WO2024255225A1 - Procédé et appareil de prédiction de temps d'attente de consultation de service client en ligne - Google Patents

Procédé et appareil de prédiction de temps d'attente de consultation de service client en ligne Download PDF

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Publication number
WO2024255225A1
WO2024255225A1 PCT/CN2024/071118 CN2024071118W WO2024255225A1 WO 2024255225 A1 WO2024255225 A1 WO 2024255225A1 CN 2024071118 W CN2024071118 W CN 2024071118W WO 2024255225 A1 WO2024255225 A1 WO 2024255225A1
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Prior art keywords
customer service
user
queuing
queue
time
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English (en)
Chinese (zh)
Inventor
何峰
何刚
康志恒
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/338—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/34—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/01—Customer relationship services
    • G06Q30/015—Providing customer assistance, e.g. assisting a customer within a business location or via helpdesk
    • G06Q30/016—After-sales

Definitions

  • the present disclosure relates to the field of computer technology, and in particular to a method and device for predicting the queuing time of online customer service consultation.
  • NLP Natural Language Processing
  • the inventors found that the existing technology, whether estimating the queuing time based on statistical methods or based on NLP technology, has a large estimation error of the queuing time and low prediction performance.
  • the embodiments of the present disclosure provide a method and device for predicting the queuing time for online customer service consultation, which greatly reduces the error of the prediction result and improves the prediction accuracy and prediction performance of the queuing time.
  • a method for predicting the queuing time of online customer service consultation comprising:
  • the customer service status characteristics include the number of currently available customer service staff and the number of sessions being processed by each customer service staff;
  • the queuing time estimation model is used to predict the queuing time of the user according to the queuing characteristics of the user, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative.
  • the queuing characteristics of the user include the queue number of the user in the waiting queue and the queue number of the user in the skill group; according to the queuing characteristics of the user, the currently available number of customer services and the number of sessions being processed by each customer service, the queuing time prediction model is used to predict the queuing time of the user, including: according to the queue number of the user in the waiting queue and the queue number of the user in the skill group, the currently available number of customer services and the number of sessions being processed by each customer service, the queuing time prediction model is used to predict the queuing time of the user.
  • the method further includes: determining a queuing time period corresponding to the user according to the sending time of the user's online customer service consultation request; using a queuing time estimation model to predict the queuing time of the user according to the queuing characteristics of the user, the current number of available customer service representatives, and the number of sessions being processed by each customer service representative, including: determining a queuing time period corresponding to the user, the queue number of the user in the waiting queue, and the number of sessions in the skill group in which the user is in the waiting queue, according to the ... sending time of the user's online customer service consultation request; The queue number, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative are used to predict the queue time of the user using a queue time estimation model.
  • the queuing time prediction model is constructed in the following manner: obtaining user online customer service consultation session data in a recent period; extracting session features corresponding to each session from the user online customer service consultation session data, the session features including the request for online consultation time, the queuing time period, the skill group to which one belongs, the queue number of the user in the waiting queue and the queue number of the user in the skill group to which one belongs, the number of currently available customer services in the skill group to which one belongs and the number of sessions being processed by each customer service, as well as the actual online consultation time; obtaining the queuing time corresponding to each session based on the request for online consultation time and the actual online consultation time corresponding to each session; for each session, using the queuing time corresponding to the session to mark the session features corresponding to the session to generate training data corresponding to the session; and performing model training based on the training data corresponding to each session to construct the queuing time prediction model.
  • the queuing time prediction model after using the queuing time prediction model to predict the queuing time of the user, it also includes: in response to the user's specified operation trigger event, re-acquiring the user's queuing characteristics and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer service staff and the number of sessions being processed by each customer service; based on the user's queuing characteristics, the current number of available customer service staff and the number of sessions being processed by each customer service, using the queuing time prediction model to predict the user's queuing time to update the user's queuing time.
