sorting on the recommendation probability of each salesman to different service types, taking the descending sorting result corresponding to each salesman as a recommendation form corresponding to the salesman, setting an incidence relation for the salesman and the recommendation form corresponding to the salesman, completing the construction of the client list distribution model, facilitating the automatic recommendation and distribution of clients to the salesman directly according to the distribution model when an old client performs continuous guarantee service or a new client performs guarantee service, and being more intelligent.
And step 207, obtaining a customer recommendation distribution result based on the service type, the customer information in the customer list and the customer list distribution model.
In this embodiment, the step of obtaining a customer recommended allocation result based on the service type, the customer information in the customer list, and the customer list allocation model specifically includes: acquiring a service type corresponding to a client to be recommended and distributed and client information in the client list; screening out a corresponding recommendation form when the recommendation probability corresponding to the service type is the maximum value based on the service type and the recommendation form; and recommending and allocating clients for different operators according to the incidence relation between the recommendation form and the operators and the client information to finish the client recommendation and allocation.
With continuing reference to FIG. 7, FIG. 7 is a flowchart of one embodiment of step 207 shown in FIG. 2, comprising the steps of:
and 703, recommending and allocating clients for different operators according to the association relationship between the recommendation form and the operators and the client information, and completing client recommendation and allocation.
In this embodiment, after the step of obtaining a customer recommended allocation result based on the service type, the customer information in the customer list, and the customer list allocation model is performed, the method further includes: obtaining a client recommendation distribution result; and according to the client recommended distribution result, executing the steps 203 to 205 again, and performing incremental updating on the client list distribution model.
And recommending and distributing clients for different operators by requesting content and the client list distribution model, executing the steps 203 to 205 again according to the recommended and distributed result, and performing incremental updating on the client list distribution model, so that the client list distribution model is continuously updated, the model is convenient to reuse, and the service application range of the model is ensured to be more and more extensive by incremental improvement.
The method comprises the steps of reading a preset database, and acquiring historical transaction data of different business types of a company according to company identification in the database; analyzing the historical transaction data, and extracting client information and serviceman information in the analysis content corresponding to the historical transaction data; constructing a bipartite graph according to the customer information and the salesman information; constructing a business type single matrix according to the bipartite graph; a single matrix is generated based on the service types and a preset prediction iterative algorithm, and a client list distribution model is constructed; receiving a client list distribution request, analyzing the distribution request and acquiring request content; and recommending and distributing clients for different operators based on the request content and the client list distribution model. When a new service exists, the client list distribution model is directly used, the client list is automatically distributed to the service personnel, intelligent and automatic prediction is achieved by using artificial intelligence, and the client list is delivered to the more suitable service personnel for contact, so that higher completion rates of different service types are guaranteed.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
In the embodiment of the application, the historical transaction data can be acquired from a preset database through a big data processing technology, then a client list distribution prediction model is constructed through a bipartite graph and a matrix, the client list distribution model is directly used when a new service exists, a client list is automatically distributed for a service person, incremental model updating is carried out, intelligent and automatic prediction is achieved through artificial intelligence, the client list is contacted by more suitable service persons, and high completion rates of different service types are guaranteed.
With further reference to fig. 8, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a device for allocating a client list, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.
As shown in fig. 8, the apparatus 800 for allocating a customer list according to this embodiment includes: a history transaction data acquisition module 801, a history transaction data analysis module 802, a bipartite graph construction module 803, a matrix construction module 804, an allocation model construction module 805, a client list allocation request module 806 and a client list allocation processing module 807. Wherein:
a historical transaction data acquisition module 801, configured to read a service database, and search historical transaction data of different service types of a company in the service database according to a service identifier;
a historical transaction data analysis module 802, configured to analyze the historical transaction data, and extract client information and clerk information from analysis content corresponding to the historical transaction data;
a bipartite graph construction module 803, configured to construct a bipartite graph according to the customer information and the salesman information;
a matrix construction module 804, configured to construct a single matrix according to the bipartite graph;
the distribution model building module 805 is used for building a client list distribution model based on the business type single matrix and a preset prediction iteration algorithm;
a client list distribution request module 806, configured to receive a client list distribution request, analyze the distribution request, and obtain request content, where the request content includes a service type corresponding to a client to be recommended and distributed and client information in the client list;
a client list distribution processing module 807, configured to obtain a client recommended distribution result based on the service type, the client information in the client list, and the client list distribution model.
