CN113240444A - Bank customer service seat recommendation method and device - Google Patents
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Abstract
The invention discloses a method and a device for recommending bank customer service seats, which relate to the technical field of artificial intelligence, and comprise the following steps: after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client; presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data; determining the character characteristic parameters of the client according to the sound characteristic parameters of the client; based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; and accessing the bank customer service seat matched with the customer for the customer. The invention can match customer service seats aiming at different customers in a personalized way, avoids the problem that the customer seats and the customers are difficult to communicate because the customer seats are randomly distributed for the customers in the prior art, improves the satisfaction degree of the customers to the customer service seats and improves the telephone communication experience of the customers.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a bank customer service seat recommendation method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, when a bank client calls to make a bank business consultation, a bank can randomly access different customer service seats to serve the client.
When a customer communicates with a randomly accessed customer service seat, the problem that the customer service seat cannot meet the requirements of the customer inevitably occurs, and the customer service seat cannot be matched with the personality of the customer, so that the communication between the customer and the randomly accessed customer service seat is not smooth, the satisfaction degree of the customer on the customer service seat is reduced, and the telephone communication experience of the customer is reduced.
Therefore, the problem to be solved urgently in the industry is solved by aiming at providing personalized bank customer service seat service for the customer by different customers and recommending the bank seat suitable for the customer.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a bank customer service seat recommendation method, which is used for improving the satisfaction degree of a customer on a customer service seat and improving the telephone communication experience of the customer, and comprises the following steps:
after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client;
presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data;
determining the character characteristic parameters of the client according to the sound characteristic parameters of the client;
based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled of a client, character characteristic parameters and historical data of a bank customer service seat;
and accessing the bank customer service seat matched with the customer for the customer.
The embodiment of the invention also provides a bank customer service seat recommending device, which is used for improving the satisfaction degree of customers on customer service seats and improving the telephone communication experience of the customers, and comprises the following components:
the data acquisition module is used for acquiring historical service record data of the client and sound characteristic parameters of the client after receiving a service request of the client;
the client pending service speculation module is used for predicting the client pending service corresponding to the service request of the client according to the historical service record data of the client;
the personality characteristic parameter determining module is used for determining the personality characteristic parameter of the client according to the sound characteristic parameter of the client;
the bank customer service seat matching module is used for recommending a neural network model based on the bank customer service seat, and determining the bank customer service seat matched with the client according to the estimated business to be handled of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled of a client, character characteristic parameters and historical data of a bank customer service seat;
and the bank customer service seat access module is used for accessing the bank customer service seat matched with the customer for the customer.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the bank customer service seat recommendation method is realized when the processor executes the computer program.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the bank customer service seat recommendation method.
In the embodiment of the invention, after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client; presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data; determining the character characteristic parameters of the client according to the sound characteristic parameters of the client; based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled of a client, character characteristic parameters and historical data of a bank customer service seat; the bank customer service seat matched with the customer is accessed for the customer, so that after a service request of the customer is received, a neural network model is recommended for the bank customer service seat, the bank customer service seat matched with the customer is accessed for the customer, the customer service seat can be matched for different customers in a personalized mode, the problem that the customer seat and the customer are difficult to communicate due to the fact that the customer service seat is randomly distributed for the customer in the prior art is solved, the satisfaction degree of the customer on the customer service seat is improved, and telephone communication experience of the customer is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a schematic flow chart illustrating a method for recommending a bank customer service agent according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating an embodiment of a method for recommending a service agent for a bank;
FIG. 3 is a diagram illustrating an embodiment of a method for recommending a service agent of a bank;
FIG. 4 is a diagram illustrating an embodiment of a method for recommending a service agent of a bank;
FIG. 5 is a diagram illustrating an embodiment of a method for recommending a service agent for a bank;
FIG. 6 is a schematic structural diagram of a bank customer service agent recommendation device according to an embodiment of the present invention;
fig. 7 is a diagram illustrating an exemplary embodiment of a bank customer service seat recommendation device according to the present invention;
FIG. 8 is a diagram illustrating an exemplary embodiment of a device for recommending a customer service seat in a bank according to the present invention;
fig. 9 is a diagram illustrating an exemplary embodiment of a bank customer service seat recommendation device according to the present invention;
FIG. 10 is a diagram illustrating a computer device for bank customer service agent recommendation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
At present, when a customer calls a bank, the customer can have the problems of unsmooth communication and the like when communicating with customer service, and the customer service assigned by calling each time is different, so that the user often speaks repeatedly for a plurality of times due to one problem, and the user experience is poor.
