CN112801676A - Express industry call center service method, device, equipment and system - Google Patents
Express industry call center service method, device, equipment and system Download PDFInfo
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Abstract
The invention relates to a method, a device, equipment and a system for serving a call center in an express industry. The method comprises the following steps: acquiring voice call data of a current session of a call center system in real time; converting voice call data into text data by using an automatic voice recognition function; predicting customer satisfaction by utilizing a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer; and when the customer satisfaction reaches a preset threshold value, sending a guidance request instruction to the leader end for customer service guidance. The method utilizes the satisfaction degree prediction model to predict the customer satisfaction degree, ensures that leaders can intervene customer service calls in time based on the customer satisfaction degree, improves the customer satisfaction degree and improves the service quality of the call center.
Description
Technical Field
The invention relates to the technical field of express calling service, in particular to a method, a device, equipment and a system for calling center service in the express industry.
Background
The express delivery industry call center provides incoming call service for the public, the problem of customer incoming call feedback relates to multiple links of collecting, transporting, dispatching, signing and the like, the current call center system is used as a bridge for communication between customer service and customers, the current call center system has a perfect high-level telephone traffic function, and the customer service and the customers can communicate through the call center system. Therefore, the professional degree of customer service in the call process directly influences the service quality.
At present, in order to ensure the communication quality, when a customer service answers a call from a client, a group leader can monitor the communication between the customer service and the client through a call center system, if the customer service needs guidance, the group leader informs the customer service through secret language operation, and even if necessary, the group leader can directly communicate with the client through call disconnection operation. In the method, the chief team adopts an active spot check mode, the operation of the mode has randomness, the problem of low spot check hit rate exists, and meanwhile, the chief team needs to monitor when and what to monitor, so that the situation that the telephone really needing the chief team guidance is not guided is easily caused, and the whole service quality of the call center is influenced.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, a device and a system for serving a call center in an express industry, which overcome the disadvantages of the prior art. The problem of current quality of service promotion inefficiency is solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a service method for a call center in express delivery industry comprises the following steps:
acquiring voice call data of a current session of a call center system in real time;
converting the voice call data into text data by using an automatic voice recognition function;
predicting customer satisfaction by utilizing a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer;
and when the customer satisfaction reaches a preset threshold value, sending a guidance request instruction to the leader end for customer service guidance.
Optionally, the training process of the satisfaction prediction model includes:
acquiring call data and self-evaluation customer satisfaction in a historical session process stored in a preset session database;
acquiring logistics information of a client corresponding to the historical conversation;
and training the satisfaction prediction model according to all the call data, the self-evaluation customer satisfaction and the logistics information.
Optionally, the training the satisfaction prediction model according to all the call data, the self-appraisal customer satisfaction and the logistics information includes:
determining the conversation data and the logistics information as independent variables, and the self-evaluation customer satisfaction as dependent variables;
fitting the independent variable and the dependent variable by using a polynomial regression algorithm, and obtaining a multivariate polynomial model as the satisfaction prediction model after training.
Optionally, the file data includes: client identity information and counseling sheet number information in the current session;
the process for acquiring the historical logistics information comprises the following steps:
sending a logistics information request instruction containing the customer identity information to a preset logistics database;
receiving all historical logistics information of the clients in the current session fed back by the logistics database;
and reading the historical logistics information corresponding to the consulting list number information from all the historical logistics information.
Optionally, the method further includes:
monitoring the running state of the communication equipment of the call center system in real time;
judging whether to start a call service function according to the running state;
if the call service function is started, the execution step acquires the voice call data of the current session of the call center system in real time.
Optionally, the acquiring, in real time, voice call data of a current session of the call center system includes:
sending a call monitoring starting instruction to monitoring equipment of the call center system to carry out session monitoring;
and receiving the voice call data of the current conversation fed back by the monitoring equipment in real time.
Optionally, the method further includes:
detecting the content of a set item of the text data;
determining whether data corresponding to the set item content in the text data is empty;
if not, the execution step predicts the customer satisfaction by using a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer;
and if the instruction is null, sending a manual application instruction function to the customer service end, so that the customer service end can manually send the instruction to the leading end.
An express delivery industry call center service device, comprising:
the voice call data acquisition module is used for acquiring the voice call data of the current session of the call center system in real time;
the text conversion module is used for converting the voice call data into text data by utilizing the automatic voice recognition function;
the satisfaction prediction module is used for predicting the satisfaction of the customer by utilizing a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer;
and the guiding instruction sending module is used for sending a guiding request instruction to the leader end for customer service guidance when the customer satisfaction reaches a preset threshold value.