The method comprises the steps of reading a preset database, and acquiring historical transaction data of different business types of a company according to company identification in the database; analyzing the historical transaction data, and extracting the client information and the salesman information in the analyzed content corresponding to the historical transaction data; constructing a bipartite graph according to the customer information and the salesman information; constructing a business type list matrix according to the bipartite graph; a single matrix is generated based on the service types and a preset prediction iterative algorithm, and a client list distribution model is constructed; receiving a client list distribution request, analyzing the distribution request and acquiring request content; and recommending and distributing customers for different salesmen based on the request content and the customer list distribution model. When a new service exists, the client list distribution model is directly used, the client list is automatically distributed to the service staff, intelligent and automatic prediction is realized by using artificial intelligence, and the client list is contacted by more suitable service staff, so that higher completion rates of different service types are ensured.
In some embodiments of the present application, the client list allocating apparatus further includes an incremental updating module 808, where the incremental updating module is configured to obtain a recommended allocation result after the client list allocating processing module performs the allocating process; and according to the recommended distribution result, sequentially starting the bipartite graph construction module, the matrix construction module and the distribution model construction module again, and performing incremental updating on the client list recommendation model.
According to the method and the device, the client list distribution model is updated incrementally through the incremental updating module, the client list distribution model is continuously updated, model multiplexing is facilitated, and the service application range of the model is ensured to be more and more wide through incremental improvement.
Those skilled in the art will appreciate that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the programs can include the processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 9 in particular, fig. 9 is a block diagram of a basic structure of a computer device according to the embodiment.
The computer device 9 comprises a memory 9a, a processor 9b, a network interface 9c communicatively connected to each other via a system bus. It is noted that only a computer device 9 having components 9a-9c is shown, but it is to be understood that not all of the shown components need be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 9a includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 9a may be an internal storage unit of the computer device 9, such as a hard disk or a memory of the computer device 9. In other embodiments, the memory 9a may also be an external storage device of the computer device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the computer device 9. Of course, the memory 9a may also comprise both an internal storage unit of the computer device 9 and an external storage device thereof. In this embodiment, the memory 9a is generally used for storing an operating system installed in the computer device 9 and various application software, such as computer readable instructions of a client list distribution method. Further, the memory 9a may also be used to temporarily store various types of data that have been output or are to be output.
The processor 9b may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 9b is typically arranged to control the overall operation of the computer device 9. In this embodiment, the processor 9b is configured to execute computer readable instructions stored in the memory 9a or process data, for example, execute computer readable instructions of the client list allocation method.
The network interface 9c may comprise a wireless network interface or a wired network interface, and the network interface 9c is typically used for establishing a communication connection between the computer device 9 and other electronic devices.
The embodiment provides a computer device, and belongs to the technical field of artificial intelligence. The method comprises the steps of reading a preset database, and acquiring historical transaction data of different business types of a company according to company identification in the database; analyzing the historical transaction data, and extracting client information and serviceman information in the analysis content corresponding to the historical transaction data; constructing a bipartite graph according to the customer information and the salesman information; constructing a business type single matrix according to the bipartite graph; a single matrix is generated based on the service types and a preset prediction iterative algorithm, and a client list distribution model is constructed; receiving a client list distribution request, analyzing the distribution request and acquiring request content; and recommending and distributing customers for different salesmen based on the request content and the customer list distribution model. When a new service exists, the client list distribution model is directly used, the client list is automatically distributed to the service staff, intelligent and automatic prediction is realized by using artificial intelligence, and the client list is contacted by more suitable service staff, so that higher completion rates of different service types are ensured.
The present application provides yet another embodiment, which is a computer readable storage medium storing computer readable instructions executable by a processor to cause the processor to perform the steps of the customer list assignment method as described above.
