CN114529150A - Method and device for recommending call-out lines in groups and electronic equipment - Google Patents
Method and device for recommending call-out lines in groups and electronic equipment Download PDFInfo
- Publication number
- CN114529150A CN114529150A CN202210025939.8A CN202210025939A CN114529150A CN 114529150 A CN114529150 A CN 114529150A CN 202210025939 A CN202210025939 A CN 202210025939A CN 114529150 A CN114529150 A CN 114529150A
- Authority
- CN
- China
- Prior art keywords
- outbound
- groups
- stock
- seats
- clues
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Finance (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Accounting & Taxation (AREA)
- Artificial Intelligence (AREA)
- Quality & Reliability (AREA)
- Educational Administration (AREA)
- Biophysics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application provides a method and a device for group recommendation of outbound clues, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing. The method comprises the steps of obtaining characteristic data of a plurality of stock outbound clues; inputting the characteristic data of a plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread; grouping a plurality of stock outbound threads according to the predicted sequencing value of each stock outbound thread to obtain a plurality of outbound groups; and distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats. The method and the device can perform grouping management aiming at the quality of stock users before triggering, distribute corresponding outbound seats aiming at a plurality of outbound groups, can achieve effective touch, and improve outbound efficiency and order conversion rate.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for group recommendation of outbound cue, an electronic device, and a computer-readable storage medium.
Background
With the increase of users at the C end of the internet, the cost of the group management of the user quality for company operation gradually increases, and the existing group management is as follows:
(1) the salesperson records the high-quality client in the dialing process in the own workbook, and does not fully know the information of the user before dialing;
(2) a first-in and last-out principle, for example, a user 1 enters first, then a user 2 enters, the user 2 is arranged in front of the user 1, the user 2 is dialed first, then the user 1 is dialed, a clue (namely, a user) is dialed according to the latest incoming user, and the timeliness of the latest incoming user is guaranteed;
(3) operation rule distribution requires an operator to configure a user group according to business experience.
In practical operation, the inventor finds that the existing grouping management has the following problems in the whole process: 1) the single-dimension analysis has great accidental injury to high-quality users, and the accuracy rate of judging the high-quality users is difficult to keep stable along with the change of services; 2) the backlog of the outgoing call clues (i.e. the outgoing call users) is that there will be a lot of backlogs of good quality clues after the outgoing call is started first, and the best dialing opportunity is lost. In the face of massive users, how to manage outbound clues to improve outbound efficiency and order conversion rate becomes a technical problem to be solved urgently.
Disclosure of Invention
In view of the above, the present application is proposed to provide a method and apparatus for group referral of outbound cues, an electronic device and a computer readable storage medium, which overcome or at least partially solve the above problems, and can improve the efficiency of outbound calls and the order conversion rate. The technical scheme is as follows:
in a first aspect, a method for group recommendation of outbound threads is provided, which includes:
acquiring characteristic data of a plurality of stock outbound clues;
inputting the characteristic data of the plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread;
grouping the plurality of stock outbound threads according to the predicted ranking value of each stock outbound thread to obtain a plurality of outbound groups;
and distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats.
In one possible implementation, the method further includes:
monitoring a newly added outbound cue;
when monitoring that an added outbound cue is newly added, acquiring characteristic data of the added outbound cue;
clustering the newly-added outbound clues and the plurality of outbound groups according to the characteristic data of the newly-added outbound clues, and determining the outbound groups to which the newly-added outbound clues are clustered;
and adding the new outbound clues to the outbound group to which the new outbound clues are clustered.
In a possible implementation manner, grouping the plurality of stock outbound threads according to the predicted ranking value of each stock outbound thread to obtain a plurality of outbound groups includes:
setting a grouping threshold interval according to the predicted ranking value of each stock outbound cue and the preset number of outbound groups;
and grouping the plurality of stock outbound threads by combining the predicted ranking values of the stock outbound threads and the grouping threshold interval to obtain a plurality of outbound groups.
In one possible implementation, allocating corresponding outbound agents to the plurality of outbound groups includes:
adding group tags to the outbound groups according to the predicted ranking values of the stock outbound threads;
and distributing corresponding outbound seats for the outbound groups based on the group labels of the outbound groups.
