Predictive Outbound Call Method, Device, Equipment and Storage Medium

Abstract

The present application discloses a predictive outbound calling method, device, equipment and storage medium, relating to the technical field of telephone outbound calling. The method includes: determining the optimal connection time interval of a user according to the user's historical outbound calling information; dynamically dividing the users based on the optimal connection time interval to obtain a number of optimal connection time sub-intervals; detecting in real time whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold; if so, generating a user information outbound calling file corresponding to the optimal connection time sub-interval; and performing an outbound calling task based on the user information outbound calling file through a predictive outbound calling system. By applying the above technical solution, the technical problem in the prior art that the predictive outbound calling system usually makes batch calls to the users in the call list in the same time period, resulting in low calling efficiency and low connection rate, is solved.

Description

Technical Field

[0001] This application relates to the technical field of telephone outbound calls, and particularly to a predictive outbound call method, device, equipment, and storage medium. Background Art

[0002] In a traditional predictive outbound call system, the dialing of a user list is usually carried out in the form of a one-time large batch sending. Specifically, an enterprise usually collects a large number of users, organizes these user data into a file, and sends the entire list to the outbound call system before the call starts. Then, the outbound call system starts to batch dial the users according to the set call loss rate and the used agent group. In this process, the outbound call system adjusts the dialing frequency and call intensity in real time to maximize the utilization efficiency of the agent group and reduce idle time. However, since all users have a fixed order in the call list and a fixed rhythm set by the outbound call system when the outbound call system makes batch calls, all users are uniformly regarded as call targets within the same time period, and different users have different connection habits and connection time periods, resulting in low call efficiency and low connection rate. Summary of the Invention

[0003] The main purpose of this application is to provide a predictive outbound call method, device, equipment, and storage medium, aiming to solve the technical problem that the predictive outbound call system in the prior art usually makes batch calls to the users in the call list within the same time period, resulting in low call efficiency and low connection rate.

[0004] To achieve the above purpose, this application proposes a predictive outbound call method, and the method includes:

[0005] Determine the best connection time interval of the user according to the user's historical outbound call information;

[0006] Dynamically divide the users based on the best connection time interval to obtain several best connection time sub-intervals;

[0007] Real-time detect whether the time interval between the current moment and the best connection time sub-interval is lower than a preset time interval threshold;

[0008] If so, generate a user information outbound call file corresponding to the best connection time sub-interval;

[0009] Execute an outbound call task through a predictive outbound call system based on the user information outbound call file.

[0010] In an embodiment, the step of determining the best connection time interval of the user according to the user's historical outbound call information includes:

[0011] Determine the historical connection times, user preference information, and call time period of the user according to the user's historical outbound call information;

[0012] Based on the historical connection times, the user preference information, and the call time period, determine the target connection success rate corresponding to the user in different time intervals through a preset time prediction model;

[0013] Determine the best connection time interval of the user according to the target connection success rate.

[0014] In one embodiment, before the step of dynamically dividing the users based on the best connection time interval to obtain several best connection time sub-intervals, the following steps are further included:

[0015] Determine the batch grouping number according to the total number of users and the number of target batch users;

[0016] The step of dynamically dividing the users based on the best connection time interval to obtain several best connection time sub-intervals includes:

[0017] Dynamically divide the users based on the batch grouping number and the best connection time interval to obtain several best connection time sub-intervals.

[0018] In one embodiment, the step of performing an outbound call task based on the user information outbound call file through a predictive outbound call system includes:

[0019] Determine the task execution priority corresponding to the target users within the best connection time sub-interval based on the user information outbound call file;

[0020] Perform an outbound call task on the target users through the predictive outbound call system based on the task execution priority.

[0021] In one embodiment, after the step of performing an outbound call task on the target users through the predictive outbound call system based on the task execution priority, the following steps are further included:

[0022] Determine the call failure users from the target users according to the execution result of the outbound call task of the target users;

[0023] Add the call failure users to the outbound call list to be made, and determine the re-call time corresponding to all the call failure users in the outbound call list to be made;

[0024] Group the call failure users based on the re-call time to determine the outbound call list to be re-called;

[0025] Perform a re-call on the outbound call list to be re-called through the predictive outbound call system.