An express delivery industry call center service device, comprising:
a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the express delivery industry call center service method;
the processor is used for calling and executing the computer program in the memory.
An express delivery industry call center service system, comprising: the system comprises a calling device, a monitoring device and the service device which is respectively in communication connection with the calling device and the monitoring device;
the monitoring equipment is also connected with the talking equipment.
The technical scheme provided by the application can comprise the following beneficial effects:
the application discloses a method, a device, equipment and a system for serving a call center in an express industry, wherein the method comprises the following steps: the method comprises the steps of obtaining voice call data of a current conversation of a call center system, converting the voice call data into text data by using an automatic voice recognition function, predicting the satisfaction degree of a client in the current conversation by using a satisfaction degree prediction model according to the text data and historical logistics information of the client, and sending a guidance request instruction to a leader end to remind the leader of call intervention if the satisfaction degree of the client reaches a preset threshold value and indicates that the leader needs to conduct customer service guidance at the moment. The method comprises the steps of converting voice call data of the current session into text data, predicting customer satisfaction by using a satisfaction prediction model according to the text data, and judging whether a leader needs to conduct customer service guidance or not according to the customer satisfaction.
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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.
Fig. 1 is a flowchart of a call center service method for the express industry according to an embodiment of the present invention;
FIG. 2 is a flow chart of a satisfaction prediction model training method provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a call center service method in the express industry according to an embodiment of the present invention;
fig. 4 is a block diagram of a service device of a call center in the courier industry according to an embodiment of the present invention;
fig. 5 is a structural diagram of a call center service device in the express industry according to an embodiment of the present invention;
fig. 6 is a structural diagram of a call center service system in the express industry according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Fig. 1 is a flowchart of a call center service method for the express industry according to an embodiment of the present invention. Referring to fig. 1, a service method for a call center in an express industry includes:
step 101: and acquiring voice call data of the current session of the call center system in real time. When the situation that the call equipment in the call center system is started is monitored, namely the customer service receives the incoming call feedback of the customer, the call service function of the application is started, and at the moment, the voice call data of the current session monitored by the monitoring equipment on the call equipment is acquired in real time, so that the function of acquiring the voice call data of the customer service and the customer is realized.
Step 102: and converting the voice call data into text data by using an automatic voice recognition function. During a call between a customer and a customer service, Speech data is converted into text data in real time using an Automatic Speech Recognition (ASR) technique, which is a technique for converting human Speech into text. In the process of conversation between the client and the customer service, the ASR technology is used for converting the conversation data between the client and the customer service into text data in real time, and a basis is provided for subsequent emotion recognition of the client and rapid understanding of conversation information by leaders.
Step 103: and predicting the customer satisfaction by utilizing a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer. After the text data of the current session is acquired, the customer satisfaction is predicted by using a satisfaction prediction model in combination with the historical logistics information of the customer.
When a client and a customer service are in communication, in order to understand the requirements of the client, the customer service consults the identity of the client and the information of the single number to be consulted, so that the converted text data comprises the identity information of the client and the information of the consulting single number, then the client identity information is sent to a logistics database to read all logistics information of the client, and then historical logistics information which is consistent with the information of the consulting single number is selected from all logistics information. After the historical logistics information is obtained, the satisfaction degree of the customer is predicted by combining text data and utilizing a satisfaction degree prediction model. It should be noted that the manner of acquiring the historical logistics information is not limited to acquiring the customer identity information and the counseling sheet number information, and may also be acquired in other feasible manners, which is determined according to the actual situation or the logistics information storage rule. For example: the corresponding historical logistics information can be obtained according to product information in the express package provided by the customer, and also the historical logistics information can be obtained according to time information related to express provided by the customer, wherein the time information can be delivery time of the express and can also be receipt time of the express.
Meanwhile, the satisfaction prediction model is trained through client call data, logistics information and client satisfaction data in historical conversation to obtain a multivariate polynomial model, and the model is the satisfaction prediction model.
Step 104: and when the customer satisfaction reaches a preset threshold value, sending a guidance request instruction to the leader end for customer service guidance. When the satisfaction degree prediction model predicts that the satisfaction degree of the client obtains a threshold value, the satisfaction degree prediction model indicates that the client is not satisfied with the current client service at the moment, and leadership intervention conversation is needed to improve the satisfaction degree of the client. The degree of satisfaction here can be expressed by numerical values, for example: the satisfaction degree is 80%; satisfaction ratings may also be expressed, for example: substantially satisfactory, fully satisfactory or unsatisfactory.