In one possible implementation, allocating corresponding outbound agents to the plurality of outbound groups includes:
determining a plurality of outbound agents in a working state, and acquiring preset outbound times of the outbound agents in a specified time period;
and distributing corresponding outbound seats for the outbound groups based on the preset outbound times of the outbound seats in a specified time period.
In a possible implementation manner, allocating corresponding outbound agents to the outbound groups based on preset outbound times of the outbound agents within a specified time period includes:
counting the number of outbound clues of each of the plurality of outbound groups;
and distributing corresponding outbound seats for the plurality of outbound groups according to the preset outbound times of the plurality of outbound seats in a specified time period and the number of the outbound clues of the plurality of outbound groups.
In a possible implementation manner, allocating corresponding outbound agents to the outbound groups based on preset outbound times of the outbound agents within a specified time period includes:
distributing different outbound grades to each outbound group according to the plurality of outbound groups;
and distributing corresponding outbound seats for the outbound groups according to preset outbound times of the outbound seats in a specified time period and the respective outbound grades of the outbound groups.
In a second aspect, an apparatus for group referral of outbound cue is provided, comprising:
the first acquisition module is used for acquiring the characteristic data of a plurality of stock outbound clues;
the prediction module is used for inputting the characteristic data of the plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread;
the grouping module is used for grouping the plurality of stock outbound threads according to the predicted ranking values of the stock outbound threads to obtain a plurality of outbound groups;
and the recommending module is used for distributing corresponding outbound seats for the outbound groups and recommending the outbound groups to the distributed outbound seats.
In a possible implementation manner, the apparatus further includes a monitoring module, a second obtaining module, and a clustering module;
the monitoring module is used for monitoring a newly added outbound cue;
the second acquisition module is used for acquiring the characteristic data of the newly added outbound clue when the newly added outbound clue is monitored;
the clustering module is used for clustering the newly-added outbound clues and the plurality of outbound groups according to the characteristic data of the newly-added outbound clues and determining the outbound groups to which the newly-added outbound clues are clustered; and adding the new outbound clues to the outbound group to which the new outbound clues are clustered.
In one possible implementation, the grouping module is further configured to:
setting a grouping threshold interval according to the predicted ranking value of each stock outbound cue and the preset number of outbound groups;
and grouping the plurality of stock outbound threads by combining the predicted ranking values of the stock outbound threads and the grouping threshold interval to obtain a plurality of outbound groups.
In one possible implementation, the recommendation module is further configured to:
adding group tags to the outbound groups according to the predicted ranking values of the stock outbound threads;
and distributing corresponding outbound seats for the outbound groups based on the group labels of the outbound groups.
In one possible implementation, the recommendation module is further configured to:
determining a plurality of outbound agents in a working state, and acquiring preset outbound times of the outbound agents in a specified time period;
and distributing corresponding outbound seats for the outbound groups based on the preset outbound times of the outbound seats in a specified time period.
In one possible implementation, the recommendation module is further configured to:
counting the number of outbound clues of each of the plurality of outbound groups;
and distributing corresponding outbound seats for the plurality of outbound groups according to the preset outbound times of the plurality of outbound seats in a specified time period and the number of the outbound clues of the plurality of outbound groups.
In one possible implementation, the recommendation module is further configured to:
distributing different outbound grades to each outbound group according to the plurality of outbound groups;
and distributing corresponding outbound seats for the outbound groups according to preset outbound times of the outbound seats in a specified time period and the respective outbound grades of the outbound groups.
In a third aspect, an electronic device is provided, which comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the method for outbound cue clustering recommendation according to any of the above.
In a fourth aspect, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the method for group referral of outbound cue as described in any of the above.
By means of the technical scheme, the method and the device for group recommendation of outbound threads, the electronic device and the computer-readable storage medium provided by the embodiment of the application can acquire the feature data of a plurality of stock outbound threads; inputting the characteristic data of a plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread; then, grouping the stock outbound clues according to the predicted ranking value of each stock outbound clue to obtain a plurality of outbound groups; and distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats. It can be seen that the embodiment of the application can perform grouping management on the quality of stock users before triggering, and allocate corresponding outbound seats to a plurality of outbound groups, thereby realizing effective triggering, and improving outbound efficiency and order conversion rate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
FIG. 1 is a flow chart illustrating a method for group referral of outbound threads according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for group referral of outbound threads according to another embodiment of the present application;
FIG. 3 is a diagram illustrating an architecture of outbound thread clustering referrals in an embodiment of the present application;
FIG. 4 is a block diagram of an apparatus for group referral of outbound threads according to an embodiment of the present application;
FIG. 5 is a block diagram of an apparatus for group referral of outbound threads according to another embodiment of the present application;
FIG. 6 shows a block diagram of an electronic device according to an embodiment of the application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that such uses are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to".