  • the queuing time prediction model after using the queuing time prediction model to predict the queuing time of the user, it also includes: in response to detecting that a customer service has ended the session being processed and released resources, taking out the queued user from the waiting queue and assigning it to the customer service, refreshing the waiting queue and updating the customer service status; when the customer service status is still that all customer services are busy, re-acquiring the queuing characteristics of the user and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer services and the number of sessions being processed by each customer service; based on the queuing characteristics of the user, the current number of available customer services and the number of sessions being processed by each customer service, using the queuing time prediction model to predict the queuing time of the user, so as to update the queuing time of the user.
  • the method further includes: when the customer service status is that there is a customer service who is not busy, allocating the user to the customer service who is not busy.
  • a device for predicting the queuing time of online customer service consultation comprising:
  • a status acquisition module configured to determine the skill group corresponding to the user and acquire the customer service status of the skill group in response to receiving an online customer service consultation request from the user;
  • a queue adding module used for adding the user to the waiting queue when the customer service status is that all customer service staff are busy
  • a feature acquisition module used to acquire the queuing features of the user and the customer service status features of the skill group, wherein the customer service status features include the number of currently available customer service staff and the number of sessions being processed by each customer service staff;
  • the duration prediction module is used to predict the queuing time of the user using a queuing time estimation model according to the queuing characteristics of the user, the number of currently available customer service staff and the number of sessions being processed by each customer service staff.
  • an electronic device comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the waiting time for online customer service consultation provided in an embodiment of the present disclosure.
  • a computer-readable medium on which a computer program is stored.
  • the program is executed by a processor, the method for predicting the online customer service consultation queue length provided in an embodiment of the present disclosure is implemented.
  • One embodiment disclosed above has the following advantages or beneficial effects: by responding to receiving an online customer service consultation request from a user, determining the skill group corresponding to the user and obtaining the customer service status of the skill group; when the customer service status is that all customer service staff are busy, adding the user to the waiting queue; obtaining the user's queuing characteristics and the customer service status characteristics of the skill group, and obtaining the customer service status of the skill group;
  • the service status characteristics include the current number of available customer service staff and the number of sessions being handled by each customer service staff.
  • the technical solution of using the queue time estimation model to predict the user's queue time based on the user's queue characteristics, the current number of available customer service staff and the number of sessions being handled by each customer service staff can predict the current user's queue time based on the status of online customer service staff that can receive the user in the customer service system at the time the current user comes online, the status of the sessions being received by these customer service staff, and the user's queue characteristics, which greatly reduces the error of the prediction result and improves the prediction accuracy and prediction performance of the queue time.
  • FIG1 is a schematic diagram of the main steps of a method for predicting the queuing time of online customer service consultation according to an embodiment of the present disclosure
  • FIG2 is a schematic diagram of a process for predicting the length of time to queue for online customer service consultation according to an embodiment of the present disclosure
  • FIG3 is a schematic diagram of the error of the prediction result of the queuing time according to an embodiment of the present disclosure
  • FIG4 is a schematic diagram of main modules of a device for predicting the queuing time for online customer service consultation according to an embodiment of the present disclosure
  • FIG5 is an exemplary system architecture diagram in which embodiments of the present disclosure may be applied.
  • FIG. 6 is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present disclosure.
  • online customer service systems have become an important part of e-commerce websites.
  • online customer service human resources are limited, and customer service staff need to take into account both busy and idle times in a day.
  • customer service reception pressure is high, resulting in some users being unable to access manual customer service in time and entering the waiting queue.
  • the consultation queue is to reduce the peak and fill the valley of the number of consultation manual users, make full use of customer service resources, and allow users to access manual customer service after a short wait, rather than leaving a message without a customer service reply, to maximize the user pick-up rate and improve the user experience.
  • the core is to inform the user of the expected waiting time, which directly determines whether the user chooses to queue, and the accuracy of the estimated waiting time also directly affects the user's queuing experience. Therefore, the estimation of the waiting time is realized through an algorithm, based on the frequency of historical customer service ending the conversation, and the prediction of how long each ongoing conversation will end, as well as the different users queuing under different skill groups, to comprehensively calculate the user's queuing time.
  • self-operated merchants have tens of thousands of customer service staff who need to be received at the same time.
  • the relatively large customer service system has tens of thousands of customer service staff who need to be managed and received at the same time.
  • the statistical method is to count the number of people in the queue and multiply the average time of queuing by the number of people to get the estimated waiting time.