The embodiment provides a computer readable storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of reading a preset database, and acquiring historical transaction data of different business types of a company according to company identification in the database; analyzing the historical transaction data, and extracting client information and serviceman information in the analysis content corresponding to the historical transaction data; constructing a bipartite graph according to the customer information and the salesman information; constructing a business type list matrix according to the bipartite graph; establishing a client list distribution model based on the business type single matrix and a preset prediction iterative algorithm; receiving a client list distribution request, analyzing the distribution request and acquiring request content; and recommending and distributing customers for different salesmen based on the request content and the customer list distribution model. When a new service exists, the client list distribution model is directly used, the client list is automatically distributed to the service staff, intelligent and automatic prediction is realized by using artificial intelligence, and the client list is contacted by more suitable service staff, so that higher completion rates of different service types are ensured.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and the embodiments are provided so that this disclosure will be thorough and complete. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A client ticket assignment method, comprising the steps of:
reading a business database, and searching historical transaction data of different business types of a company in the business database according to a business identifier;
analyzing the historical transaction data, and extracting client information and salesman information from the analysis content corresponding to the historical transaction data;
constructing a bipartite graph according to the customer information and the salesman information;
constructing a business type list matrix according to the bipartite graph;
establishing a client list distribution model based on the business type single matrix and a preset prediction iterative algorithm;
receiving a client list distribution request, analyzing the distribution request, and acquiring request content, wherein the request content comprises a service type corresponding to a client to be recommended and distributed and client information in a client list;
and obtaining a customer recommendation distribution result based on the service type, the customer information in the customer list and the customer list distribution model.
2. The client ticket assignment method of claim 1, wherein the client information comprises a client number, the operator information comprises a corresponding operator number when the service is a delivery ticket, and the step of extracting the client information and the operator information from the analysis content corresponding to the historical delivery data specifically comprises:
traversing the analysis content to obtain a traversal result, and acquiring all client numbers in the analysis content according to the traversal result;
and taking the client number as a retrieval field, taking preset different service types as a limiting field, and searching for the corresponding salesman numbers of all clients in the analysis content when the clients form a delivery list based on the different service types.
3. The client ticket assignment method of claim 2, wherein the step of constructing a bipartite graph according to the client information and the attendant information specifically comprises:
taking all the obtained client numbers as elements in a first subset to construct the first subset;
constructing a second subset by taking the salesman number as an element in the second subset;
through a preset display interface, performing column display on the elements in the first subset and the elements in the second subset according to different corresponding sets;
acquiring the consignment order relation between the elements in the first subset and the elements in the second subset based on different service types according to the numbers of the waiters respectively corresponding to all the clients when the clients form the consignment orders based on different service types;
according to the corresponding delivery list relation between the elements in the first subset and the elements in the second subset based on different service types, carrying out line connection processing on the column values in the display columns corresponding to the first subset and the column values in the display columns corresponding to the second subset on the display interface;
and acquiring an interface display diagram after the connection processing, namely finishing the construction of the bipartite diagram.
4. The method for distributing a customer list according to claim 3, wherein the step of constructing a business type list matrix according to the bipartite graph specifically comprises:
determining the number of column values in the display column corresponding to the first subset in a traversal mode, and recording the number as m;
determining the number of column values in the display column corresponding to the second subset in a traversal mode, and recording the number as n;
with A (i,j) The corresponding numerical value is used as an element in the matrix to construct an m × n business type single matrix, wherein i is a column value in a display column corresponding to the first subset, and j is a column value in a display column corresponding to the second subset;
the compound A (i,j) The step of constructing a single matrix of m × n service types by using the corresponding numerical values as elements in the matrix specifically includes:
identifying whether a connecting line exists between the column value corresponding to i and the column value corresponding to j based on the bipartite graph;
if the connection line exists, the A is (i,j) Corresponding number is 1, i.e. said A (i,j) The corresponding matrix has an element of 1;
if no connecting line exists, the step A (i,j) Corresponding to a value of 0, i.e. said A (i,j) The corresponding matrix has an element of 0.