The embodiment of the application provides a method for recommending outbound clues in groups, which can be applied to electronic devices such as servers, personal computers, smart phones, tablet computers, smart watches and the like, as shown in fig. 1, the method for recommending outbound clues in groups can include the following steps S101 to S104:
step S101, obtaining characteristic data of a plurality of stock outbound clues;
step S102, inputting the characteristic data of a plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread;
step S103, grouping a plurality of stock outbound threads according to the predicted sequencing value of each stock outbound thread to obtain a plurality of outbound groups;
and step S104, distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats.
The method and the device can acquire the characteristic data of a plurality of stock outbound clues; inputting the characteristic data of a plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread; then, grouping the stock outbound clues according to the predicted ranking value of each stock outbound clue to obtain a plurality of outbound groups; and distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats. It can be seen that the embodiment of the application can perform grouping management on the quality of stock users before triggering, and allocate corresponding outbound seats to a plurality of outbound groups, thereby realizing effective triggering, and improving outbound efficiency and order conversion rate.
The out-of-stock call thread mentioned in the above step S101 may be a user having a need for insurance service, house property service, automobile service, lesson service, or the like. The characteristic data of the stock outbound cue can be used for representing the personal attribute and the social attribute of the stock outbound cue, wherein the characteristic data of the stock outbound cue can specifically comprise data related to the personal attribute, such as sex, age, hobbies, family personnel and the like, and the characteristic data of the stock outbound cue can also specifically comprise data related to the social attribute, such as the city, the occupation type, the income condition and the like. It should be noted that the examples are merely illustrative and do not limit the embodiments of the present application.
A possible implementation is provided in the embodiment of the present application, and the ranking model may be trained through the following steps a1 to A3:
step A1, constructing an initial sequencing model;
step A2, collecting characteristic data and historical order data of a plurality of outgoing call clues of completed calls;
and step A3, training the initial ranking model based on the characteristic data of a plurality of outbound clues and historical order data to obtain a trained ranking model.
The initial ranking model in step a1 may be constructed based on a lightGBM model, an FM (quantization Machine) model, or other neural network model. In step a2, the initial ranking model is trained by using the collected feature data of the plurality of outgoing call clues of completed calls and the historical order data as training samples, so as to obtain a trained ranking model. The characteristic data of the outbound call clue may be gender, age, hobbies, family, city, occupation type, income status, etc., and the historical order data may be call-on time, order conversion time, product type, order amount, etc., which is not limited in this embodiment.
The embodiment of the present application provides a possible implementation manner, and the clustering management may be implemented by adopting a clustering means for newly added outbound threads, which specifically includes the following steps B1 to B4:
step B1, monitoring newly added outbound clues;
step B2, when monitoring the new calling thread, obtaining the characteristic data of the new calling thread;
step B3, clustering the newly added outbound clues and a plurality of outbound groups according to the characteristic data of the newly added outbound clues, and determining the outbound groups to which the newly added outbound clues are clustered;
and step B4, adding the new outbound thread to the outbound group to which the new outbound thread is clustered.
The embodiment of the application can realize real-time grouping management on the newly-added outbound clues, improve timeliness, save the cost of operating the outbound clues and improve the order conversion rate.
The embodiment of the application provides a possible implementation manner, which can acquire the feature data of a plurality of newly added outbound threads in the outbound thread feature database. The outbound cue feature database is a database used for storing feature data of mass outbound cues, wherein the outbound cues refer to called parties in a call service system and are users with insurance purchase requirements, house property purchase requirements, tourist route reservation requirements, course purchase requirements, automobile purchase requirements and the like; then, clustering the newly added outbound clues and a plurality of outbound groups according to the characteristic data of the newly added outbound clues, and determining the outbound groups to which the newly added outbound clues are clustered; and adding the new outbound clues to the outbound group to which the new outbound clues are clustered. It can be seen that the embodiment of the application can adopt a clustering means to realize clustering management, thereby improving the information assurance degree of the subsequent outbound agents on the outbound clues, and improving the outbound efficiency and the order conversion rate.