Therefore, the problem to be solved urgently in the industry is solved by aiming at providing personalized bank customer service seat service for the customer by different customers and recommending the bank seat suitable for the customer.
The embodiment of the invention provides a bank customer service seat recommendation method, which relates to the technical field of artificial intelligence and is used for improving the satisfaction degree of a customer on a customer service seat and improving the telephone communication experience of the customer, and as shown in figure 1, the method can comprise the following steps:
step 101: after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client;
step 102: presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data;
step 103: determining the character characteristic parameters of the client according to the sound characteristic parameters of the client;
step 104: based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of a bank customer service seat;
step 105: and accessing the bank customer service seat matched with the customer for the customer.
In the embodiment of the invention, after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client; presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data; determining the character characteristic parameters of the client according to the sound characteristic parameters of the client; based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of a bank customer service seat; the bank customer service seat matched with the customer is accessed to the customer, so that after a service request of the customer is received, a neural network model is recommended based on the bank customer service seat, the customer is accessed to the bank customer service seat matched with the customer, the customer service seat can be matched in a personalized mode aiming at different customers, the problem that the customer seat and the customer are difficult to communicate due to the fact that the customer service seat is randomly distributed for the customer in the prior art is solved, the satisfaction degree of the customer on the customer service seat is improved, and telephone communication experience of the customer is improved.
In specific implementation, firstly, after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client.
In an embodiment, the historical service record data of the client includes a historical service request, a historical dial-in record, a historical chat content, a historical transaction record, and a client service record of the client.
In the embodiment, after a service request of a client is received, historical service record data of the client and sound characteristic parameters of the client are acquired, and a client pending business corresponding to the service request of the client is presumed according to the historical service record data of the client.
In the above embodiment, the service request of the client is presumed to correspond to the pending service of the client according to the historical service record data of the client, and may be, for example: according to the previous customer service call dialing record of the user, the chat content with the customer seat and the recent transaction record, such as the transaction record of a branch office of a mobile phone bank, the service to be dealt with of the customer can be calculated by the data and an artificial intelligence algorithm.
In the embodiment, after the business to be transacted of the client corresponding to the service request of the client is presumed according to the historical service record data of the client, the personality characteristic parameter of the client is determined according to the voice characteristic parameter of the client.
In an embodiment, the personality characteristic parameter of the client is determined according to the sound characteristic parameter of the client, and may be, for example: the character characteristics of the user are judged through the voice and audio tones of the user, the data are put into an intelligent system, and the system calculates the customer service suitable for the user and accesses the customer service for the user.
In the above embodiment, by obtaining the historical service record data of the customer, it is helpful to infer the customer pending business corresponding to the service request of the customer in the subsequent step. By supposing the customer to-be-managed service corresponding to the service request of the customer and determining the personality characteristic parameters of the customer, the method is helpful for determining the bank service seat matched with the customer for the customer based on the bank service seat recommendation neural network model in the subsequent steps.
In specific implementation, after the personality characteristic parameters of a client are determined according to the voice characteristic parameters of the client, a neural network model is recommended based on a bank customer service seat, and the bank customer service seat matched with the client is determined according to the deduced business to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of the bank customer service seat.
In the embodiment, the neural network model is recommended based on the bank customer service seat, the bank customer service seat matched with the customer can be accessed to the customer, the customer service seat can be matched in a personalized mode aiming at different customers, the problem that the customer seat and the customer are difficult to communicate due to the fact that the customer seat is randomly distributed for the customer in the prior art is solved, the satisfaction degree of the customer on the customer service seat is improved, and the telephone communication experience of the customer is improved.
In specific implementation, after the neural network model is recommended based on the bank customer service seat, the bank customer service seat matched with the customer is determined according to the estimated business to be handled of the customer and the personality characteristic parameters of the customer, and the bank customer service seat matched with the customer is accessed for the customer.
In the embodiment, the bank customer service seat matched with the customer is accessed to the customer, so that the customer service seat can be matched for different customers in a personalized manner, and the problem that the customer seat is difficult to communicate with the customer due to random distribution of the customer service seat for the customer in the prior art is solved.
In specific implementation, the method for recommending a bank customer service seat according to the embodiment of the present invention, as shown in fig. 2, may further include:
establishing a bank customer service seat recommendation neural network model according to the following modes:
step 201: dividing historical recommendation data into a training data set and a test data set;
step 202: training the neural network model by using a training data set based on a back propagation algorithm and a genetic algorithm to obtain a trained bank customer service seat recommended neural network model;
step 203: and testing the trained neural network model recommended by the bank customer service seat by using the test data set.