In the embodiment, the data content of the voice call of the current session between the client and the customer service is converted into the text data, then the satisfaction degree of the client is predicted by using the satisfaction degree prediction model in combination with the text data and the historical logistics information, and when the satisfaction degree is low, the leader is reminded to intervene in the call, so that the satisfaction degree of the client is improved, and the service quality of the call center is improved.
On the basis of the above embodiments, in the present application, the content of the text data is also detected accurately, and first, several conventional item variables are preset, for example: customer identity information, express bill numbers, consultation questions and the like. And then determining whether the content of the conventional item in the text data is empty, if the content of the conventional item is empty, the satisfaction degree prediction cannot be performed through the text data due to the lack of necessary data, and the judgment on whether leadership intervention is needed cannot be performed. The embodiment is provided with a manual application guidance function, so that the situation that the call intervention cannot be carried out in time when the customer satisfaction cannot be accurately judged due to content loss in the text data is avoided. The leader can intervene in the current session in time under any condition, and the timeliness of the leader in intervening the current session is greatly improved.
In the above embodiment, a method for predicting the customer satisfaction by using a satisfaction prediction model and then performing call guidance intervention on the current session is described. On the basis of the above embodiments, the present application also discloses a process for training a satisfaction prediction model, which specifically includes the following steps:
fig. 2 is a flowchart of a satisfaction prediction model training method according to an embodiment of the present invention. Referring to fig. 2, the process of training the satisfaction prediction model includes:
step 201: and acquiring call data and self-evaluation customer satisfaction in the historical conversation process stored in a preset conversation database. In the service center system, after each session is finished, the call data related in the session process can be stored in the session database, meanwhile, customer service personnel can also perform manual evaluation on the customer satisfaction degree in the session process to obtain the self-evaluation satisfaction degree, and the self-evaluation satisfaction degree is stored in the corresponding session database for later reference or archiving.
Step 202: and acquiring logistics information of a client corresponding to the historical conversation. In the session process, the customer service can acquire the identity information and the like of the customer through consultation, and the logistics information of the customer can be acquired from the logistics database of the express company through the identity information.
Step 203: and determining the call data and the logistics information as independent variables, and the self-evaluation customer satisfaction as dependent variables.
Step 204: fitting the independent variable and the dependent variable by using a polynomial regression algorithm, and obtaining a multivariate polynomial model as the satisfaction prediction model after training. And taking the call data and the logistics information as independent variables, taking the self-evaluation customer satisfaction as a dependent variable, and performing multiple training by using a polynomial regression algorithm to obtain a multivariate polynomial model, wherein the model is a satisfaction prediction model.
The above embodiment discloses a specific process for training a satisfaction degree prediction model, and it should be noted that the types of data involved in the above training process are not fixed, and the satisfaction degree prediction model is not necessarily trained through the above three types of data, and models with other functions can be trained according to actual conditions.
In the embodiment, the satisfaction degree prediction model is trained by utilizing the multiple times of historical session data, so that the customer satisfaction degree in the session process is predicted by the model, and then an accurate basis can be provided for informing the leader of performing the call intervention, so that the effectiveness of the call intervention is improved.
For more detailed description of the implementation process of the call center service method in the present application, detailed description is given by way of example, and specifically, the following is provided:
fig. 3 is a schematic diagram of a call center service method in the express industry according to an embodiment of the present invention. Referring to fig. 3, a customer a initiates a customer service request through a telephone, after the customer a makes a call to a call center, an operator establishes a connection between the customer a and the call center, the call center is provided with a plurality of customer service staff, and at this time, the call is distributed to a customer service B to answer according to a set rule of the call center of an express company, so as to establish a call connection between the customer a and the customer service B. Meanwhile, when the call connection between the monitoring equipment and the call center is established, the monitoring equipment on the call equipment and the service equipment of the call center are started. The monitoring equipment monitors the conversation, can acquire voice call data between the client A and the client service B in real time, then transmits the voice call data to the service equipment in real time, the service equipment pushes the voice call data to the satisfaction degree prediction server, the satisfaction degree prediction server submits the voice call data to the voice recognition server, and the voice recognition server converts the voice call data into text data by utilizing an automatic voice recognition function and returns the text data to the satisfaction degree prediction server. And after the satisfaction prediction server receives the text data, historical logistics information of the client A in the current session is called from the logistics information database, the satisfaction prediction model obtained from the satisfaction training server is used for performing satisfaction prediction on the client A in combination with the historical logistics information and the text data to obtain the satisfaction of the client, the satisfaction of the client is returned to the service equipment of the call center, and the service equipment pushes the satisfaction of the client to the client service B and the leader so as to remind the leader of call intervention.