In the embodiment of the present application, a possible implementation manner is provided, where in the step S103, the plurality of stock outbound threads are grouped according to the predicted ranking value of each stock outbound thread to obtain a plurality of outbound groups, and the method specifically includes the following steps C1 and C2:
step C1, setting a clustering threshold interval according to the predicted ranking value of each stock outbound cue and the preset number of outbound groups;
and step C2, grouping the plurality of stock outbound threads by combining the predicted ranking values and the grouping threshold intervals of the stock outbound threads to obtain a plurality of outbound groups.
The method and the device for processing the outbound call have the advantages that the sequencing scheme of the outbound call clues and the setting of the grouping threshold interval are more reasonable in combination with the service scene, and the outbound call efficiency and the order conversion rate can be effectively improved.
A possible implementation manner is provided in the embodiment of the present application, where the step S104 allocates corresponding outbound seats to multiple outbound groups, specifically, the following steps D1 and D2 may be included:
step D1, adding group labels to the outbound groups according to the predicted ranking values of the outbound clues of each stock;
and D2, distributing corresponding outbound seats for the outbound groups based on the group labels of the outbound groups.
According to the method and the device, the group labels can be added to the outbound groups according to the predicted sequencing values of the stock outbound clues, the corresponding outbound seats are distributed to the outbound groups based on the group labels of the outbound groups, the outbound seats matched with the outbound groups can be distributed according to the characteristics of the outbound groups, and the outbound efficiency and the order conversion rate can be improved.
A possible implementation manner is provided in the embodiment of the present application, where the step S104 allocates corresponding outbound seats to multiple outbound groups, specifically, the following steps D3 and D4 may be included:
step D3, determining a plurality of outbound agents in a working state, and acquiring the preset outbound times of the outbound agents in a specified time period;
and D4, distributing corresponding outbound seats for the outbound groups based on the preset outbound times of the outbound seats in the appointed time period.
The embodiment of the application can determine a plurality of outbound seats in the working state, and acquire the preset outbound times of the plurality of outbound seats in the specified time period, and then based on the preset outbound times of the plurality of outbound seats in the specified time period, distribute corresponding outbound seats for a plurality of outbound groups, distribute corresponding outbound seats for the plurality of outbound groups according to the actual working condition of the outbound seats, and improve the outbound efficiency and the order conversion rate.
In the embodiment of the present application, a possible implementation manner is provided, where in the step D4, based on preset outbound times of a plurality of outbound agents in a specified time period, allocating corresponding outbound agents to a plurality of outbound groups, and specifically may include the following steps D4-1 and D4-2:
d4-1, counting the number of outbound clues of each outbound group;
and D4-2, distributing corresponding outbound seats for the outbound groups according to the preset outbound times of the outbound seats in the appointed time period and the number of the outbound threads of each outbound group.
The number of the respective outbound clues of the plurality of outbound groups is counted, and then the corresponding outbound seats are distributed for the plurality of outbound groups according to the preset outbound times of the plurality of outbound seats in the appointed time period and the number of the respective outbound clues of the plurality of outbound groups, the corresponding outbound seats can be distributed for the plurality of outbound groups according to the actual working condition of the outbound seats and the respective characteristics of the plurality of outbound groups, and the outbound efficiency and the order conversion rate can be improved.
In the embodiment of the present application, a possible implementation manner is provided, where in the step D4, based on preset outbound times of a plurality of outbound agents in a specified time period, allocating corresponding outbound agents to a plurality of outbound groups, and specifically may include the following steps D4-3 and D4-4:
d4-3, distributing different outbound grades for each outbound group according to the plurality of outbound groups;
and D4-4, distributing corresponding outbound seats for the outbound groups according to the preset outbound times of the outbound seats in the appointed time period and the outbound grades of the outbound groups.