In the embodiment, the establishment of the neural network model for recommending the bank customer service seat can be realized by an algorithm according to the neural network model.
The artificial intelligence algorithm can be realized as follows: according to a machine learning method, a bank customer service seat recommendation neural network model is established, and input data of the model can comprise historical recommendation data; the output data of the model is the determined bank customer service seat matched with the customer.
In an embodiment, the neural network model may include a GA-BP model established by a BP algorithm and a genetic algorithm. The specific modeling process is as follows: and introducing a genetic algorithm in the optimization aspect of the weight and the threshold of the BP neural network, and constructing the GA-BP neural network model.
Furthermore, in the process of constructing the GA-BP neural network structure, the BP neural network structure is determined according to the number of input data and output data of a network end, and then the number of parameters needing to be optimized in a genetic algorithm is determined. According to the kolmogorov (kolmogorov theory, namely local uniform isotropic turbulence, three basic assumptions which are provided by kolmogorov in 1941 in the process of establishing a statistical theory of turbulence) principle, a three-layer BP neural network is enough to complete any mapping from n dimension to m dimension, generally only one hidden layer is needed, and the number of hidden layer nodes is determined by a trial-and-error method, so that the GA-BP neural network structure is finally established.
The optimal individuals output by the genetic algorithm can be used as initial weights and thresholds of the BP neural network, and training and learning of the BP neural network model are achieved. And taking the historical recommendation data as historical data, dividing the historical data into a training set and a testing set, training the GA-BP neural network model based on historical data analysis, and verifying the prediction accuracy of the model by using a testing sample.
The GA-BP neural network model can continuously self-optimize the model through a machine learning method in the using process, and the effectiveness of the model is continuously improved.
In a specific implementation, the historical recommendation data may further include: the business handling priority of different bank service agents and the historical data of the character characteristic parameters of different bank service agents.
In an embodiment, the method for recommending a bank customer service agent according to an embodiment of the present invention, as shown in fig. 3, may further include:
step 301: acquiring historical working records, sound characteristic parameters and working information of different bank customer service agents;
step 302: determining service handling priorities of different bank service agents according to the acquired historical working records of the different bank service agents;
step 303: and determining the character characteristic parameters of different bank customer service agents according to the acquired sound characteristic parameters and the acquired professional information of the different bank customer service agents.
In an embodiment, the generating a bank service seat matched with a customer according to the estimated service to be handled of the customer and the personality characteristic parameters of the customer based on the neural network model recommended by the bank service seat may include:
and generating the bank service seat matched with the client according to the deduced service to be handled of the client, the personality characteristic parameters of the client, the service handling priorities of different bank service seats and the personality characteristic parameters of different bank service seats on the basis of the neural network model recommended by the bank service seat.
In the above embodiment, the bank service seat matched with the customer can be generated by mapping input data established by the bank service seat recommendation neural network model to output data, using the inferred customer service to be transacted, the personality characteristic parameter of the customer, the service transaction priorities of different bank service seats and the personality characteristic parameter of different bank service seats as input data, and using the bank service seat matched with the customer as output data.
In specific implementation, after receiving a service request of a client, obtaining historical service record data of the client and a sound characteristic parameter of the client, as shown in fig. 4, may include:
step 401: after receiving a service request of a client, searching a customer service seat which provides customer service for the client from historical service record data of the client;
step 402: if the customer service seat which provides the customer service for the customer is found, recommending the found customer service seat which provides the customer service for the customer to the customer;
step 403: and if the customer service seat which provides the customer service for the customer is not found, acquiring the historical service record data of the customer and the sound characteristic parameters of the customer.
In the embodiment, if a customer service seat that has already provided customer service for a customer is found, recommending the found customer service seat that has already provided customer service for the customer to the customer includes:
and if the customer service seat which provides the customer service for the customer is found and confirmation information that the customer confirms to use the customer service seat is received, recommending the found customer service seat which provides the customer service for the customer to the customer.
In the above embodiment, by searching the historical service record data of the customer for the customer service seat that has already provided the customer service for the customer, the customer service seat that provides the customer service for the customer can be recommended for the customer.
In the embodiment, the user can independently select the customer service of the user, and can recommend and select the customer service which is provided with the service for the customer last time for the customer when the user makes a call repeatedly; the user can select own customer service independently or help the user to select proper customer service in an artificial intelligence mode according to data such as voice of the user, and can subjectively select to continue the previous customer service or select the customer service again when the call is repeatedly made.