[0026] In one embodiment, after the step of the predictive outbound system performing an outbound task on the target user based on the task execution priority, the following steps are further included:

[0027] Determine the connection success rate corresponding to the optimal connection time sub-interval according to the outbound task execution result of the target user;

[0028] Determine the actual connection situation of the target user according to the outbound task execution result;

[0029] Construct a target optimization function based on the actual connection situation and the connection success rate;

[0030] Optimize the preset time prediction model through the target optimization function;

[0031] Wherein, the target optimization function is:

[0032]

[0033] In the formula, is the target optimization function, is the actual connection situation, is the connection success rate, is the optimal connection time sub-interval, is the model parameter.

[0034] In one embodiment, before the step of determining the optimal connection time interval of the user according to the user's historical outbound information, the following steps are further included:

[0035] Determine the user's outbound status according to the historical outbound task execution result;

[0036] When the user's outbound status is the connection success status, determine the successful call time period corresponding to the current successful connection task and the historical call duration;

[0037] Determine the outbound connection rate and the outbound call loss rate based on the user's outbound status and the historical call duration;

[0038] Train the initial time prediction model based on the successful call time period, the outbound connection rate, and the outbound call loss rate to obtain a preset time prediction model.

[0039] In addition, to achieve the above object, the present application also proposes a predictive outbound device, and the device includes:

[0040] A time prediction module, configured to determine the optimal connection time interval of the user according to the user's historical outbound information;

[0041] A dynamic batch division module, configured to dynamically divide the users based on the optimal connection time interval to obtain a plurality of optimal connection time sub-intervals;

[0042] A time interval detection module, configured to detect in real time whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold;

[0043] An outbound call file generation module, configured to, if so, generate a user information outbound call file corresponding to the optimal connection time sub-interval;

[0044] An outbound call task execution module, configured to execute an outbound call task based on the user information outbound call file through a predictive outbound call system.

[0045] In addition, to achieve the above object, the present application further provides a predictive outbound call device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the predictive outbound call method as described above.

[0046] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the predictive outbound call method as described above.

[0047] The present application provides a predictive outbound call method. The present application discloses determining an optimal connection time interval of a user according to the historical outbound call information of the user; dynamically dividing the users based on the optimal connection time interval to obtain a plurality of optimal connection time sub-intervals; detecting in real time whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold; if so, generating a user information outbound call file corresponding to the optimal connection time sub-interval; executing an outbound call task based on the user information outbound call file through a predictive outbound call system; compared with the prior art, when the outbound call system in the prior art makes a batch call to users according to the user list, due to the fixed call order and rhythm, and different connection habits and connection time periods of different users, the call efficiency is not high and the connection rate is low. Since the present invention can dynamically divide the optimal connection time interval of the user to obtain a plurality of optimal connection time sub-intervals, and make an outbound call to the user based on the user outbound call file when the current moment is close to the optimal connection time sub-interval, thereby solving the technical problem that the predictive outbound call system in the prior art usually makes a batch call to the users in the call list in the same time period, resulting in low call efficiency and low connection rate. Description of the Drawings

[0048] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the predictive outbound calling method of this application;

[0051] Figure 2 It is a flowchart for generating a time prediction model in the predictive outbound calling method of this application;

[0052] Figure 3 It is a schematic flowchart provided for Embodiment 2 of the predictive outbound calling method of this application;

[0053] Figure 4 It is a flowchart for processing batch calling tasks in the predictive outbound calling method of this application;

[0054] Figure 5 It is a schematic flowchart provided for Embodiment 3 of the predictive outbound calling method of this application;

[0055] Figure 6 It is a flowchart for the redial strategy in the predictive outbound calling method of this application;

[0056] Figure 7 It is a flowchart for optimizing the outbound calling strategy in the predictive outbound calling method of this application;

[0057] Figure 8 It is a overall flowchart of the outbound calling system in the predictive outbound calling method of this application;

[0058] Figure 9 It is a schematic diagram of the module structure of the predictive outbound calling device according to an embodiment of this application;

[0059] Figure 10 It is a schematic diagram of the device structure of the hardware operating environment involved in the predictive outbound calling method according to an embodiment of this application.