The implementation example of the application allocates different outbound grades to each outbound group according to a plurality of outbound groups, and then allocates corresponding outbound seats to a plurality of outbound groups according to preset outbound times of the plurality of outbound seats in a specified time period and respective outbound grades of the plurality of outbound groups, and can allocate corresponding outbound seats to the plurality of outbound groups according to respective characteristics of the plurality of outbound groups and actual working conditions of the outbound seats, so that the outbound efficiency and the order conversion rate can be improved.
In the above, various implementation manners of each link in the embodiment shown in fig. 1 are introduced, and the method for recommending the group of outbound threads provided by the embodiment of the present application is further described by using a specific embodiment.
Fig. 2 is a flowchart illustrating a method for group referral of outbound threads according to another embodiment of the present application, and as shown in fig. 2, the method for group referral of outbound threads may include the following steps S201 to S206.
In step S201, feature data of a plurality of stock outbound threads and feature data of a plurality of new outbound threads are obtained.
In the step, a newly added outbound cue can be monitored, and when the newly added outbound cue is monitored, the characteristic data of the newly added outbound cue is obtained; and the characteristic data of a plurality of newly added outbound threads in the outbound thread characteristic database can be obtained. The outgoing call clue characteristic database is used for storing characteristic data of a batch of outgoing call clues, wherein the outgoing call clues refer to called parties in a call service system and have users with insurance purchase demands, real estate purchase demands, tourist route reservation demands, course purchase demands, automobile purchase demands and the like.
Step S202, inputting the characteristic data of a plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread.
In this step, the ranking model can be trained through the steps a1 to A3 described above, and will not be described herein.
And step S203, setting a grouping threshold interval according to the predicted sequence value of each stock outbound cue and the preset number of outbound groups, and grouping a plurality of stock outbound cues by combining the predicted sequence value of each stock outbound cue and the grouping threshold interval to obtain a plurality of outbound groups.
And step S204, clustering the newly added outbound clues and the plurality of outbound groups according to the characteristic data of the newly added outbound clues, and determining the outbound groups to which the newly added outbound clues are clustered.
Step S205, adding the new outbound cue to the outbound group to which the new outbound cue is clustered.
And step S206, distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats.
In this step, group tags may be added to the outbound groups according to the predicted ranking values of the stock outbound threads, and then corresponding outbound agents are allocated to the outbound groups based on the group tags of the outbound groups. And the corresponding outbound seats are distributed for the outbound groups based on the preset outbound times of the outbound seats in the appointed time period.
According to the method and the device, the quality of the outbound clues is managed, grouping management can be performed on the quality of stock users and newly added users before triggering, corresponding outbound seats are distributed for a plurality of outbound groups, effective triggering can be achieved, and outbound efficiency and order conversion rate are improved.
Fig. 3 is a diagram illustrating an architecture of the outbound cue grouping recommendation in the embodiment of the present application, and in fig. 3, a CRM (Customer Relationship Management) distribution system is combined to perform grouping Management of the stock outbound cue and the new outbound cue, that is, grouping Management of the stock subscriber and the new cue, so as to help a salesperson perform fine operation. Specifically, from top to bottom, the S32 ranking model groups the inventory users and the users newly entering the system in the S31 system, the S33 newly-added users group, the S34 stores the users according to different outbound groups, and finally the S35 is distributed in CRM.
Thread utilization efficiency:
(1) high-quality user recalls are carried out on channels with large clue quantity and the channels are put into a CRM system;
(2) for channels with less clues, the method has the advantages of fine operation, grouped management and improvement of timeliness and user experience;
(3) and the newly added user completes cold start contact in a small data clustering mode.
The grouping management of the users is a part which is important for each enterprise operation, and the embodiment of the application provides an instructive solution for solving the grouping operation problem of massive users and grouping recommendation of real-time clues. Specifically, the embodiment of the application optimizes the management of the users on the basis of the original CRM system, improves the system performance through the management of the number and the quality of the users, saves the cost of operating the users, and improves the order rate of the users under the corresponding sales modules.
The embodiment of the application combines the service scene to more rationalize the arrangement of the user sorting scheme and the grouping threshold interval. And the users with relatively few data dimensions are clustered, so that the purpose of grouping is achieved, and the information grasping degree of the salesperson to the users is improved. The embodiment of the application can be applied to scenes for finely operating users of different group layers, and the data of response and the result threshold value can be adjusted by combining different service scenes.