In the process, the call center stores the call data and the self-evaluation customer satisfaction in each session process into a historical call database. The satisfaction training server can acquire the call data of the historical conversation and the self-evaluation customer satisfaction data from the historical call database, and then sends the call data to the voice recognition server for text conversion to obtain text data. And then, the satisfaction degree training server calls the logistics information of the client in each historical conversation from the logistics information database, and trains by using a large amount of text data, logistics information and self-evaluated client satisfaction degree data to obtain a satisfaction degree prediction model.
In the service process of the call center, the service equipment of the call center has the following functions: the method provides basic call service for the client and the customer service, provides advanced telephone traffic functions such as monitoring, whisper and inter-cut, distributes the customer telephone traffic to the customer service, and pushes the customer satisfaction to the customer service and leaders.
It should be noted that, in the foregoing embodiment, the multiple servers and the service device may be the same server in an actual application process, and the functions of each server and the functions of the service device may all be implemented on the same device, which is described here for more clearly describing the implementation process of the service method of the present application, and the specific implementation process may be determined according to actual situations.
In the embodiment, the predicted customer satisfaction is fed back to the current service customer service in real time so as to placate the customer in time and improve the service quality, meanwhile, the predicted data is fed back to the team leader in real time, and a recommendation for intervening the call is given according to the result, so that the decision time of the team leader is saved, the team leader is enabled to spend main efforts on improving the service quality, and a basis is provided for improving the overall service quality.
Corresponding to the express industry call center service method provided by the embodiment of the invention, the embodiment of the invention also provides an express industry call center service device. Please see the examples below.
Fig. 4 is a block diagram of a service device of a call center in the courier industry according to an embodiment of the present invention. Referring to fig. 4, an express delivery industry call center service apparatus includes:
the voice call data obtaining module 401 is configured to obtain voice call data of a current session of the call center system in real time.
A text conversion module 402, configured to convert the voice call data into text data by using the automatic voice recognition function.
And a satisfaction prediction module 403, configured to predict customer satisfaction using a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer. Wherein the file data includes: client identity information and counseling sheet number information in the current session; the acquisition process of the historical logistics information comprises the following steps: sending a logistics information request instruction containing the customer identity information to a preset logistics database; receiving all historical logistics information of the clients in the current session fed back by the logistics database; and reading the historical logistics information corresponding to the consulting list number information from all the historical logistics information.
And a guidance instruction sending module 404, configured to send a guidance request instruction to the leader to perform customer service guidance when the customer satisfaction reaches a preset threshold.
The voice call data acquisition module 401 is specifically configured to: sending a call monitoring starting instruction to monitoring equipment of the call center system to carry out session monitoring; and receiving the voice call data of the current conversation fed back by the monitoring equipment in real time.
Furthermore, on the basis of the above device, the device of the present application further includes:
and the historical conversation data acquisition module is used for acquiring the conversation data and the self-evaluation customer satisfaction in the historical conversation process stored in a preset conversation database.
And the historical logistics information acquisition module is used for acquiring the logistics information of the client corresponding to the historical session.
And the variable determining module is used for determining that the call data and the logistics information are independent variables and the self-evaluation customer satisfaction is a dependent variable.
And the model training module is used for fitting the independent variable and the dependent variable by utilizing a polynomial regression algorithm, and obtaining a multivariate polynomial model as the satisfaction prediction model after training.
And the monitoring module is used for monitoring the running state of the call equipment of the call center system in real time.
The service function starting module is used for judging whether to start the call service function according to the running state; if the call service function is enabled, go to step 401.
And the item content detection module is used for detecting the set item content of the text data.
And the data content judging module is used for determining whether the corresponding data of the set item content in the text data is empty or not.
And an automatic prediction starting module, configured to execute step 403 if the corresponding data of the set item content is not empty.
And the manual request intervention module is used for sending a manual application instruction function to the customer service end if the corresponding data of the set project content is null, so that the customer service can manually send the instruction to the leader.
The device of the embodiment can predict customer satisfaction in real time, provide service reference for customer service and a team leader, and actively inform the team leader to intervene in conversation when the customer service is poor, so that the decision time of the team leader is saved, and the service quality is improved.