[0060] The realization of the purpose, functional features, and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0061] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0062] To better understand the technical solution of this application, the following will be described in detail in combination with the accompanying drawings of the specification and specific implementation manners.

[0063] The main solution of the embodiment of this application is: determining the optimal connection time interval of the user according to the user's historical outbound call information; dynamically dividing the user based on the optimal connection time interval to obtain several optimal connection time sub-intervals; real-time detecting whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold; if so, generating a user information outbound call file corresponding to the optimal connection time sub-interval; and performing an outbound call task based on the user information outbound call file through a predictive outbound call system.

[0064] Since in the prior art, when an outbound call system makes batch calls to users based on a user list, since the order of all users in the call list and the rhythm set by the outbound call system are fixed, all users are uniformly regarded as call objects within the same time period, and the connection habits and connection time periods of different users are different, resulting in low call efficiency and low connection rate.

[0065] This application provides a solution, which can dynamically divide the optimal connection time interval of the user to obtain several optimal connection time sub-intervals, and make an outbound call to the user based on the user outbound call file when the current moment is close to the optimal connection time sub-interval, thus solving the technical problem that the predictive outbound call system in the prior art usually makes batch calls to the users in the call list within the same time period, resulting in low call efficiency and low connection rate.

[0066] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a predictive outbound call system, etc. that can implement the above functions. Hereinafter, taking the predictive outbound call system as an example (hereinafter referred to as the device), this embodiment and the following embodiments will be described.

[0067] Based on this, the embodiment of this application provides a predictive outbound call method, referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the predictive outbound call method of this application.

[0068] In this embodiment, the predictive outbound call method includes steps S10 to S50:

[0069] Step S10: Determining the optimal connection time interval of the user according to the user's historical outbound call information.

[0070] It should be understood that the above historical outbound call information can be the outbound call information of the user within a past period of time, such as the outbound call connection time, call times, connection success rate, etc. of the user within a past period of time. This embodiment places no restrictions on this.

[0071] It should be noted that the above optimal connection time interval can be the optimal time interval for the user to answer the phone, that is, the time interval when the probability of the user answering the phone is the highest when making an outbound call to the user. In this embodiment, the historical outbound call information of the user can be input into a time prediction model, and the optimal connection time interval of the user can be output through calculation by the time prediction model based on the historical outbound call information of the user.

[0072] Step S20: Dynamically divide the user based on the optimal connection time interval to obtain several optimal connection time sub-intervals.

[0073] It can be understood that the above optimal connection time sub-interval can be a sub-interval within the optimal connection time interval. In this embodiment, the system can dynamically divide the user according to the optimal connection time interval, so that the user can be divided into multiple different time intervals, and at this time, several optimal connection time sub-intervals can be obtained. Among them, the users divided into this optimal connection time sub-interval have the highest connection rate within this time sub-interval. Compared with the traditional outbound call system that dials users in batches at one time, this solution can dynamically divide the time interval and make outbound calls during the period when the user connection rate is high, thereby greatly improving the success rate of outbound calls.

[0074] Step S30: Real-time detect whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold.

[0075] It should be noted that the above preset time interval threshold can be the minimum time value used to characterize the proximity between the current moment and the optimal connection time sub-interval.

[0076] Step S40: If so, generate a user information outbound call file corresponding to the optimal connection time sub-interval.

[0077] It should be noted that the above user information outbound call file can be a file composed of the outbound call information of the users belonging to the optimal connection time sub-interval. In this embodiment, the user information outbound call file can contain information such as user contact information and the optimal outbound call time.

[0078] Step S50: Execute an outbound call task based on the user information outbound call file through a predictive outbound call system.

[0079] It should be understood that the above predictive outbound call system can be a system used to perform outbound call operations on users.

