CN116170541A - Method, device, equipment and storage medium for distributing agents for manual call transfer - Google Patents

Method, device, equipment and storage medium for distributing agents for manual call transfer Download PDF

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Publication number
CN116170541A
CN116170541A CN202211600254.8A CN202211600254A CN116170541A CN 116170541 A CN116170541 A CN 116170541A CN 202211600254 A CN202211600254 A CN 202211600254A CN 116170541 A CN116170541 A CN 116170541A
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China
Prior art keywords
service
intention
agent
type
client
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CN202211600254.8A
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Chinese (zh)
Inventor
李良斌
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Beijing SoundAI Technology Co Ltd
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Beijing SoundAI Technology Co Ltd
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Priority to CN202211600254.8A priority Critical patent/CN116170541A/en
Publication of CN116170541A publication Critical patent/CN116170541A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
    • H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1822—Parsing for meaning understanding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/22—Arrangements for supervision, monitoring or testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
    • H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
    • H04M3/523—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing with call distribution or queueing

Abstract

The disclosure relates to a manual agent distribution method, device and equipment for outbound call transfer and a storage medium. According to the embodiment of the disclosure, the voice information of the client is received in response to the call answering of the client; carrying out service intention analysis based on the voice information, and determining the intention service type of the client; in a target service seat group corresponding to the preset intention service type, a target service seat corresponding to the intention service type is allocated for the client, so that the intention service of the client can be intelligently identified according to the voice information of the client, and the service seat corresponding to the intention service is allocated for the client at the first time, thereby greatly shortening the waiting time for switching the intention service seat of the client, improving the switching success rate and accuracy and improving the use experience of the user.

Description

Method, device, equipment and storage medium for distributing agents for manual call transfer
Technical Field
The disclosure relates to the technical field of communication, and in particular relates to a method, a device, equipment and a storage medium for distributing agents for outward call transfer manual work.
Background
In the existing intelligent outbound system, when the service of the artificial seat is required to be changed after outbound, a personal seat is generally randomly allocated to a customer, and after the seat is answered, both parties successfully enter into service conversation, so that the function of abutting the customer by the personal seat is realized.
However, the existing manual agent distribution mode for outbound switching does not consider the specific service requirements of the clients, if the distributed manual agents are not service agents corresponding to the service requirements of the clients, the services are not corresponding, and the agents corresponding to the services are required to be searched and switched, so that the waiting time of the clients and the time cost of agent scheduling are greatly increased, and the user experience is reduced.
Disclosure of Invention
In order to solve the technical problems, the present disclosure provides a method, a device, equipment and a storage medium for distributing agents for outbound switching manual work.
A first aspect of an embodiment of the present disclosure provides a method for distributing an agent for outbound switching, where the method includes:
receiving voice information of a customer in response to the customer answering a call;
carrying out service intention analysis based on the voice information, and determining the intention service type of the client;
and distributing the target service agents corresponding to the intention service types to the clients in the target service agent groups corresponding to the preset intention service types.
A second aspect of an embodiment of the present disclosure provides an outbound manual agent distribution device, including:
the receiving module is used for responding to the call answering of the client and receiving the voice information of the client;
the intention analysis module is used for carrying out business intention analysis based on the voice information and determining the intention business type of the client;
the first distribution module is used for distributing target service agents corresponding to the intention service types for clients in target service agent groups corresponding to the preset intention service types.
A third aspect of the embodiments of the present disclosure provides a computer apparatus, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for assigning an agent for outbound call forwarding manual according to the first aspect may be implemented.
A fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium, in which a computer program is stored, which when executed by a processor, can implement the outbound call manual agent allocation method of the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the embodiment of the disclosure, the voice information of the client is received in response to the call answering of the client; carrying out service intention analysis based on the voice information, and determining the intention service type of the client; in a target service seat group corresponding to the preset intention service type, a target service seat corresponding to the intention service type is allocated for the client, so that the intention service of the client can be intelligently identified according to the voice information of the client, and the service seat corresponding to the intention service is allocated for the client at the first time, thereby greatly shortening the waiting time for switching the intention service seat of the client, improving the switching success rate and accuracy and improving the use experience of the user.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of an outbound manual seat allocation method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a manual agent allocation method for outbound switching according to an embodiment of the present disclosure;
fig. 3 is a flowchart of another manual agent distribution method for outbound switching provided in an embodiment of the present disclosure;
FIG. 4 is a flow chart of yet another manual agent distribution method for outbound switching provided by embodiments of the present disclosure;
fig. 5 is a schematic diagram of another manual seat allocation method for outbound switching according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an outbound manual seat distribution device according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
In the existing intelligent outbound system, when the service of the artificial seat is required to be changed after outbound, a personal seat is generally randomly allocated to a customer, and after the seat is answered, both parties successfully enter into service conversation, so that the function of abutting the customer by the personal seat is realized. For example, as shown in fig. 1, fig. 1 is a schematic diagram of an outbound switching manual agent distribution method, an intelligent outbound system initiates an outbound to a client, the client triggers the intelligent outbound system to switch to a manual agent after switching on, and the intelligent outbound system switches to a manual agent in an agent group according to a sequential distribution strategy and a sequential switching-on method, so as to realize the function of the manual agent for abutting the client.
However, the existing manual agent distribution mode for outbound switching does not consider the specific service requirements of the clients, if the distributed manual agents are not service agents corresponding to the service requirements of the clients, the services are not corresponding, and the agents corresponding to the services are required to be searched and switched, so that the waiting time of the clients and the time cost of agent scheduling are greatly increased, and the user experience is reduced.
Aiming at the defects of the related art in the aspect of the external call transfer manual seat distribution, the embodiment of the disclosure provides an external call transfer manual seat distribution method, device, equipment and storage medium, which can intelligently identify the intention service of a client according to the voice information of the client, and distribute the service seat corresponding to the intention service for the client in the first time, thereby greatly shortening the waiting time for transferring the client intention service seat, improving the transfer success rate and accuracy and improving the use experience of the user.
The outbound manual agent allocation method provided by the embodiments of the present disclosure may be performed by a computer device, which may be understood as any device having processing and computing capabilities, including, but not limited to, mobile terminals such as smartphones, notebook computers, personal Digital Assistants (PDAs), tablet computers (PADs), portable Multimedia Players (PMPs), etc., and stationary electronic devices such as digital TVs, desktop computers, etc.
In order to better understand the inventive concepts of the embodiments of the present disclosure, the technical solutions of the embodiments of the present disclosure are described below in conjunction with exemplary embodiments.
Fig. 2 is a flowchart of an outbound manual agent allocation method provided in an embodiment of the present disclosure, and as shown in fig. 2, the outbound manual agent allocation method provided in the embodiment may include steps 210 to 230:
step 210, receiving the voice information of the customer in response to the customer answering the call.
In the embodiment of the disclosure, an intelligent outbound system is installed in the computer equipment, and after a call is made to a customer by the intelligent outbound system, the intelligent outbound system can respond to the answering of the customer to receive the voice information of the customer.
And 220, carrying out service intention analysis based on the voice information, and determining the intention service type of the client.
In the embodiment of the present disclosure, the service type may be understood as a type of service, and may include a general service consultation type, an order service consultation type, a price service consultation type, and the like, but is not limited thereto. The intended traffic type may be understood as the traffic type required by the customer.
In the embodiment of the disclosure, after receiving the voice information of the client, the computer device may perform service intention analysis based on the voice information of the client, and determine the intention service type of the client.
In some embodiments, performing business intent analysis based on voice information, determining the intent business type of the customer may include steps 2201-2202:
step 2201, performing service keyword recognition on the voice information to obtain service keywords corresponding to each service type in the voice information.