  • the method based on NLP technology to predict the end time of a single-call session is to predict the end time of a single-call session and accumulate the estimated end time of all ongoing sessions of all customer service staff who can receive the user to get the waiting time for a single user.
  • the final estimated queuing time requires the accumulation of the predicted time of multiple conversations, and the error will be infinitely magnified; (2) It is necessary to predict the estimated end time of all conversations currently being received by the online customer service.
  • the prediction of a user's queuing time requires thousands of predictions, which has low performance and low feasibility.
  • the present disclosure provides a method for predicting the queuing time for online customer service consultation, and its implementation principle is: according to the current user entry time, the online customer service status that can receive the user in the current customer service system, as well as the session status that these customer service are receiving, and the position of the user in the waiting queue are obtained, and relevant features are extracted.
  • the actual queuing time of historical users queuing under the current features is used as a label, and model learning is performed based on the machine learning regression method to predict the estimated queuing time when the current user enters the waiting queue, and the queuing time is divided into multiple slices, which are returned to the front end to be presented to the user.
  • Fig. 1 is a schematic diagram of the main steps of the method for predicting the online customer service consultation queue length according to an embodiment of the present disclosure. As shown in Fig. 1, the method for predicting the online customer service consultation queue length according to an embodiment of the present disclosure mainly includes the following steps S101 to S104.
  • Step S101 In response to receiving a user's online customer service consultation request, determine the user When a user requests online customer service, the system will first determine the skill group corresponding to the online customer service request based on the business scope. Each skill group corresponds to an online customer service task for a certain business.
  • Each skill group may include multiple customer service representatives, and each customer service representative may also correspond to multiple skill groups, and each customer service representative can handle multiple online customer service consultation sessions at the same time.
  • the customer service status of the skill group is that all customers are busy. Otherwise, if there are customer service representatives who have not handled sessions, or if the number of sessions currently handled by a customer service representative does not reach the limit of the number of sessions that the customer service representative can handle, the customer service status of the skill group is considered to be that there are customers available.
  • Step S102 When the customer service status is that all customer services are busy, the user is added to the waiting queue.
  • all customer services of the skill group are processing sessions, and the number of sessions currently processed by each customer service is the limit of the number of sessions that the customer service can process, all customer services are busy, and at this time, the user is added to the waiting queue.
  • Step S103 Obtain the queuing characteristics of the user and the customer service status characteristics of the skill group, wherein the customer service status characteristics include the number of currently available customer service staff and the number of sessions being processed by each customer service staff.
  • the queuing time of the user will be estimated based on the user's queuing status and the status characteristics of the customer service of the skill group.
  • the queuing characteristics of the user mainly include the queue number of the user in the waiting queue and the queue number of the user in the skill group.
  • the waiting queue stores all the queuing users who need online customer service consultation and their corresponding online customer service consultation requests, and also stores the skill group information corresponding to each user. Therefore, the first queue number of the user in the waiting queue and the second queue number of the user in the corresponding skill group can be obtained according to the information of the waiting queue.
  • Step S104 predicting the queuing time of the user using a queuing time estimation model according to the queuing characteristics of the user, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative.
  • the queuing characteristics of the user include the queue number of the user in the waiting queue and the queue number of the user in the skill group. Furthermore, according to the queuing characteristics of the user, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative, the queuing duration prediction model is used to predict the queuing duration of the user, which may specifically include: according to the queue number of the user in the waiting queue and the queue number of the user in the skill group, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative, the queuing duration prediction model is used to predict the queuing duration of the user.
  • a queue time estimation model before using a queue time estimation model to predict the queue time of the user according to the queue characteristics of the user, the current number of available customer service staff, and the number of sessions being processed by each customer service staff, it may also include: determining the queue time period corresponding to the user according to the sending time of the user's online customer service consultation request.
  • using a queue time estimation model to predict the queue time of the user may specifically include: using a queue time estimation model to predict the queue time of the user according to the queue time period corresponding to the user, the queue number of the user in the waiting queue and the queue number of the user in the skill group, the current number of available customer service staff, and the number of sessions being processed by each customer service staff.