[0080] In practical applications, the system can dynamically divide users according to the predicted optimal connection time intervals of the users, divide the users into multiple different optimal connection time sub-intervals, and automatically collect the information of the users belonging to the optimal connection time sub-interval when the current moment approaches the optimal connection time sub-interval. Then, based on the user information, a user information outbound call file is generated. After the user information outbound call file is generated, the system can send the user information outbound call file to the predictive outbound call system, so that the predictive outbound call system can perform outbound call operations on the users according to the user information outbound call file to ensure that the outbound call tasks are carried out during the time periods with a relatively high user connection rate, thereby improving the success rate of outbound calls. In addition, business personnel can set multiple time prediction strategies, and the system can dynamically adjust the outbound call time of each batch of customers according to these strategies. For example, when the system predicts that the outbound call success rate in a certain time period is high, the system can give priority to arranging the outbound call tasks in that time period. In this embodiment, by allocating the outbound call tasks in batches according to time intervals, the system can make more reasonable use of outbound call resources, reduce ineffective calls, and improve the overall operation efficiency of the system.

[0081] Further, before the step S10, the method further includes: determining the user outbound call status according to the execution results of historical outbound call tasks; when the user outbound call status is the connection success status, determining the successful call time period and the historical call duration corresponding to the current successfully connected task; determining the outbound call connection rate and the outbound call loss rate based on the user outbound call status and the historical call duration; training the initial time prediction model based on the successful call time period, the outbound call connection rate, and the outbound call loss rate to obtain a preset time prediction model.

[0082] It can be understood that the above historical outbound call task execution results can be the results obtained from performing outbound call tasks on users in the past period of time; the above user outbound call status can be the connection status of the user when making an outbound call to the user. In this embodiment, the user outbound call status can include, but is not limited to, the connection success status, the connection failure status, etc.

[0083] It should be understood that the above current successfully connected task can be an outbound call task successfully connected by the user. In this embodiment, the task with the user outbound call status of the connection success status can be determined as the current successfully connected task. The above successful call time period can be the time period when the user successfully connects the current successfully connected task; correspondingly, the above historical call duration can be the call duration after the user connects the current successfully connected task.

[0084] It should be noted that the above outbound call connection rate can be the probability that the user connects the outbound call task during the automatic outbound call process of the system.

[0085] It should be noted that the above-mentioned outbound call loss rate can be the probability that the call fails to be successfully assigned to the terminal seat due to various reasons during the automatic outbound call process of the system. In practical applications, the outbound call loss rate can reflect the loss situation of the outbound call system during the call process.

[0086] In this embodiment, the system can divide the number of outbound call tasks with the outbound call status of successful outbound call within a past period of time by the total number of outbound call tasks within this time period to determine the outbound call connection rate during this time period. At the same time, the system can divide the number of outbound call tasks with the outbound call status of failed outbound call within a past period of time by the total number of outbound call tasks within this time period to determine the outbound call loss rate during this time period. Among them, since there may be accidental touch situations when the user answers the phone, this embodiment can determine whether the user's current answer of the phone belongs to an accidental touch situation according to the historical call duration. If so, the connection status of this outbound call task is still determined as the connection failure status, and the number of outbound call tasks with the outbound call status of successful outbound call and the number of outbound call tasks with the outbound call status of failed outbound call of the user within this time period are updated. Then, the outbound call connection rate and the outbound call loss rate are calculated based on the updated number of outbound call tasks. Finally, the system can train the initial time prediction model based on the successful call time period, the outbound call connection rate, and the outbound call loss rate to obtain a preset time prediction model. Specifically, the system can input the successful call time period, the outbound call connection rate, and the outbound call loss rate of the outbound call tasks within a past period of time into the untrained time prediction model for training and optimization, so that the time prediction model can predict the most suitable time for outbound calls at different times of each day, and the preset time prediction model is obtained after continuous iteration.