In the embodiment, if a customer service seat which provides customer service for a customer is found and confirmation information that the customer refuses to use the customer service seat is received, historical service record data of the customer and sound characteristic parameters of the customer are obtained, a neural network model is recommended based on the bank service seat, and the bank service seat matched with the customer is generated according to the presumed business to be handled of the customer and the personality characteristic parameters of the customer.
In specific implementation, the method for recommending a bank customer service seat according to the embodiment of the present invention, as shown in fig. 5, may further include:
step 501: if the number of the bank service agents matched with the client is multiple, the information of the bank service agents matched with the client is sent to the client;
step 502: receiving selection information of a plurality of bank customer service seats matched with customers from the customers;
accessing the bank customer service seat matched with the customer for the customer may include:
and accessing the bank service seat selected by the customer for the customer according to the received selection information of the customer on the plurality of bank service seats matched with the customer.
In the embodiment, a plurality of bank service agents matched with the client are determined based on the neural network model recommended by the bank service agents according to the estimated service to be handled of the client and the personality characteristic parameters of the client, if the number of the bank service agents matched with the client is multiple, the information of the bank service agents matched with the client is sent to the client, the selection opportunity is provided for the client, and the user can subjectively select the favorite service agents.
In the embodiment of the invention, after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client; presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data; determining the character characteristic parameters of the client according to the sound characteristic parameters of the client; based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of a bank customer service seat; the bank customer service seat matched with the customer is accessed to the customer, so that after a service request of the customer is received, a neural network model is recommended based on the bank customer service seat, the customer is accessed to the bank customer service seat matched with the customer, the customer service seat can be matched in a personalized mode aiming at different customers, the problem that the customer seat and the customer are difficult to communicate due to the fact that the customer service seat is randomly distributed for the customer in the prior art is solved, the satisfaction degree of the customer on the customer service seat is improved, and telephone communication experience of the customer is improved.
A specific embodiment is given below to illustrate a specific application of the method of the present invention, and in this embodiment, the method may include:
firstly, information such as voice, audio, tone and volume of customer service can be collected, and the information and professional characteristics of the customer service, such as frequently solving the problem of a credit card, and the information of the age, the specialty, the sex and the like of the customer service are input into an artificial intelligence system. The system judges the character type of the customer service, and outputs the character type as { outward, optimistic, convergent, sinkable } and the like. And binding the personality category with the user.
When a user calls, the system calls the previous dial-in record and chat content of the user, and the recent transaction record comprises a branch office of a mobile banking, the business function transacted by the user is calculated according to the data and an artificial intelligence algorithm, the character characteristic of the user is judged according to the voice and audio tone of the user, the data are put into the intelligent system, and the system calculates the customer service suitable for the user and accesses the customer service for the user.
A model is established through a BP neural network and a genetic algorithm, the genetic algorithm is introduced in the aspect of optimizing the weight and the threshold of the BP neural network, and a GA-BP neural network model is established. Determining a GA-BP neural network structure, determining the BP neural network structure according to the number of network input and output, and further determining the number of parameters needing to be optimized in a genetic algorithm. According to the kolmogorov principle, one three-layer BP neural network can sufficiently complete any mapping from n dimension to m dimension, generally only one hidden layer is needed, and the number of hidden layer nodes is determined by a trial and error method, so that the GA-BP neural network structure is determined. And (4) training and learning the BP neural network by taking the optimal individual output by the genetic algorithm as the initial weight and the threshold of the BP neural network. And taking the collected client information as historical data, dividing the historical data into a training set and a testing set, training the GA-BP neural network model based on historical data analysis, and verifying the prediction accuracy of the model by using a testing sample. The model is continuously self-optimized through a machine learning method in the using process, and the effectiveness of the model is improved.
Of course, it is understood that other variations of the above detailed flow can be made, and all such variations are intended to fall within the scope of the present invention.
The embodiment of the invention also provides a bank customer service seat recommendation device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to the bank customer service seat recommendation method, the implementation of the device can refer to the implementation of the bank customer service seat recommendation method, and repeated parts are not described again.
An embodiment of the present invention further provides a bank customer service seat recommendation device, configured to improve satisfaction of a customer on a customer service seat and improve telephone communication experience of the customer, as shown in fig. 6, where the device may include:
the data acquisition module 01 is used for acquiring historical service record data of a client and sound characteristic parameters of the client after receiving a service request of the client;
the client business to be handled presumption module 02 is used for presuming the client business to be handled corresponding to the service request of the client according to the historical service record data of the client;
the personality characteristic parameter determining module 03 is used for determining personality characteristic parameters of the client according to the voice characteristic parameters of the client;
the bank service seat matching module 04 is used for recommending a neural network model based on the bank service seat, and determining the bank service seat matched with the client according to the presumed business to be handled of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of a bank customer service seat;
and the bank customer service seat access module 05 is used for accessing the bank customer service seat matched with the customer for the customer.