  • the queuing time estimation model is constructed in the following manner: obtaining user online customer service consultation session data in a recent period; extracting session features corresponding to each session from the user online customer service consultation session data, the session features including the time of requesting online consultation, the queuing period, the skill group to which they belong, the queue number of the user in the waiting queue and the queue number of the user in the skill group to which they belong, the number of currently available customer services of the skill group to which they belong and the number of sessions being processed by each customer service, as well as the actual online consultation time; obtaining the queuing time corresponding to each session according to the time of requesting online consultation and the actual online consultation time corresponding to each session; for each session, marking the session features corresponding to the session using the queuing time corresponding to the session to generate training data corresponding to the session; and performing model training based on the training data corresponding to each session to construct the queuing time prediction model.
  • the present disclosure when constructing a queue time estimation model, the present disclosure first obtains the user online customer service consultation session data in the recent period, for example, the user queue session data, customer service reception data, and the actual queue time data in the past month can be screened.
  • the user queue session data is used to extract model training features and labels
  • the customer service reception data is used to calculate the user's online time, the number of customer service staff who can currently receive the user, and the number of ongoing sessions with these customer service staff
  • the actual queue time is obtained by burying the user's queue time to obtain the time between the user starting to queue and the conversation with the manual customer service.
  • the embodiment of the present disclosure needs to construct user portrait features and customer service portrait features respectively. At the same time, these features must be available at any time during the user queue process.
  • the feature definition is as follows:
  • Queuing time period divided into hours, the time period when users come in within the hour is numbered, because the time for users to queue up to access the line is different in different time periods. For example, if you queue up in the early morning, the number of customer service staff available is small, and the queuing time is usually more than 10 minutes. Therefore, the queuing time period can affect the actual queuing time of users;
  • the user's queue number in the waiting queue Statistics on the application customer service system, the sequence number of the current user in the waiting queue. The earlier the queue number, the shorter the waiting time;
  • the user's queue number in the skill group to which they belong Since users first navigate to the corresponding skill group through the system and queue under the skill group before they can access the line, the queue number under the skill group needs to be recorded. If the overall queue number is large, but the number of queues under the skill group is small, it means that the current incoming business is unevenly distributed. Users can still access the line in a shorter queue time under the skill group with fewer queues.
  • the number of currently available agents in the skill group that is, the total number of agents in the customer service system who have added the user's incoming skill group. The more agents there are, the faster the ongoing sessions are released and the shorter the user's waiting time.
  • Number of sessions being processed by customer service The number of sessions that customer service that can receive the current queued users is currently receiving at the same time. The more sessions there are, the greater the probability that the session will be released soon. The shorter the queue time;
  • Skill group identifier When a user comes in, he or she is first navigated to the corresponding skill group through the system. Due to business differences and the number of customer service personnel, the queuing time for different skill groups is also different. Therefore, this feature is also an important feature for predicting the user's queuing time.
  • the embodiment of the present disclosure needs to construct a label of the user's actual queue time for model learning.
  • the user can be buried at the time when the user starts queuing and ends queuing, and the user's actual queue time can be recorded, which can be used as a label for model training data.
  • the point is not buried, the user's actual queue time cannot be obtained, so it is necessary to construct a method to simulate the user's actual queue entry time, so as to use it as a label for model learning.
  • the specific method is as follows: with minutes as the granularity, take the conversation that generates messages within each minute, assume that these message users enter the line at the beginning of one minute to generate a queue at the same time, and then take the conversation that is happening at this moment, obtain the time from the actual end time of each conversation to the current moment, sort them from small to large according to the time length, and traverse the queue users and the ongoing conversations in turn. If the customer service received by the ongoing conversation can receive the queue user skill group, then the end time of this conversation is the waiting time of the current queue user, and so on. Theoretically, if the customer service has not grabbed the order, is not online or is suspended, and the upper limit of the connection mode has not been adjusted, then the queued user may be connected only after the current ongoing session ends.
  • Table 1 shows the queue information of queuing users according to an embodiment of the present disclosure.