[0087] In specific implementation, in order to improve the success rate of outbound call tasks, this solution can introduce a time prediction model to facilitate dynamically adjusting the execution time of outbound call tasks. Refer to Figure 2 , Figure 2 is the generation flowchart of the time prediction model in the predictive outbound call method of this application. As Figure 2As shown in the figure, the system can first collect and analyze the user's historical outbound call data, such as the successful call time, call duration, etc., and determine the successful connection rate and call loss rate of outbound call tasks within a certain period of time based on this historical outbound call data. Then, it divides time intervals based on the successful connection rate and call loss rate to train a time prediction model. The model can make predictions according to factors such as different time periods, user behaviors, and success rates, and output the most suitable outbound call time periods for different time periods of each day. Finally, a trained time prediction model is obtained and updated to the outbound call task scheduling system. In addition, in this solution, the system can optimize and analyze the execution effect of outbound call tasks in real time through data analysis, generate key operation data such as connection rates and call loss rates. By analyzing these data, the system can further optimize the time prediction model and batch allocation strategy, so as to achieve continuous optimization and iteration and improve the overall effect of outbound call tasks.

[0088] This embodiment provides a predictive outbound call method, which discloses determining the optimal connection time interval of a user according to the user's historical outbound call information; dynamically dividing users based on the optimal connection time interval to obtain several optimal connection time sub-intervals; real-time detecting whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold; if so, generating a user information outbound call file corresponding to the optimal connection time sub-interval; performing an outbound call task based on the user information outbound call file through a predictive outbound call system; compared with the prior art, when the outbound call system makes batch calls to users according to the user list, due to the fixed call order and rhythm, and the connection habits and connection time periods of different users are different, resulting in low call efficiency and low connection rate. Since this embodiment can dynamically divide the optimal connection time interval of users to obtain several optimal connection time sub-intervals, and make outbound calls to users based on the user outbound call file when the current moment is close to the optimal connection time sub-interval, it solves the technical problem that the predictive outbound call system in the prior art usually makes batch calls to users in the call list in the same time period, resulting in low call efficiency and low connection rate.

[0089] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as the above-mentioned embodiment one can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , Figure 3 which is the flowchart provided for the second embodiment of the predictive outbound call method of the present application.

[0090] In this embodiment, step S10 includes steps S101 to S103:

[0091] Step S101: Determine the historical connection times, user preference information, and call time period of the user according to the user's historical outbound call information.

[0092] It should be understood that the above historical outbound call information can be the information obtained after performing outbound call tasks on users within a certain period of time in the past. In this embodiment, the historical outbound call information may include at least one of the user's historical connection times, user preference information, and call time period. Among them, the historical connection times can be the number of times the user has connected to the outbound call task within a certain period of time in the past; the user preference information can be the preferred time period for the user to connect to the outbound call task within a certain period of time in the past; the call time period can be the time period when the outbound call task makes an outbound call to the user.

[0093] Step S102: Based on the historical connection times, the user preference information, and the call time period, determine the target connection success rate corresponding to the user in different time intervals through a preset time prediction model.

[0094] It can be understood that the above target connection success rate can be the probability that the preset time prediction model predicts that the user connects to the outbound call task in different time intervals.

[0095] Step S103: Determine the best connection time interval of the user according to the target connection success rate.

[0096] In this embodiment, the system can input the user's historical connection times, user preference information, and call time period into the preset time prediction model, so that the preset time prediction model predicts the probability that the user connects to the outbound call task in different time intervals, obtains the target connection success rate, and then can determine the time interval with the highest target connection success rate as the best connection time interval of the user.

[0097] In practical applications, to optimize the outbound call success rate, this embodiment can use a time prediction model to analyze the connection probability of each user at different time periods of a day. Specifically, historical data can be used and a regression model can be adopted to predict the connection probability P(t) at different time periods, where t represents the specific time period of a day. At this time, the time prediction model can be expressed as: P(t)=f(X), where f(X) represents a machine learning regression model, and X is a feature vector, which includes information such as historical connection situations, customer preferences, and call time periods. For example, for VIP customers, the time prediction model predicts that the connection probability from 2 pm to 4 pm is 70%. Therefore, the outbound call tasks for VIP customers are preferentially arranged during this time period.

[0098] Furthermore, before the step S20, it further includes: determining the number of batch groups according to the total number of users and the number of target batch users.