It should be noted that, in practical applications, all the possible embodiments described above may be combined in a combined manner at will to form possible embodiments of the present application, and details are not described here again.
Based on the method for recommending the outbound cue groups provided by the embodiments, the embodiment of the application also provides a device for recommending the outbound cue groups based on the same inventive concept.
Fig. 4 is a block diagram illustrating an apparatus for group referral of outbound threads according to an embodiment of the present application. As shown in fig. 4, the apparatus for group-based referral of outbound threads may include a first obtaining module 410, a predicting module 420, a grouping module 430 and a referral module 440.
A first obtaining module 410, configured to obtain feature data of a plurality of call-out cues;
the prediction module 420 is configured to input feature data of a plurality of stock outbound threads into a pre-trained ranking model, and predict the ranking value of each stock outbound thread by using the ranking model to obtain a predicted ranking value of each stock outbound thread;
the grouping module 430 is configured to group the plurality of stock outbound threads according to the predicted ranking value of each stock outbound thread to obtain a plurality of outbound groups;
and the recommending module 440 is configured to allocate corresponding outbound seats to the outbound groups, and recommend the outbound groups to the allocated outbound seats.
In the embodiment of the present application, a possible implementation manner is provided, as shown in fig. 5, the apparatus shown in fig. 4 may further include a monitoring module 510, a second obtaining module 520, and a clustering module 530;
a monitoring module 510, configured to monitor a new outbound cue;
a second obtaining module 520, configured to obtain feature data of the new outbound cue when the new outbound cue is monitored;
the clustering module 530 is used for clustering the newly added outbound clues and a plurality of outbound groups according to the characteristic data of the newly added outbound clues and determining the outbound groups to which the newly added outbound clues are clustered; and adding the new outbound clues to the outbound group to which the new outbound clues are clustered.
In the embodiment of the present application, a possible implementation manner is provided, and the grouping module 430 shown in fig. 4 is further configured to:
setting a grouping threshold interval according to the predicted ranking value of each stock outbound cue and the preset number of outbound groups;
and grouping the plurality of stock outbound threads by combining the predicted sequencing values and the grouping threshold intervals of the stock outbound threads to obtain a plurality of outbound groups.
In an embodiment of the present application, a possible implementation manner is provided, and the recommendation module 440 shown in fig. 4 is further configured to:
adding group tags to the outbound groups according to the predicted ranking values of the stock outbound clues;
and distributing corresponding outbound seats for the outbound groups based on the group labels of the outbound groups.
In an embodiment of the present application, a possible implementation manner is provided, and the recommendation module 440 shown in fig. 4 is further configured to:
determining a plurality of outbound agents in a working state, and acquiring preset outbound times of the plurality of outbound agents in a specified time period;
and distributing corresponding outbound agents for the plurality of outbound groups based on the preset outbound times of the plurality of outbound agents in the appointed time period.
In an embodiment of the present application, a possible implementation manner is provided, and the recommendation module 440 shown in fig. 4 is further configured to:
counting the number of outbound clues of each of a plurality of outbound groups;
and distributing corresponding outbound seats for the plurality of outbound groups according to the preset outbound times of the plurality of outbound seats in the appointed time period and the number of the outbound clues of the plurality of outbound groups.
In an embodiment of the present application, a possible implementation manner is provided, and the recommendation module 440 shown in fig. 4 is further configured to:
allocating different outbound grades to each outbound group according to the plurality of outbound groups;
and distributing corresponding outbound seats for the plurality of outbound groups according to the preset outbound times of the plurality of outbound seats in the appointed time period and the respective outbound grades of the plurality of outbound groups.
Based on the same inventive concept, an embodiment of the present application further provides an electronic device, which includes a processor and a memory, where the memory stores a computer program, and the processor is configured to execute the computer program to perform the method for outbound cue clustering recommendation according to any of the above embodiments.
In an exemplary embodiment, there is provided an electronic device, as shown in fig. 6, the electronic device 600 shown in fig. 6 including: a processor 601 and a memory 603. The processor 601 is coupled to the memory 603, such as via a bus 602. Optionally, the electronic device 600 may also include a transceiver 604. It should be noted that the transceiver 604 is not limited to one in practical applications, and the structure of the electronic device 600 is not limited to the embodiment of the present application.