  • Table 2 shows the currently ongoing conversation queue, which mainly includes the customer service number (including the skill group information mounted by the customer service), the actual end time of the conversation (in seconds), and the matching queue user index.
  • customer service representative A3-1005 in the online customer service can receive this skill group, so after the customer service representative's most recent conversation ends, the user in the queue can get on the line, so the actual end time of the conversation is 5 seconds, which is the waiting time for the user in the queue.
  • the queuing time corresponding to each session can be obtained.
  • the queuing time corresponding to the session is used to mark the session features corresponding to the session to generate the training data corresponding to the session.
  • model training is performed based on the training data corresponding to each session to construct a queuing time estimation model.
  • a GBDT Gradient Boosting Decision Tree
  • the user's queue time can be predicted based on the user's corresponding queue period, the user's queue characteristics, the current number of available customer service representatives, and the number of sessions being handled by each customer service representative.
  • the queue time estimation model after using the queue time estimation model to predict the queue time of the user, it may also include: in response to the specified operation triggering event of the user, re-acquiring the queue characteristics of the user and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer service staff and the number of sessions being handled by each customer service; based on the queue characteristics of the user, the current number of available customer service staff and the number of sessions being handled by each customer service, using the queue time estimation model to predict the queue time of the user to update the queue time of the user.
  • the specified operation is, for example, click, slide, return, switch screen and other operations.
  • the user When the user generates event operations such as click, slide, return, switch screen, etc., it will trigger the prediction of the user's queue time again, because the estimated time may change at any time, For example, the initial estimated duration is 3 minutes, but during the 3 minutes, the customer service may leave for something, which will lengthen the waiting time, or if more customer service staff are added, the estimated time will become shorter. Recalculating based on the triggering of several events and obtaining more real-time features can make the estimated duration more accurate.
  • event operations such as click, slide, return, switch screen, etc.
  • the queue time estimation model after using the queue time estimation model to predict the queue time of the user, it may also include: in response to detecting that a customer service ends the session being processed and releases resources, taking out the queued user from the waiting queue and assigning it to the customer service, refreshing the waiting queue and updating the customer service status; when the customer service status is still that all customer services are busy, re-acquiring the queue characteristics of the user and the customer service status characteristics of the skill group, the customer service status characteristics include the current number of available customer services and the number of sessions being processed by each customer service; according to the queue characteristics of the user, the current number of available customer services and the number of sessions being processed by each customer service, using the queue time estimation model to predict the queue time of the user, so as to update the queue time of the user.
  • a customer service When a customer service ends a session and releases resources, it will trigger the system to update the waiting queue information and customer service status information, and assign a new online customer service consultation task to the customer service.
  • the system By updating the customer service status information and the waiting queue information, more accurate features can be provided to the algorithm, making the predicted queue time more accurate.
  • the method may further include: when the customer service status is that there is a customer service that is not busy, assigning the user to the customer service that is not busy.
  • the customer service status is that there is a customer service that is not busy
  • assigning the user to the customer service that is not busy When there is a customer service that is not busy, the user's online consultation request can be directly processed.
  • the user when the user is in the queue, he can also actively cancel the queue, at which point the process ends and there is no need to calculate the user's queue time.
  • Figure 2 is a schematic diagram of the prediction process of the online customer service consultation queue length in an embodiment of the present disclosure.
  • the prediction process of the online customer service consultation queue length is as follows: when receiving the user's online customer service consultation request, it is determined that the customer is online, and then the customer service status of the skill group is obtained, and whether there is an idle customer service is determined according to the customer service status; if there is an idle customer service, directly enter the online customer service consultation; otherwise, wait in the queue. Then, obtain the user's queue characteristics and the customer service status characteristics of the skill group, and use the queue length
  • the estimation model predicts the user's waiting time and outputs the predicted waiting time.
  • the user After that, it will monitor whether the user has triggered the specified operation event. If the user has triggered the specified operation event, it will re-execute the steps of obtaining the user's queue characteristics and the customer service status characteristics of the skill group, and using the queue time estimation model to predict the user's queue time, and output the predicted queue time; otherwise, it will continue to monitor whether there are customer service staff releasing resources. If a customer service staff releases resources, the queued user is taken out of the waiting queue and assigned to the customer service staff, and then the waiting queue is refreshed and the customer service status is updated. It will also re-execute the steps of obtaining the user's queue characteristics and the customer service status characteristics of the skill group, and using the queue time estimation model to predict the user's queue time, and output the predicted queue time.