In the embodiment of the disclosure, the business keyword may be understood as a word corresponding to the business type, for example, the business keyword corresponding to the general business consultation type may include a product parameter, a product function, a use instruction, and the like; the business keywords corresponding to the order business consultation types can comprise product recommendation, logistics, activity introduction, quality assurance and the like; the business keywords corresponding to the price business consultation type can include price, amount, preference, etc.
In the embodiment of the disclosure, after receiving the voice information of the client, the computer device may identify the service keywords of the voice information, so as to obtain the service keywords corresponding to each service type in the voice information. For example, the voice information can be converted into text information through an intelligent voice recognition algorithm, and then the business keywords in the text information are recognized to obtain the business keywords corresponding to each business type in the voice information.
Step 2202, performing intent analysis based on the business keywords, and determining the intent business type of the client.
In the embodiment of the disclosure, after obtaining the service keywords corresponding to each service type in the voice information of the client, the computer device may perform intent analysis based on the service keywords to determine the intent service type of the client.
In some embodiments, the determining the intent business type of the customer based on the business keywords may include S11-S12:
s11, counting the first number of business keywords corresponding to each business type.
In the embodiment of the disclosure, the computer device may count a first number of service keywords corresponding to each service type in the voice information.
And S12, when the first quantity is larger than a preset threshold, determining the service type corresponding to the first quantity as the intention service type.
In the embodiment of the disclosure, the computer device may determine whether the first number of service keywords corresponding to each service type is greater than a preset threshold, and when the first number is greater than the preset threshold, may determine the service type corresponding to the first number as the intended service type of the client. The preset threshold may be set as needed, and is not particularly limited herein.
In other embodiments, the determining the intent business type of the client based on the intent analysis of the business keywords may include S21-S22:
s21, counting second numbers of business keywords corresponding to the business types in a first preset duration.
In the embodiment of the disclosure, the computer device may count the second number of the service keywords corresponding to each service type within the first preset duration. The first preset duration may be set as needed, which is not specifically limited herein.
S22, determining the corresponding service type as the intention service type when the second number is larger than a preset threshold value.
In the embodiment of the disclosure, the computer device may determine whether the second number of the service keywords corresponding to each service type in the first preset duration is greater than a preset threshold, and when the second number is greater than the preset threshold, may determine the service type corresponding to the second number greater than the preset threshold as the intention service type.
Step 230, distributing target service agents corresponding to the intention service types for the clients in the service agent groups corresponding to the preset intention service types.
In the embodiment of the disclosure, preset service agent groups corresponding to each service type are stored in the computer device in advance, and each service agent group at least comprises one service agent.
In the embodiment of the disclosure, after the intention business type of the client is obtained, the computer device may determine a target business agent group corresponding to the intention business type in the preset business agent groups corresponding to the business types, and allocate the target business agent corresponding to the intention business type for the client from the target business agent group.
According to the embodiment of the disclosure, the voice information of the client is received in response to the call answering of the client; carrying out service intention analysis based on the voice information, and determining the intention service type of the client; in a target service seat group corresponding to the preset intention service type, a target service seat corresponding to the intention service type is allocated for the client, so that the intention service of the client can be intelligently identified according to the voice information of the client, and the service seat corresponding to the intention service is allocated for the client at the first time, thereby greatly shortening the waiting time for switching the intention service seat of the client, improving the switching success rate and accuracy and improving the use experience of the user.
Fig. 3 is a flowchart of an outbound manual agent allocation method provided by an embodiment of the present disclosure, and as shown in fig. 3, the outbound manual agent allocation method provided by the embodiment may include steps 310 to 360:
step 310, receiving the voice information of the customer in response to the customer answering the call.
Step 320, performing service intention analysis based on the voice information, and determining the intention service type of the client.
Steps 310-320 in the embodiments of the present disclosure may refer to the content of steps 210-220 described above, and will not be described herein.
Step 330, determining a target service seat group corresponding to the intention service type in the service seat groups corresponding to the preset service types.