The Processor 601 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 601 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
The Memory 603 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
The memory 603 is used for storing application program codes for executing the scheme of the application, and the processor 601 controls the execution. The processor 601 is configured to execute application program code stored in the memory 603 to implement the content shown in the foregoing method embodiments.
Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
Based on the same inventive concept, the present application further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the method for outbound cue clustering recommendation of any one of the above embodiments when the computer program is run.
It can be clearly understood by those skilled in the art that the specific working processes of the system, the apparatus, and the module described above may refer to the corresponding processes in the foregoing method embodiments, and for the sake of brevity, the detailed description is omitted here.
Those of ordinary skill in the art will understand that: the technical solution of the present application may be essentially or wholly or partially embodied in the form of a software product, where the computer software product is stored in a storage medium and includes program instructions for enabling an electronic device (e.g., a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application when the program instructions are executed. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Alternatively, all or part of the steps of implementing the foregoing method embodiments may be implemented by hardware (an electronic device such as a personal computer, a server, or a network device) associated with program instructions, which may be stored in a computer-readable storage medium, and when the program instructions are executed by a processor of the electronic device, the electronic device executes all or part of the steps of the method described in the embodiments of the present application.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced within the spirit and principle of the present application; such modifications or substitutions do not depart from the scope of the present application.
Claims (10)
1. A method for group referral of outbound cues, comprising:
acquiring characteristic data of a plurality of stock outbound clues;
inputting the characteristic data of the plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread;
grouping the plurality of stock outbound threads according to the predicted ranking value of each stock outbound thread to obtain a plurality of outbound groups;
and distributing corresponding outbound seats for the outbound groups, and recommending the outbound groups to the distributed outbound seats.
2. The method of claim 1, further comprising:
monitoring a newly added outbound cue;
when monitoring that an added outbound cue is newly added, acquiring characteristic data of the added outbound cue;
clustering the newly-added outbound clues and the plurality of outbound groups according to the characteristic data of the newly-added outbound clues, and determining the outbound groups to which the newly-added outbound clues are clustered;
and adding the new calling-out clues to the calling-out group to which the new calling-out clues are clustered.
3. The method of claim 1, wherein grouping the stock outbound threads according to the predicted ranking values of the stock outbound threads to obtain a plurality of outbound groups comprises:
setting a grouping threshold interval according to the predicted ranking value of each stock outbound cue and the preset number of outbound groups;
and grouping the plurality of stock outbound threads by combining the predicted ranking values of the stock outbound threads and the grouping threshold interval to obtain a plurality of outbound groups.
4. The method of claim 1, wherein assigning the outbound call seats to the outbound groups comprises:
adding group tags to the outbound groups according to the predicted ranking values of the stock outbound threads;
and distributing corresponding outbound seats for the outbound groups based on the group labels of the outbound groups.
5. The method of claim 1, wherein assigning the outbound call seats to the outbound groups comprises:
determining a plurality of outbound agents in a working state, and acquiring preset outbound times of the outbound agents in a specified time period;
and distributing corresponding outbound seats for the outbound groups based on the preset outbound times of the outbound seats in a specified time period.
6. The outbound thread grouping recommendation method according to claim 5, wherein allocating corresponding outbound agents to the plurality of outbound groups based on the preset outbound times of the plurality of outbound agents in a specified time period comprises:
counting the number of outbound clues of each of the plurality of outbound groups;
and distributing corresponding outbound seats for the plurality of outbound groups according to the preset outbound times of the plurality of outbound seats in a specified time period and the number of the outbound clues of the plurality of outbound groups.
7. The outbound thread grouping recommendation method according to claim 5, wherein allocating corresponding outbound agents to the plurality of outbound groups based on the preset outbound times of the plurality of outbound agents in a specified time period comprises:
distributing different outbound grades to each outbound group according to the plurality of outbound groups;
and distributing corresponding outbound seats for the outbound groups according to preset outbound times of the outbound seats in a specified time period and the respective outbound grades of the outbound groups.