  • Figure 3 is a schematic diagram of the error of the prediction result of the queue time in the embodiment of the present disclosure. As shown in Figure 3, the horizontal axis shows the error result in seconds, and the vertical axis shows the percentile value.
  • the prediction algorithm of the queue time in the embodiment of the present disclosure is used for prediction, and the average error of the prediction result is only 18.3 seconds. Among them, the prediction result error of the 25% percentile value is 2.8 seconds; the prediction result error of the 50% percentile value is 6.5 seconds; the prediction result error of the 75% percentile value is 15 seconds; the prediction result error of the 90% percentile value is 35.6 seconds; the prediction result error of the 99% percentile value is 212 seconds.
  • FIG4 is a schematic diagram of the main modules of the device for predicting the online customer service consultation queue length according to an embodiment of the present disclosure.
  • the device for predicting the online customer service consultation queue length 400 of the embodiment of the present disclosure mainly includes:
  • the status acquisition module 401 is used to determine the skill group corresponding to the user in response to receiving the user's online customer service consultation request, and obtain the customer service status of the skill group;
  • a queue adding module 402 configured to add the user to a waiting queue when the customer service status is that all customer service staff are busy;
  • the feature acquisition module 403 is used to acquire the queuing features of the user and the customer service status features of the skill group, wherein the customer service status features include the number of currently available customer service staff and the number of active customer service staff of each customer service staff. Number of sessions being processed;
  • the duration prediction module 404 is used to predict the queuing time of the user using a queuing time estimation model according to the queuing characteristics of the user, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative.
  • the queuing feature of the user includes the queue number of the user in the waiting queue and the queue number of the user in the skill group.
  • the duration prediction module 404 can also be used to: predict the queuing duration of the user using a queuing duration prediction model according to the queue number of the user in the waiting queue and the queue number of the user in the skill group, the number of currently available customer service representatives, and the number of sessions being processed by each customer service representative.
  • the device 400 for predicting the queuing time of online customer service consultation also includes a queuing time period determination module (not shown in the figure), which is used to: determine the queuing time period corresponding to the user according to the sending time of the user's online customer service consultation request before using the queuing time estimation model to predict the queuing time of the user according to the queuing characteristics of the user, the currently available number of customer services and the number of sessions being processed by each customer service; the time prediction module 404 can also be used to: use the queuing time estimation model to predict the queuing time of the user according to the queuing time period corresponding to the user, the queue number of the user in the waiting queue and the queue number of the user in the skill group, the currently available number of customer services and the number of sessions being processed by each customer service.
  • a queuing time period determination module (not shown in the figure), which is used to: determine the queuing time period corresponding to the user according to the sending time of the user's online customer service consultation request before using the que
  • the queuing time estimation model is constructed in the following manner: obtaining user online customer service consultation session data in a recent period; extracting session features corresponding to each session from the user online customer service consultation session data, the session features including the time of requesting online consultation, queuing time period, skill group, the queue number of the user in the waiting queue and the queue number of the user in the skill group, the number of currently available customer services of the skill group and the number of sessions being processed by each customer service, as well as the actual online consultation time; obtaining the queuing time corresponding to each session according to the time of requesting online consultation and the actual time of online consultation corresponding to each session; for each session, using the queue time corresponding to the session The queue duration marks the session features corresponding to the session to generate training data corresponding to the session; and performs model training based on the training data corresponding to each session to construct the queue duration estimation model.
  • the device 400 for predicting the queuing time of online customer service consultation also includes a queuing time updating module (not shown in the figure), which is used to: after using the queuing time prediction model to predict the queuing time of the user, in response to the user's specified operation trigger event, re-acquire the user's queuing characteristics and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer services and the number of sessions being processed by each customer service; based on the user's queuing characteristics, the current number of available customer services and the number of sessions being processed by each customer service, use the queuing time prediction model to predict the user's queuing time to update the user's queuing time.