In order to more clearly introduce the hardware device for implementing the embodiment of the present invention, the embodiment of the present invention further provides an express industry call center service device and system, corresponding to the express industry call center service method provided by the embodiment of the present invention. Please see the examples below.
Fig. 5 is a structural diagram of a call center service device in the express industry according to an embodiment of the present invention. Referring to fig. 5, an express industry call center service apparatus includes:
a processor 501, and a memory 502 connected to the processor 501;
the memory 502 is used for storing a computer program, and the computer program is at least used for executing the express industry call center service method;
the processor 501 is used for calling and executing the computer program in the memory 502.
Fig. 6 is a structural diagram of a call center service system in the express industry according to an embodiment of the present invention. Referring to fig. 6, a service system for a call center in express delivery industry includes:
a calling device 601, a monitoring device 602, and the service device 603, which is in communication connection with the calling device 601 and the monitoring device 602 respectively;
the listening device 602 is further connected to the telephony device 601.
Meanwhile, the application also discloses a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the express industry call center service method are realized.
The equipment and the system are combined with a service scene of a call center in the express industry, can predict customer satisfaction, feed back to the current customer service in real time so as to placate customers in time and inform the chief to intervene in conversation, and provide a basis for improving customer satisfaction and improving the overall service quality of the call center.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A service method for a call center in express delivery industry is characterized by comprising the following steps:
acquiring voice call data of a current session of a call center system in real time;
converting the voice call data into text data by using an automatic voice recognition function;
predicting customer satisfaction by utilizing a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer;
and when the customer satisfaction reaches a preset threshold value, sending a guidance request instruction to the leader end for customer service guidance.
2. The method of claim 1, wherein the training process of the satisfaction prediction model comprises:
acquiring call data and self-evaluation customer satisfaction in a historical session process stored in a preset session database;
acquiring logistics information of a client corresponding to the historical conversation;
and training the satisfaction prediction model according to all the call data, the self-evaluation customer satisfaction and the logistics information.
3. The method of claim 2, wherein said training said satisfaction prediction model based on all of said call data, said self-rated customer satisfaction, and said logistics information comprises:
determining the conversation data and the logistics information as independent variables, and the self-evaluation customer satisfaction as dependent variables;
fitting the independent variable and the dependent variable by using a polynomial regression algorithm, and obtaining a multivariate polynomial model as the satisfaction prediction model after training.
4. The method of claim 1, wherein the file data comprises: client identity information and counseling sheet number information in the current session;
the process for acquiring the historical logistics information comprises the following steps:
sending a logistics information request instruction containing the customer identity information to a preset logistics database;
receiving all historical logistics information of the clients in the current session fed back by the logistics database;
and reading the historical logistics information corresponding to the consulting list number information from all the historical logistics information.
5. The method of claim 1, further comprising:
monitoring the running state of the communication equipment of the call center system in real time;
judging whether to start a call service function according to the running state;
if the call service function is started, the execution step acquires the voice call data of the current session of the call center system in real time.
6. The method of claim 1, wherein the obtaining voice call data of the current session of the call center system in real time comprises:
sending a call monitoring starting instruction to monitoring equipment of the call center system to carry out session monitoring;
and receiving the voice call data of the current conversation fed back by the monitoring equipment in real time.
7. The method of claim 1, further comprising:
detecting the content of a set item of the text data;
determining whether data corresponding to the set item content in the text data is empty;
if not, the execution step predicts the customer satisfaction by using a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer;
and if the instruction is null, sending a manual application instruction function to the customer service end, so that the customer service end can manually send the instruction to the leading end.
8. An express delivery industry call center service device, comprising:
the voice call data acquisition module is used for acquiring the voice call data of the current session of the call center system in real time;
the text conversion module is used for converting the voice call data into text data by utilizing the automatic voice recognition function;
the satisfaction prediction module is used for predicting the satisfaction of the customer by utilizing a pre-trained satisfaction prediction model according to the text data and pre-acquired historical logistics information of the customer;
and the guiding instruction sending module is used for sending a guiding request instruction to the leader end for customer service guidance when the customer satisfaction reaches a preset threshold value.
9. An express delivery industry call center service equipment, comprising:
a processor, and a memory coupled to the processor;
the memory for storing a computer program for performing at least the courier industry call center service method of any of claims 1-7;
the processor is used for calling and executing the computer program in the memory.
10. An express delivery industry call center service system, comprising:
a calling device, a monitoring device, and the service device as claimed in claim 9, which is in communication connection with the calling device and the monitoring device, respectively;
the monitoring equipment is also connected with the talking equipment.
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