[0099] It should be noted that, in order to make the outbound task more effective, this solution proposes a method of splitting the user list into multiple batches for processing. First, the outbound system can split the user list to be called into several batches according to a predetermined strategy. Specifically, in this embodiment, it can be divided into several batches according to business requirements or outbound strategies. Each batch can be further segmented according to customer characteristics (such as geographical location, user type, contact frequency), and the number of users in each batch should be appropriate to facilitate subsequent outbound plans. Refer to Figure 4 , Figure 4 which is the processing flowchart of the batch calling task in the predictive outbound method of this application. As Figure 4 shown, when making an outbound call, first, a batch task can be generated, the user list to be called is split into multiple batches for processing, and the time interval for each batch of users to make a call is determined. Then, these users are sent to the predictive outbound system in batches for making calls through the predictive outbound system. After completing the outbound task of one batch, the system can wait for all calls in this batch to complete and then start the outbound task of the next batch. In addition, for customers who need to be redialed, the system will add them to the next batch to be called according to the preset interval time for redialing.

[0100] It should be understood that the above-mentioned target batch user quantity can be the number of users called in each batch of the batch outbound task; the above-mentioned batch grouping quantity can be the number of this batch of tasks. For example, if the total number of users is N and the number of users in each batch is n, then the batch grouping quantity B = N / n.

[0101] Correspondingly, the step S20 includes: dynamically dividing the users based on the batch grouping quantity and the optimal connection time interval to obtain several optimal connection time sub-intervals.

[0102] It can be understood that after determining the batch grouping quantity B, the users can be dynamically divided into B batches, and the optimal connection time of the users in each batch is within the corresponding optimal connection time sub-interval.

[0103] Further, the step S50 includes: determining the task execution priority corresponding to the target users within the optimal connection time sub-interval based on the user information outbound file; and performing the outbound task on the target users through the predictive outbound system based on the task execution priority.

[0104] It should be understood that the above-mentioned task execution priority can be the priority for making an outbound call to the target users within the optimal connection time sub-interval. In this embodiment, the system can give priority to making outbound calls to users with a higher priority.

[0105] In a specific implementation, for each task batch B_k, the system can generate an outbound call file containing user information according to the predicted optimal time period T_optimal. Among them, the file format can be File(B_k, T_optimal). Then the system can transmit the file to the predictive outbound call system, so that the predictive outbound call system can execute tasks in the T_optimal time period in the order of priority until all users in the batch have been called.

[0106] In this embodiment, it is disclosed to determine the historical connection times, user preference information, and call time period of a user according to the user's historical outbound call information; based on the historical connection times, user preference information, and call time period, determine the target connection success rate corresponding to the user in different time intervals through a preset time prediction model; determine the optimal connection time interval of the user according to the target connection success rate, so that outbound calls can be made in the time period with a high user connection rate, thereby improving the success rate of outbound calls.

[0107] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, for the same or similar content as in the above embodiments, reference can be made to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 5 , Figure 5 which is a schematic flowchart provided for the third embodiment of the predictive outbound call method of the present application.

[0108] In this embodiment, after step S50, the method further includes steps S601 to S604:

[0109] Step S601: Determine the call failure users from the target users according to the execution results of the outbound call tasks of the target users.

[0110] It can be understood that the above call failure users can be users who did not answer or had incorrect numbers among the target users. In this embodiment, the system can determine the outbound call status of all target users according to the execution results of the outbound call tasks of the target users, including the connection success status and the connection failure status, and then can determine all users in the connection failure status as call failure users.

[0111] Step S602: Add the call failure users to the outbound call list to be made, and determine the re-call time corresponding to all call failure users in the outbound call list to be made.

[0112] It should be understood that the above outbound call list to be made can be a list for storing information of users who need to be redialed. The above re-call time can be the time for making the next outbound call to the call failure users.

[0113] Step S603: Group the call failure users based on the re-call time to determine the groups to be re-called.

[0114] Step S604: Re-call the to-be-re-called group through the predictive outbound call system.