  • a queuing time updating module (not shown in the figure), which is used to: after using the queuing time prediction model to predict the queuing time of the user, in response to the user's specified operation trigger event, re-acquire the user's queuing characteristics and the customer service
  • the device 400 for predicting the queuing time of online customer service consultation also includes a queuing time updating module (not shown in the figure), which is used to: after using the queuing time prediction model to predict the queuing time of the user, in response to detecting that a customer service has ended the session being processed and released resources, take out the queued user from the waiting queue and assign it to the customer service, refresh the waiting queue and update the customer service status; when the customer service status is still that all customer services are busy, re-acquire the queuing characteristics of the user and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer services and the number of sessions being processed by each customer service; based on the queuing characteristics of the user, the current number of available customer services and the number of sessions being processed by each customer service, use the queuing time prediction model to predict the queuing time of the user to update the queuing time of the user.
  • a queuing time updating module (not shown in the figure), which is used to:
  • the device 400 for predicting the queuing time of online customer service consultation also includes a customer service allocation module (not shown in the figure), which is used to: when the customer service status is that there is a customer service who is not busy, assign the user to the customer service who is not busy.
  • a customer service allocation module (not shown in the figure), which is used to: when the customer service status is that there is a customer service who is not busy, assign the user to the customer service who is not busy.
  • the invention in response to receiving the user's online customer
  • the invention provides a technical solution for predicting the queuing time of a user by using a queuing time estimation model based on the queuing characteristics of the user, the current number of available customer services and the number of sessions being processed by each customer service, and the queuing characteristics of the user.
  • the technical solution can predict the queuing time of the current user based on the status of online customer services that can receive the user in the customer service system at the time when the current user comes online, the status of the sessions being received by these customer services, and the queuing characteristics of the user, thereby greatly reducing the error of the prediction result and improving the prediction accuracy and performance of the queuing time.
  • FIG. 5 shows an exemplary system architecture 500 to which the method for predicting the online customer service consultation queue length or the device for predicting the online customer service consultation queue length according to the embodiments of the present disclosure can be applied.
  • system architecture 500 may include terminal devices 501, 502, 503, a network 504 and a server 505.
  • Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505.
  • Network 504 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.
  • terminal devices 501, 502, 503 Users can use terminal devices 501, 502, 503 to interact with server 505 through network 504 to receive or send messages, etc.
  • Various communication client applications can be installed on terminal devices 501, 502, 503, such as shopping applications, web browser applications, customer service consulting applications, instant messaging tools, social platform software, etc. (only as examples).
  • the terminal devices 501 , 502 , and 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.
  • Server 505 may be a server that provides various services, such as a backend management server that supports shopping websites browsed by users using terminal devices 501, 502, and 503 (for example only).
  • the backend management server may provide services such as online customer service consultation requests received.
  • the data responds to receiving an online customer service consultation request from a user, determines the skill group corresponding to the user, and obtains the customer service status of the skill group; when the customer service status is that all customer services are busy, adds the user to the waiting queue; obtains the queuing characteristics of the user and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer services and the number of sessions being processed by each customer service; uses a queuing time estimation model to predict the queuing time of the user based on the queuing characteristics of the user, the current number of available customer services, and the number of sessions being processed by each customer service, and feeds back the processing results (such as the user's queuing time - just an example) to the terminal device.
  • the method for predicting the online customer service consultation queue length provided in the embodiment of the present disclosure is generally executed by the server 505 , and accordingly, the device for predicting the online customer service consultation queue length is generally set in the server 505 .
  • terminal devices, networks and servers in Figure 5 is only illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements.
  • FIG. 6 a schematic diagram of a computer system 600 suitable for implementing a terminal device or server of the present disclosure is shown.
  • the terminal device or server shown in Figure 6 is only an example and should not limit the functions and scope of use of the present disclosure.
  • the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603.
  • ROM read-only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 600 are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604.
  • An input/output (I/O) interface 605 is also connected to the bus 604.
  • the I/O interface 605 includes an input section 606 including a keyboard, a mouse, etc.; a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.
  • the I/O interface 605 includes an output section 607; a storage section 608 including a hard disk or the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 609 performs communication processing via a network such as the Internet.