In the embodiment of the disclosure, after determining the intention business type of the client, the computer device may determine, in the preset business agent groups corresponding to the business types, a target business agent group corresponding to the intention business type.
And 340, judging whether available agents in an idle state exist in the target service agent group.
In the embodiment of the disclosure, the idle state may be understood as a service agent that does not answer a task and is in an online state; the available agents can be understood as service agents capable of performing normal answering tasks, that is, service agents meeting normal answering requirements in hardware and software levels, for example, call lines of the agents are connected normally and call terminals of the agents are terminals successfully registered in the intelligent outbound system. The on-line state can be understood as a state that the seat personnel are in the working position and can execute the answering task at any time, and the off-line state can be understood as a state that the seat personnel are not in the working position and cannot execute the answering task. In some embodiments, an image of the operator's work position may be acquired by a camera, and by identifying the image of the work position, it is determined whether the operator is on the work position, i.e., whether the operator is online.
Because the agents in the answering state or the offline state may exist in the target service agent group, the outbound switching is manually failed, and the next agent allocation is triggered after the failure until the switching to the available agents in the idle state is continued, but the switching time and the switching task failure risk are increased. Therefore, in the embodiment of the present disclosure, after determining the target service agent group corresponding to the intent service type of the client, it may be determined whether there is an available agent in the idle state in the target service agent group.
And 350, if the available agents in the idle state exist in the target service agent group, selecting any available agent in the idle state from the target service agent group as the target service agent.
In the embodiment of the disclosure, if an available agent in an idle state exists in the target service agent group, the computer device may select any available agent in an idle state from the target service agent group as the target service agent.
Step 360, if no available agent in the idle state exists in the target service agent group, waiting for any available agent in the idle state to be selected from the target service agent group as the target service agent when the available agent in the idle state exists in the target service agent group.
In the embodiment of the disclosure, if no available agent in the idle state exists in the target service agent group, the computer device may wait for any available agent in the idle state to be selected from the target service agent group as the target service agent when the available agent in the idle state exists in the target service agent group.
Therefore, the available agents in the idle state can be allocated to the clients as the target service agents by sensing the states of the agents in the target service agent group, the waiting time for transferring the client intention service agents can be greatly shortened, the transfer success rate and the accuracy rate are improved, and the use experience of the users is improved.
Fig. 4 is a flowchart of an outbound manual agent allocation method provided by an embodiment of the present disclosure, as shown in fig. 4, the outbound manual agent allocation method provided by the embodiment may include steps 410 to 460:
step 410, receiving the voice information of the customer in response to the customer answering the call.
The steps of the embodiments of the present disclosure may refer to the content of step 210, which is not described herein.
Step 420, judging whether available agents in an idle state exist in service agent groups of all service types preset at the current moment.
In the embodiment of the disclosure, the computer device may determine whether an available agent in an idle state exists in a service agent group of each service type preset at the current time.
Step 430, if there are available agents in idle state in the service agent group of each service type preset at the current moment, performing service intention analysis based on the voice information, and determining the intention service type of the client.
In the embodiment of the disclosure, if an available agent in an idle state exists in a service agent group of each service type preset at the current moment, the computer device may perform service intent analysis based on the voice information, and determine the intent service type of the client.
Step 440, if no available agents in the idle state exist in the service agent group of each service type preset at the current moment, waiting for service intention analysis based on the voice information when the available agents in the idle state exist in the service agent group of each service type, and determining the intention service type of the client.
In the embodiment of the disclosure, if no available agent in an idle state exists in the service agent group of each service type preset at the current moment, the computer device may wait for the available agent in the idle state in the service agent group of each service type, and then perform service intent analysis based on the voice information to determine the intent service type of the client.
And 450, determining a target service seat group corresponding to the intention service type in the service seat groups corresponding to the preset service types.
Step 460, judging whether available agents in an idle state exist in the target service agent group.
Step 470, if there is an available agent in the idle state in the target service agent group, selecting any available agent in the idle state from the target service agent group as the target service agent.