In one embodiment, the historical service record data of the customer includes historical service requests, historical dial-in records, historical chat content, historical transaction records and customer service records of the customer.
In one embodiment, as shown in fig. 7, further includes:
a neural network model modeling module 06 for:
establishing a bank customer service seat recommendation neural network model according to the following modes:
dividing historical recommendation data into a training data set and a test data set;
training the neural network model by using a training data set based on a back propagation algorithm and a genetic algorithm to obtain a trained bank customer service seat recommended neural network model;
and testing the trained neural network model recommended by the bank customer service seat by using the test data set.
In one embodiment, the historical recommendation data further includes: the business handling priority of different bank service agents and the historical data of the character characteristic parameters of different bank service agents.
In one embodiment, as shown in fig. 8, further includes:
the bank customer service seat characteristic determining module 07 is used for:
acquiring historical working records, sound characteristic parameters and working information of different bank customer service agents;
determining service handling priorities of different bank service agents according to the acquired historical working records of the different bank service agents;
and determining the character characteristic parameters of different bank customer service agents according to the acquired sound characteristic parameters and the acquired professional information of the different bank customer service agents.
In one embodiment, the bank customer service agent matching module is specifically configured to:
and generating the bank service seat matched with the client according to the deduced service to be handled of the client, the personality characteristic parameters of the client, the service handling priorities of different bank service seats and the personality characteristic parameters of different bank service seats on the basis of the neural network model recommended by the bank service seat.
In one embodiment, the data acquisition module is specifically configured to:
after receiving a service request of a client, searching a customer service seat which provides customer service for the client from historical service record data of the client;
if the customer service seat which provides the customer service for the customer is found, recommending the found customer service seat which provides the customer service for the customer to the customer;
and if the customer service seat which provides the customer service for the customer is not found, acquiring the historical service record data of the customer and the sound characteristic parameters of the customer.
In one embodiment, the customer confirmation module is specifically configured to:
and if the customer service seat which provides the customer service for the customer is found and confirmation information that the customer confirms to use the customer service seat is received, recommending the found customer service seat which provides the customer service for the customer to the customer.
In one embodiment, as shown in fig. 9, further includes:
a customer selection module 08 to:
if the number of the bank service agents matched with the client is multiple, the information of the bank service agents matched with the client is sent to the client;
receiving selection information of a plurality of bank customer service seats matched with customers from the customers;
the bank customer service seat access module is specifically used for:
and accessing the bank service seat selected by the customer for the customer according to the received selection information of the customer on the plurality of bank service seats matched with the customer.
An embodiment of the present invention provides a computer device for implementing all or part of contents in the bank customer service seat recommendation method, where the computer device specifically includes the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between related devices; the computer device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the computer device may be implemented with reference to the embodiment for implementing the method for recommending a bank customer service seat and the embodiment for implementing the device for recommending a bank customer service seat in the embodiments, which are incorporated herein, and repeated details are not repeated.
Fig. 10 is a schematic block diagram of a system configuration of a computer apparatus 1000 according to an embodiment of the present application. As shown in fig. 10, the computer apparatus 1000 may include a central processing unit 1001 and a memory 1002; the memory 1002 is coupled to the cpu 1001. Notably, this fig. 10 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the bank customer service agent recommendation function may be integrated into the central processor 1001. The cpu 1001 may be configured to perform the following control:
after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client;
presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data;
determining the character characteristic parameters of the client according to the sound characteristic parameters of the client;
based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of a bank customer service seat;
and accessing the bank customer service seat matched with the customer for the customer.
In another embodiment, the bank customer service seat recommendation device may be configured separately from the central processing unit 1001, for example, the bank customer service seat recommendation device may be configured as a chip connected to the central processing unit 1001, and the bank customer service seat recommendation function is realized by the control of the central processing unit.
As shown in fig. 10, the computer apparatus 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, a power supply 1007. It is noted that the computer device 1000 does not necessarily include all of the components shown in FIG. 10; furthermore, the computer device 1000 may also comprise components not shown in fig. 10, which can be referred to in the prior art.