  • a drive 610 is also connected to the I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.
  • an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart.
  • the computer program can be downloaded and installed from a network through a communication part 609, and/or installed from a removable medium 611.
  • CPU central processing unit
  • the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above.
  • Computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.
  • a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • a computer-readable signal medium may also be any medium other than a computer-readable storage medium.
  • Computer-readable media that can send, propagate or transmit programs for use by or in conjunction with an instruction execution system, apparatus or device.
  • the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
  • each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function.
  • the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.
  • each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
  • the units or modules involved in the embodiments described in the present disclosure may be implemented by software or by hardware.
  • the units or modules described may also be arranged in a processor.
  • a processor including a state acquisition module, a queue addition module, a feature acquisition module, and a duration prediction module.
  • the names of these units or modules do not constitute a limitation on the units or modules themselves under certain circumstances.
  • the duration prediction module may also be described as "a module for predicting the queuing duration of the user using a queuing duration estimation model based on the queuing characteristics of the user, the number of currently available customer services, and the number of sessions being processed by each customer service.”
  • the present disclosure further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device.
  • the above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device The method comprises: in response to receiving an online customer service consultation request from a user, determining the skill group corresponding to the user and obtaining the customer service status of the skill group; when the customer service status is that all customer service staff are busy, adding the user to a waiting queue; obtaining the queuing characteristics of the user and the customer service status characteristics of the skill group, the customer service status characteristics including the current number of available customer service staff and the number of sessions being processed by each customer service; and predicting the queuing time of the user using a queuing time estimation model according to the queuing characteristics of the user, the current number of available customer service staff and the number of sessions being processed by each customer service.
  • the skill group corresponding to the user is determined, and the customer service status of the skill group is obtained; when the customer service status is that all customer services are busy, the user is added to the waiting queue; the user's queuing characteristics and the customer service status characteristics of the skill group are obtained, and the customer service status characteristics include the current number of available customer services and the number of sessions being processed by each customer service; according to the user's queuing characteristics, the current number of available customer services and the number of sessions being processed by each customer service, a queuing time estimation model is used to predict the user's queuing time.
  • the technical solution can predict the current user's queuing time based on the status of online customer services that can receive the user in the customer service system at the time when the current user comes online, the status of sessions being received by these customer services, and the user's queuing characteristics, thereby greatly reducing the error of the prediction result and improving the prediction accuracy and prediction performance of the queuing time.

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Abstract

La présente invention se rapporte au domaine des technologies informatiques et concerne un procédé et un appareil de prédiction d'un temps d'attente de consultation de service client en ligne. Un mode de réalisation spécifique du procédé comprend : en réponse à la réception d'une demande de consultation de service client en ligne provenant d'un utilisateur, la détermination d'un groupe de compétences correspondant à l'utilisateur et l'acquisition du statut de service client du groupe de compétences ; lorsque le statut de service client indique que tous les représentants de service client sont occupés, l'ajout de l'utilisateur à une file d'attente ; l'acquisition de caractéristiques de mise en file d'attente de l'utilisateur et de caractéristiques de statut de service client du groupe de compétences, les caractéristiques de statut de service client comprenant le nombre de représentants de service client actuellement disponibles et le nombre de conversations en cours de traitement par chaque représentant de service client ; et la prédiction d'un temps d'attente de l'utilisateur au moyen d'un modèle d'estimation de temps d'attente sur la base des caractéristiques de mise en file d'attente de l'utilisateur, du nombre de représentants de service client actuellement disponibles et du nombre de conversations en cours de traitement par chaque représentant de service client. Le mode de réalisation réduit considérablement les erreurs de résultats de prédiction et améliore la précision de prédiction de temps d'attente et les performances de prédiction.
PCT/CN2024/071118 2023-06-13 2024-01-08 Procédé et appareil de prédiction de temps d'attente de consultation de service client en ligne Ceased WO2024255225A1 (fr)

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CN118486438B (zh) * 2024-05-21 2025-04-18 苍兰棱星(南京)信息技术有限公司 一种用于远程医疗服务的服务优化管理系统
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