Step 480, if no available agent in the idle state exists in the target service agent group, selecting any available agent in the idle state from the target service agent group as the target service agent when the available agent in the idle state exists in the target service agent group.
Steps 450-480 of the embodiments of the present disclosure may refer to the content of steps 330-360 described above, and are not described herein.
Therefore, after the available agents in the idle state exist in the service agent groups of each service type, service intention analysis is performed based on voice information, the intention service type of the client is determined, calculation resources can be saved, service intention analysis efficiency is improved, the available agents in the idle state are allocated to the client as target service agents by sensing the states of the agents in the target service agent groups, waiting time for transferring the client intention service agents can be greatly shortened, transfer success rate and accuracy are improved, and user experience is improved.
In some embodiments of the present disclosure, selecting any available agent in an idle state from the target service agent group as the target service agent may include S31-S32:
s31, if at least two available agents in the idle state exist in the target service agent group, determining the answered workload of each agent in the idle state within a second preset duration, wherein the answered workload comprises the answered duration or the number of times of answering.
In the embodiment of the disclosure, the answered workload may be understood as the answered duration or the number of times of answering of the seat.
In the embodiment of the disclosure, if at least two available agents in the idle state exist in the target service agent group, the computer device may determine the received workload of each agent in the available agents in the idle state within a second preset duration. The second preset time period may be set as needed, and is not specifically limited herein.
S32, determining the available seat with the least answered workload in the idle state as a target service seat.
In the embodiment of the disclosure, the computer device may determine an available agent with the least amount of answered workload among available agents in an idle state in the target service agent group, and determine the available agent in the idle state with the least amount of answered workload as the target service agent.
Therefore, the available agents with the least answering workload in the target service agent group and in the idle state can be selected as target service agents, the workload of each service agent can be reasonably distributed, and the working efficiency of the agents is further improved.
In other embodiments of the present disclosure, before receiving the voice information of the customer in response to the customer answering the call, S41-S42 may be included:
s41, creating service seat groups corresponding to the service types.
In the embodiment of the disclosure, before receiving the voice information of the client in response to the client answering the call, the computer device may create a service agent group corresponding to each service type.
S42, based on the service type of each service seat group, distributing the service seat and the service keyword corresponding to the service type of at least one service seat group for each service seat group.
In the embodiment of the disclosure, after creating service agent groups corresponding to each service type, the computer device groups service agents corresponding to the service types of at least one service agent group for each service agent group based on the service types of each service agent group, and groups service keywords corresponding to the service types of at least one service agent group for each service agent group.
For example, as shown in fig. 5, fig. 5 is a schematic diagram of an outbound manual agent allocation method, an intelligent outbound system initiates an outbound to a client, and after the client is connected, triggers a task of the intelligent outbound system to transfer a manual agent, and the intelligent outbound system transfers the client to a certain service agent in a corresponding service agent group according to an intelligent queuing policy, that is, according to any outbound manual agent allocation method in the above embodiments of the disclosure, according to an intention service type of the client, so as to realize a function of the manual agent for docking the client.
Fig. 6 is a schematic structural diagram of an outbound manual seat distribution device according to an embodiment of the present disclosure, which may be understood as the above-mentioned computer device or a part of functional modules in the above-mentioned computer device. As shown in fig. 6, the outbound manual agent distribution device 600 may include:
a receiving module 610, configured to receive voice information of a client in response to a call received by the client;
the intent analysis module 620 is configured to perform intent analysis of the service based on the voice information, and determine an intent service type of the client;
the first allocation module 630 is configured to allocate, for the client, a target service agent corresponding to the intention service type in a target service agent group corresponding to the preset intention service type.
Optionally, the intent analysis module 620 may include:
the recognition sub-module is used for carrying out service keyword recognition on the voice information to obtain service keywords corresponding to each service type in the voice information;
and the first determining submodule is used for carrying out intent analysis based on the business keywords and determining the intent business type of the client.