As shown in fig. 10, the central processing unit 1001, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, and the central processing unit 1001 receives input and controls the operation of the various components of the computer apparatus 1000.
The memory 1002 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the cpu 1001 can execute the program stored in the memory 1002 to realize information storage or processing, or the like.
The input unit 1004 provides input to the cpu 1001. The input unit 1004 is, for example, a key or a touch input device. The power supply 1007 is used to supply power to the computer apparatus 1000. The display 1006 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 1002 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 1002 may also be some other type of device. Memory 1002 includes buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application/function storage part 1022, the application/function storage part 1022 being used for storing application programs and function programs or a flow for executing the operation of the computer device 1000 by the central processing unit 1001.
The memory 1002 may also include a data store 1023, the data store 1023 being used to store data such as contacts, digital data, pictures, sounds and/or any other data used by the computer device. Driver storage 1024 of memory 1002 may include various drivers for the computer device for communication functions and/or for performing other functions of the computer device (e.g., messaging applications, directory applications, etc.).
The communication module 1003 is a transmitter/receiver 1003 that transmits and receives signals via an antenna 1008. A communication module (transmitter/receiver) 1003 is coupled to the central processor 1001 to provide an input signal and receive an output signal, which may be the same as the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 1003, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same computer device. The communication module (transmitter/receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010 to implement general telecommunications functions. The audio processor 1005 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 1005 is also coupled to the central processor 1001, so that sound can be recorded locally through the microphone 1010, and so that locally stored sound can be played through the speaker 1009.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing the bank customer service seat recommendation method.
In the embodiment of the invention, after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client; presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data; determining the character characteristic parameters of the client according to the sound characteristic parameters of the client; based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled by a client, character characteristic parameters and historical data of a bank customer service seat; the bank customer service seat matched with the customer is accessed to the customer, so that after a service request of the customer is received, a neural network model is recommended based on the bank customer service seat, the customer is accessed to the bank customer service seat matched with the customer, the customer service seat can be matched in a personalized mode aiming at different customers, the problem that the customer seat and the customer are difficult to communicate due to the fact that the customer service seat is randomly distributed for the customer in the prior art is solved, the satisfaction degree of the customer on the customer service seat is improved, and telephone communication experience of the customer is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (20)
1. A bank customer service seat recommendation method is characterized by comprising the following steps:
after receiving a service request of a client, acquiring historical service record data of the client and sound characteristic parameters of the client;
presume the customer that the customer's service request corresponds to the business to be dealt with according to the customer's historical service record data;
determining the character characteristic parameters of the client according to the sound characteristic parameters of the client;
based on a bank service seat recommendation neural network model, determining a bank service seat matched with a client according to the deduced service to be transacted of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled of a client, character characteristic parameters and historical data of a bank customer service seat;
and accessing the bank customer service seat matched with the customer for the customer.
2. The method of claim 1, wherein the customer's historical service record data comprises the customer's historical service requests, historical dial-in records, historical chat content, historical transaction records, and customer service recordings.
3. The method of claim 1, further comprising:
establishing a bank customer service seat recommendation neural network model according to the following modes:
dividing historical recommendation data into a training data set and a test data set;
training the neural network model by using a training data set based on a back propagation algorithm and a genetic algorithm to obtain a trained bank customer service seat recommended neural network model;
and testing the trained neural network model recommended by the bank customer service seat by using the test data set.
4. The method of claim 1 or 3, wherein the historical recommendation data further comprises: the business handling priority of different bank service agents and the historical data of the character characteristic parameters of different bank service agents.
5. The method of claim 4, further comprising:
acquiring historical working records, sound characteristic parameters and working information of different bank customer service agents;
determining service handling priorities of different bank service agents according to the acquired historical working records of the different bank service agents;
and determining the character characteristic parameters of different bank customer service agents according to the acquired sound characteristic parameters and the acquired professional information of the different bank customer service agents.
6. The method of claim 5, wherein generating the bank service agent matching the customer based on the neural network model for recommendation of the bank service agent and the inferred customer pending business and the personality characteristic parameters of the customer comprises:
and generating the bank service seat matched with the client according to the deduced service to be handled of the client, the personality characteristic parameters of the client, the service handling priorities of different bank service seats and the personality characteristic parameters of different bank service seats on the basis of the neural network model recommended by the bank service seat.
7. The method of claim 1, wherein obtaining historical service record data of the customer and the sound characteristic parameters of the customer after receiving the service request of the customer comprises:
after receiving a service request of a client, searching a customer service seat which provides customer service for the client from historical service record data of the client;
if the customer service seat which provides the customer service for the customer is found, recommending the found customer service seat which provides the customer service for the customer to the customer;
and if the customer service seat which provides the customer service for the customer is not found, acquiring the historical service record data of the customer and the sound characteristic parameters of the customer.