8. An apparatus for group referral of outbound cues, comprising:
the first acquisition module is used for acquiring the characteristic data of a plurality of stock outbound clues;
the prediction module is used for inputting the characteristic data of the plurality of stock outbound threads into a pre-trained sequencing model, and predicting the sequencing value of each stock outbound thread by using the sequencing model to obtain the predicted sequencing value of each stock outbound thread;
the grouping module is used for grouping the plurality of stock outbound threads according to the predicted sequencing values of the stock outbound threads to obtain a plurality of outbound groups;
and the recommending module is used for distributing corresponding outbound seats for the outbound groups and recommending the outbound groups to the distributed outbound seats.
9. An electronic device, comprising a processor and a memory, wherein the memory has stored therein a computer program, the processor being configured to execute the computer program to perform the method of outbound cue clustering recommendation of any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the method for group referral of outbound cue as claimed in any one of claims 1 to 7 when running.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210025939.8A CN114529150A (en) | 2022-01-11 | 2022-01-11 | Method and device for recommending call-out lines in groups and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210025939.8A CN114529150A (en) | 2022-01-11 | 2022-01-11 | Method and device for recommending call-out lines in groups and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114529150A true CN114529150A (en) | 2022-05-24 |
Family
ID=81621817
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210025939.8A Pending CN114529150A (en) | 2022-01-11 | 2022-01-11 | Method and device for recommending call-out lines in groups and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114529150A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115643340A (en) * | 2022-10-08 | 2023-01-24 | 海南泽山软件科技有限责任公司 | User communication equipment outbound method, device, electronic equipment and computer medium |
-
2022
- 2022-01-11 CN CN202210025939.8A patent/CN114529150A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115643340A (en) * | 2022-10-08 | 2023-01-24 | 海南泽山软件科技有限责任公司 | User communication equipment outbound method, device, electronic equipment and computer medium |
| CN115643340B (en) * | 2022-10-08 | 2024-10-18 | 海南泽山软件科技有限责任公司 | Outbound method and device for user communication equipment, electronic equipment and computer medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108923996B (en) | Capacity analysis method and device | |
| CN112948111B (en) | Task allocation method, device, equipment and computer readable medium | |
| CN110704677A (en) | Program recommendation method and device, readable storage medium and terminal equipment | |
| CN111598360A (en) | Service policy determination method and device and electronic equipment | |
| CN114529150A (en) | Method and device for recommending call-out lines in groups and electronic equipment | |
| CN107688959B (en) | Breakpoint list processing method, storage medium and server | |
| CN110837511B (en) | Data processing method, system and related equipment | |
| CN112286930A (en) | Method, device, storage medium and electronic equipment for resource sharing of redis business side | |
| CN116739279A (en) | Task allocation method, device, computer equipment and medium | |
| CN114549048A (en) | Method and device for recommending outbound user and electronic equipment | |
| CN116342139A (en) | Sales lead distribution method, device, equipment and medium with incentive strategy | |
| CN112651582A (en) | User category identification method for product loss user and related equipment | |
| CN113408817B (en) | Traffic distribution method, device, equipment and storage medium | |
| CN114529143A (en) | Method and device for recommending outbound clues and electronic equipment | |
| CN111951011B (en) | Monitoring system threshold value determining method and device | |
| CN112836971A (en) | Quota resource determination method and device, electronic equipment and storage medium | |
| CN113656046A (en) | Application deployment method and device | |
| CN113723974A (en) | Information processing method, device, equipment and storage medium | |
| CN112131468A (en) | Data processing method and device in recommendation system | |
| CN110837951A (en) | Business channel sequencing method and device and electronic equipment | |
| CN109829103A (en) | A kind of method for down loading and device of application program | |
| CN112036839B (en) | Resource allocation method and related device | |
| CN104240016A (en) | Management method and device for users in virtual place | |
| CN114006981B (en) | Outbound service processing method and device and electronic equipment | |
| CN116385102B (en) | Information recommendation method, device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 100102 201 / F, block C, 2 lizezhong 2nd Road, Chaoyang District, Beijing Applicant after: Beijing Shuidi Technology Group Co.,Ltd. Address before: 100102 201 / F, block C, 2 lizezhong 2nd Road, Chaoyang District, Beijing Applicant before: Beijing Health Home Technology Co.,Ltd. |
|
| CB02 | Change of applicant information |