Optionally, the first determining sub-module may include:
the first statistics unit is used for counting the first quantity of the business keywords corresponding to each business type;
and the first determining unit is used for determining the service type corresponding to the first number as the intention service type when the first number is larger than a preset threshold value.
Optionally, the first determining sub-module may include:
the second statistics unit is used for counting the second quantity of the business keywords corresponding to each business type in the first preset duration;
and the second determining unit is used for determining the corresponding service type as the intention service type when the second number is larger than the preset threshold value.
Optionally, the intent analysis module 620 may include:
the first judging submodule is used for judging whether available agents in an idle state exist in service agent groups of all service types preset at the current moment;
the second determining submodule is used for carrying out service intention analysis based on the voice information if the voice information exists and determining the intention service type of the client;
and the first waiting sub-module is used for waiting for service intention analysis based on the voice information when the available agents in the idle state exist in the service agent groups of each service type if the available agents do not exist, and determining the intention service type of the client.
Optionally, the first allocation module 630 may include:
a third determining submodule, configured to determine a target service agent group corresponding to the intention service type in service agent groups corresponding to preset service types;
the second judging submodule is used for judging whether available agents in an idle state exist in the target service agent group or not;
the selecting submodule is used for selecting any available agent in an idle state from the target service agent group to serve as a target service agent if the available agent exists;
and the second waiting sub-module is used for selecting any available agent in the idle state from the target service agent group to serve as the target service agent when the available agent in the idle state exists in the target service agent group if the available agent does not exist.
Optionally, the selecting sub-module or the second waiting sub-module may include:
the third determining unit is used for determining the answered workload of each agent in the available agents in the idle state if at least two available agents in the idle state exist in the target service agent group, wherein the answered workload comprises the answered duration or the answered times;
and the fourth determining unit is used for determining the available seat with the least answered workload in the idle state as the target service seat.
Optionally, the manual agent distribution device 600 for outbound call may include:
the creation module is used for creating a service seat group corresponding to each service type;
the second distribution module is used for distributing the service agents and the service keywords corresponding to the service type of at least one service agent group for each service agent group based on the service type of each service agent group.
The method of any one of the above embodiments can be implemented by the manual seat distribution device for outbound call according to the embodiments of the present disclosure, and the implementation manner and the beneficial effects of the manual seat distribution device are similar and are not repeated here.
The embodiment of the disclosure further provides a computer device, where the computer device includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the method of any one of the foregoing embodiments may be implemented, and an implementation manner and a beneficial effect of the method are similar, and are not repeated herein.
A computer device in embodiments of the present disclosure may be understood as any device having processing and computing capabilities, which may include, but is not limited to, mobile terminals such as smartphones, notebook computers, personal Digital Assistants (PDAs), tablet computers (PADs), portable Multimedia Players (PMPs), etc., as well as stationary electronic devices such as digital TVs, desktop computers, etc.
Fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure, as shown in fig. 7, a computer device 700 may include a processor 710 and a memory 720, where the memory 720 stores a computer program 721, and when the computer program 721 is executed by the processor 710, the method provided in any of the foregoing embodiments may be implemented, and the implementation manner and the beneficial effects are similar, and are not repeated herein.
Of course, only some of the components of the computer apparatus 700 relevant to the present invention are shown in fig. 7 for simplicity, and components such as buses, input/output interfaces, input devices, output devices, and the like are omitted. In addition, the computer device 700 may include any other suitable components depending on the particular application.
The embodiments of the present disclosure provide a computer readable storage medium, in which a computer program is stored, where when the computer program is executed by a processor, the method of any of the foregoing embodiments may be implemented, and the implementation manner and beneficial effects are similar, and are not described herein again.
The computer readable storage media described above can employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer programs described above may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer device, partly on the user's device, as a stand-alone software package, partly on the user's computer device and partly on a remote computer device or entirely on the remote computer device or server.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (11)

1. The manual seat distribution method for the outbound call is characterized by comprising the following steps of:
receiving voice information of a customer in response to the customer answering a call;
performing service intention analysis based on the voice information, and determining the intention service type of the client;
and distributing the target service agents corresponding to the intention service types to the clients in a preset target service agent group corresponding to the intention service types.