8. The method of claim 7, wherein if a customer service seat that has served the customer is found, recommending the found customer service seat that has served the customer to the customer comprises:
and if the customer service seat which provides the customer service for the customer is found and confirmation information that the customer confirms to use the customer service seat is received, recommending the found customer service seat which provides the customer service for the customer to the customer.
9. The method of claim 1, further comprising:
if the number of the bank service agents matched with the client is multiple, the information of the bank service agents matched with the client is sent to the client;
receiving selection information of a plurality of bank customer service seats matched with customers from the customers;
accessing the bank customer service seat matched with the customer for the customer comprises the following steps:
and accessing the bank service seat selected by the customer for the customer according to the received selection information of the customer on the plurality of bank service seats matched with the customer.
10. A bank customer service agent recommendation device is characterized by comprising:
the data acquisition module is used for acquiring historical service record data of the client and sound characteristic parameters of the client after receiving a service request of the client;
the client pending service speculation module is used for predicting the client pending service corresponding to the service request of the client according to the historical service record data of the client;
the personality characteristic parameter determining module is used for determining the personality characteristic parameter of the client according to the sound characteristic parameter of the client;
the bank customer service seat matching module is used for recommending a neural network model based on the bank customer service seat, and determining the bank customer service seat matched with the client according to the estimated business to be handled of the client and the personality characteristic parameters of the client; the bank customer service seat recommending neural network model is obtained by training the neural network model according to historical recommending data, wherein the historical recommending data comprises the business to be handled of a client, character characteristic parameters and historical data of a bank customer service seat;
and the bank customer service seat access module is used for accessing the bank customer service seat matched with the customer for the customer.
11. The apparatus of claim 10, wherein the customer's historical service record data comprises the customer's historical service requests, historical dial-in records, historical chat content, historical transaction records, and customer service recordings.
12. The apparatus of claim 10, further comprising:
a neural network model modeling module to:
establishing a bank customer service seat recommendation neural network model according to the following modes:
dividing historical recommendation data into a training data set and a test data set;
training the neural network model by using a training data set based on a back propagation algorithm and a genetic algorithm to obtain a trained bank customer service seat recommended neural network model;
and testing the trained neural network model recommended by the bank customer service seat by using the test data set.
13. The apparatus of claim 10 or 12, wherein the historical recommendation data further comprises: the business handling priority of different bank service agents and the historical data of the character characteristic parameters of different bank service agents.
14. The apparatus of claim 13, further comprising:
the bank customer service seat characteristic determining module is used for:
acquiring historical working records, sound characteristic parameters and working information of different bank customer service agents;
determining service handling priorities of different bank service agents according to the acquired historical working records of the different bank service agents;
and determining the character characteristic parameters of different bank customer service agents according to the acquired sound characteristic parameters and the acquired professional information of the different bank customer service agents.
15. The apparatus of claim 14, wherein the bank customer service agent matching module is specifically configured to:
and generating the bank service seat matched with the client according to the deduced service to be handled of the client, the personality characteristic parameters of the client, the service handling priorities of different bank service seats and the personality characteristic parameters of different bank service seats on the basis of the neural network model recommended by the bank service seat.
16. The apparatus of claim 10, wherein the data acquisition module is specifically configured to:
after receiving a service request of a client, searching a customer service seat which provides customer service for the client from historical service record data of the client;
if the customer service seat which provides the customer service for the customer is found, recommending the found customer service seat which provides the customer service for the customer to the customer;
and if the customer service seat which provides the customer service for the customer is not found, acquiring the historical service record data of the customer and the sound characteristic parameters of the customer.
17. The apparatus of claim 16, wherein the data acquisition module is specifically configured to:
and if the customer service seat which provides the customer service for the customer is found and confirmation information that the customer confirms to use the customer service seat is received, recommending the found customer service seat which provides the customer service for the customer to the customer.
18. The apparatus of claim 10, further comprising: a customer selection module to:
if the number of the bank service agents matched with the client is multiple, the information of the bank service agents matched with the client is sent to the client;
receiving selection information of a plurality of bank customer service seats matched with customers from the customers;
the bank customer service seat access module is specifically used for:
and accessing the bank service seat selected by the customer for the customer according to the received selection information of the customer on the plurality of bank service seats matched with the customer.
19. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 9 when executing the computer program.
20. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 9.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113656605A (en) * | 2021-08-31 | 2021-11-16 | 北京沃东天骏信息技术有限公司 | Customer service distribution method and device |
| CN113656704A (en) * | 2021-08-31 | 2021-11-16 | 中国平安财产保险股份有限公司 | Insurance data processing method and equipment based on similarity matching and storage medium |
| CN113743824A (en) * | 2021-09-17 | 2021-12-03 | 中国银行股份有限公司 | Business consultation method and device |
| CN113902455A (en) * | 2021-10-29 | 2022-01-07 | 中国工商银行股份有限公司 | Customer service providing method and device |
| CN114529209A (en) * | 2022-02-23 | 2022-05-24 | 平安科技（深圳）有限公司 | User allocation method, device, equipment and storage medium |
| CN115099629A (en) * | 2022-06-28 | 2022-09-23 | 中国银行股份有限公司 | Customer service demand evaluation distribution method and system, electronic device and storage medium |
| WO2023125779A1 (en) * | 2021-12-31 | 2023-07-06 | 中兴通讯股份有限公司 | Model training method, customer service agent matching method, server, and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024217A (en) * | 2011-09-23 | 2013-04-03 | 中兴通讯股份有限公司 | Method for realizing customer service and customer service system |
| CN107330706A (en) * | 2017-06-06 | 2017-11-07 | 上海航动科技有限公司 | A kind of electricity battalion's customer service system and commercial operation pattern based on artificial intelligence |
| CN110766271A (en) * | 2019-09-10 | 2020-02-07 | 中国平安财产保险股份有限公司 | Customer service agent configuration method and device based on deep learning and computer equipment |
| CN111031185A (en) * | 2019-12-19 | 2020-04-17 | 易谷网络科技股份有限公司 | Agent allocation method based on artificial intelligence navigation and related device |
| CN111554304A (en) * | 2020-04-25 | 2020-08-18 | 中信银行股份有限公司 | User tag obtaining method, device and equipment |
| CN112862374A (en) * | 2021-03-31 | 2021-05-28 | 中国工商银行股份有限公司 | Customer service representative pushing method, device, equipment and medium |
-
2021
- 2021-06-18 CN CN202110677620.9A patent/CN113240444A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024217A (en) * | 2011-09-23 | 2013-04-03 | 中兴通讯股份有限公司 | Method for realizing customer service and customer service system |
| CN107330706A (en) * | 2017-06-06 | 2017-11-07 | 上海航动科技有限公司 | A kind of electricity battalion's customer service system and commercial operation pattern based on artificial intelligence |
| CN110766271A (en) * | 2019-09-10 | 2020-02-07 | 中国平安财产保险股份有限公司 | Customer service agent configuration method and device based on deep learning and computer equipment |
| CN111031185A (en) * | 2019-12-19 | 2020-04-17 | 易谷网络科技股份有限公司 | Agent allocation method based on artificial intelligence navigation and related device |
| CN111554304A (en) * | 2020-04-25 | 2020-08-18 | 中信银行股份有限公司 | User tag obtaining method, device and equipment |
| CN112862374A (en) * | 2021-03-31 | 2021-05-28 | 中国工商银行股份有限公司 | Customer service representative pushing method, device, equipment and medium |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113656605A (en) * | 2021-08-31 | 2021-11-16 | 北京沃东天骏信息技术有限公司 | Customer service distribution method and device |
| CN113656704A (en) * | 2021-08-31 | 2021-11-16 | 中国平安财产保险股份有限公司 | Insurance data processing method and equipment based on similarity matching and storage medium |
| CN113743824A (en) * | 2021-09-17 | 2021-12-03 | 中国银行股份有限公司 | Business consultation method and device |
| CN113902455A (en) * | 2021-10-29 | 2022-01-07 | 中国工商银行股份有限公司 | Customer service providing method and device |
| WO2023125779A1 (en) * | 2021-12-31 | 2023-07-06 | 中兴通讯股份有限公司 | Model training method, customer service agent matching method, server, and storage medium |
| CN114529209A (en) * | 2022-02-23 | 2022-05-24 | 平安科技（深圳）有限公司 | User allocation method, device, equipment and storage medium |
| CN114529209B (en) * | 2022-02-23 | 2024-09-10 | 平安科技（深圳）有限公司 | User allocation method, device, equipment and storage medium |
| CN115099629A (en) * | 2022-06-28 | 2022-09-23 | 中国银行股份有限公司 | Customer service demand evaluation distribution method and system, electronic device and storage medium |
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