5. The method according to claim 4, wherein the step of constructing a client list distribution model based on the service type single matrix and a preset predictive iterative algorithm specifically includes:
based on the business type single matrix, screening out the A corresponding to each salesman (i,j) When the number is 1, all client numbers corresponding to the operator numbers, constructing a client number set corresponding to the operator numbers, and representing the set by using in (j);
based on the predictive iterative algorithm:
obtaining the recommendation probability of each operator to different service types, wherein PR (j) represents the recommendation probability of each operator to different service types, alpha represents the probability of continuously searching the next client corresponding to the current operator starting from the column value corresponding to the current operator, and iee in (j) is used for limiting the next client continuously searched from the column value corresponding to the current operator to belong to the corresponding in (j) setPR (i) represents the probability that the current salesman recommends the same service type as the customer number i to an uncertain new customer when the last iteration is completed, out (i) represents the probability that different salesmen are respectively selected starting from the value corresponding to the customer number i searched by the current salesmen, i.e. the departure degree of the customer, j = u represents that the salesmen j selected by the customer in the set in (j) is still u after the iteration starting from the initial salesmen u, and j ≠ u represents that the salesmen j selected by the customer in the set in (j) is not u after the iteration starting from the initial salesmen u;
and performing descending sorting on the recommendation probability of each salesman for different service types, taking the descending sorting result corresponding to each salesman as a recommendation form corresponding to the salesman, setting an association relationship for the salesman and the recommendation form corresponding to the salesman, and completing the construction of the client list distribution model.
6. The method for distributing a customer list according to claim 5, wherein the step of obtaining a customer recommended distribution result based on the service type, the customer information in the customer list and the customer list distribution model specifically comprises:
acquiring a service type corresponding to a client to be recommended and distributed and client information in the client list;
screening out a corresponding recommendation form when the recommendation probability corresponding to the service type is the maximum value based on the service type and the recommendation form;
and recommending and allocating clients for different operators according to the incidence relation between the recommendation form and the operators and the client information, and completing client recommendation and allocation.
7. The client ticket assignment method of claim 1, wherein after performing the step of obtaining a client recommended assignment based on the service type, client information in the client list and the client list assignment model, the method further comprises:
obtaining a client recommendation distribution result;
and executing a customer list distribution model updating step according to the customer recommended distribution result, and performing incremental updating on the customer list distribution model.
8. A customer list distribution apparatus, comprising:
the historical transaction data acquisition module is used for reading a service database and searching historical transaction data of different service types of companies in the service database according to the service identification;
the historical transaction data analysis module is used for analyzing the historical transaction data and extracting client information and salesman information from the analysis content corresponding to the historical transaction data;
the bipartite graph building module is used for building a bipartite graph according to the client information and the salesman information;
the matrix construction module is used for constructing a single matrix of the service type according to the bipartite graph;
the distribution model building module is used for building a client list distribution model based on the business type single matrix and a preset prediction iterative algorithm;
the client list distribution request module is used for receiving a client list distribution request, analyzing the distribution request and acquiring request content, wherein the request content comprises a service type corresponding to a client to be recommended and distributed and client information in a client list;
and the client list distribution processing module is used for obtaining a client recommendation distribution result based on the service type, the client information in the client list and the client list distribution model.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the client ticket assignment method of any of claims 1 to 7.
10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, carry out the steps of a client ticket assignment method according to any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116629796A (en) * | 2023-05-26 | 2023-08-22 | 深圳科海数信科技有限公司 | Full-period sales management system based on artificial intelligence and big data |
| CN117312678A (en) * | 2023-10-24 | 2023-12-29 | 亿企查科技有限公司 | Intelligent recommendation method, equipment and storage medium for potential clients based on big data |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116629796A (en) * | 2023-05-26 | 2023-08-22 | 深圳科海数信科技有限公司 | Full-period sales management system based on artificial intelligence and big data |
| CN117312678A (en) * | 2023-10-24 | 2023-12-29 | 亿企查科技有限公司 | Intelligent recommendation method, equipment and storage medium for potential clients based on big data |
| CN117312678B (en) * | 2023-10-24 | 2024-04-05 | 亿企查科技有限公司 | Intelligent recommendation method, equipment and storage medium for potential clients based on big data |
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