2. The method of claim 1, wherein said performing a business intent analysis based on said voice information, determining an intent business type for said customer, comprises:
carrying out service keyword recognition on the voice information to obtain service keywords corresponding to each service type in the voice information;
and carrying out intent analysis based on the business keywords, and determining the intent business type of the client.
3. The method of claim 2, wherein the determining the type of intent business for the customer based on the business keywords by performing intent analysis comprises:
counting the first quantity of the business keywords corresponding to each business type;
and when the first quantity is larger than a preset threshold value, determining the service type corresponding to the first quantity as the intention service type.
4. The method of claim 2, wherein the determining the type of intent business for the customer based on the business keywords by performing intent analysis comprises:
counting the second number of the business keywords corresponding to each business type in a first preset duration;
and determining the corresponding service type when the second number is larger than a preset threshold value as the intention service type.
5. The method of claim 1, wherein said performing a business intent analysis based on said voice information, determining an intent business type for said customer, comprises:
judging whether available seats in an idle state exist in a service seat group of each service type preset at the current moment;
if yes, carrying out service intention analysis based on the voice information, and determining the intention service type of the client;
and if the service intention analysis is not performed, waiting for the available agents in the idle state in the service agent groups of the service types, and determining the intention service type of the client based on the voice information.
6. The method according to claim 1 or 5, wherein the assigning, in the preset target service agent group corresponding to the intention service type, the target service agent corresponding to the intention service type to the client includes:
determining a target service seat group corresponding to the intention service type in service seat groups corresponding to preset service types;
judging whether available agents in an idle state exist in the target service agent group;
if yes, selecting any available agent in an idle state from the target service agent group as the target service agent;
and if the available agents in the idle state do not exist in the target service agent group, selecting any available agent in the idle state from the target service agent group as the target service agent.
7. The method of claim 6, wherein selecting any available agent in an idle state from the set of target service agents as the target service agent comprises:
if at least two available agents in the idle state exist in the target service agent group, determining the answered workload of each agent in the available agents in the idle state, wherein the answered workload comprises the answered duration or the answered times;
and determining the available seat in the idle state with the least answered workload as the target service seat.
8. The method of claim 1, wherein prior to receiving the customer's voice information in response to the customer answering the call, the method further comprises:
creating a service seat group corresponding to each service type;
based on the service type of each service agent group, distributing at least one service agent and service keywords corresponding to the service type of the service agent group for each service agent group.
9. An outbound manual seat distribution device, comprising:
the receiving module is used for responding to the call answering of the client and receiving the voice information of the client;
the intention analysis module is used for carrying out service intention analysis based on the voice information and determining the intention service type of the client;
the first allocation module is used for allocating the target service agents corresponding to the intention service types for the clients in the target service agent groups corresponding to the preset intention service types.
10. A computer device, comprising:
a memory and a processor, wherein the memory stores a computer program which, when executed by the processor, implements the outbound manual agent distribution method according to any one of claims 1 to 8.
11. A computer-readable storage medium, in which a computer program is stored which, when executed by a processor, implements the outbound manual agent distribution method according to any one of claims 1 to 8.
CN202211600254.8A 2022-12-12 2022-12-12 Method, device, equipment and storage medium for distributing agents for manual call transfer Pending CN116170541A (en)

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CN202211600254.8A CN116170541A (en) 2022-12-12 2022-12-12 Method, device, equipment and storage medium for distributing agents for manual call transfer

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211600254.8A CN116170541A (en) 2022-12-12 2022-12-12 Method, device, equipment and storage medium for distributing agents for manual call transfer

Publications (1)

Publication Number Publication Date
CN116170541A true CN116170541A (en) 2023-05-26

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