[0115] In this embodiment, referring to Figure 6 , Figure 6 is the flowchart of the redial strategy in the predictive outbound call method of this application. As Figure 6 shown, after executing the current outbound call task, the system can analyze the reasons why the call fails for the users who are not connected according to the execution result of this outbound call task, and judge whether to redial them according to these reasons. If so, the users who need to be redialed can be added to the to-be-outbound call list, and then the redial time of all the call-failed users in the to-be-outbound call list can be predicted through a preset time prediction model, and these call-failed users can be called in batches based on the redial time. Finally, it is judged whether these call-failed users are connected during the redial. If they are connected, the successful result can be recorded and the outbound call status of the call-failed users can be switched to the connected successfully status.

[0116] In practical applications, if the outbound call result is represented by the variable R_i, where R_i = 1 indicates successful connection and R_i = 0 indicates not connected, the system classifies and statistically analyzes the outbound call results of all customers: S = ∑R_i, where S represents the total number of connections. For the customers who are not connected (R_i = 0), the system can add them to the to-be-outbound call list of the next batch B_{k + 1} to further improve the connection rate.

[0117] Further, after step S50, the method further includes: determining the connection success rate corresponding to the optimal connection time sub-interval according to the execution result of the outbound call task of the target user; determining the actual connection situation of the target user according to the execution result of the outbound call task; constructing a target optimization function based on the actual connection situation and the connection success rate; optimizing the preset time prediction model through the target optimization function;

[0118] Among them, the target optimization function is:

[0119]

[0120] In the formula, is the target optimization function, is the actual connection situation, is the connection success rate, is the optimal connection time sub-interval, is the model parameter.

[0121] It can be understood that the above target optimization function can be a function for optimizing a preset time prediction model. The above actual connection situation can be a characteristic value used to represent whether the target user is connected. If the user is connected, the value is 1; if the user is not connected, the value is 0.

[0122] In this embodiment, referring to Figure 7 , Figure 7 is the flowchart for optimizing the outbound call strategy in the predictive outbound call method of this application. As Figure 7 shown, after the system finishes executing an outbound call task each time, it can collect call data to generate an analysis report of the outbound call task, count data such as the outbound call success rate and call duration in different time periods, and generate a connection rate report and a call loss rate report based on these data. Then, it analyzes the report data, so as to adjust and optimize the actual prediction model in combination with the actual effect of the outbound call task to improve the success rate of subsequent outbound call tasks. For example, the system can count the connection rates in different time periods and calculate the connection success rate P_success(t)=∑(R_i) / |T| in each time period, where T represents all customer sets in time period t, and |T| is the number of users. Then the system can make fine-tuning of the model according to the feedback of the connection success rate to achieve higher prediction accuracy. The model adjustment can be achieved based on the optimization loss function L_adjust(θ), such as:

[0123]

[0124] In the formula, y_i is the actual connection situation (1 for connected and 0 for not connected), f(X) is a machine learning regression model, X is the feature vector, which can include information such as historical connection situations, customer preferences, and call time periods, and θ is the model parameter.

[0125] Through repeated optimization and testing, the model finally obtains the best outbound call time periods for different customer categories, ensuring that the success rate and efficiency of the outbound call task reach the highest.

[0126] In the specific implementation, referring to Figure 8 , Figure 8 is the overall flowchart of the outbound call system in the predictive outbound call method of this application. As Figure 8As shown, first, data on outbound calls to users over a past period of time can be collected, and a time prediction model can be trained and generated based on the users' historical outbound call information. Then, the users can be dynamically divided according to the users' optimal call connection time intervals, and the users can be divided into multiple batches. Furthermore, these users can be sent to the predictive outbound call system in batches for outbound calls. At this time, the system can determine whether the user is connected according to the outbound call result. If connected, the successful result is recorded; if not connected, the user can be added to the redial task to facilitate redialing of users with call failures later. Among them, for customers who need to be redialed, the system can add them back to the next batch to be outbound-called according to the preset interval time.

[0127] In this embodiment, it is disclosed to determine call-failed users from target users according to the execution results of outbound call tasks of the target users; add the call-failed users to the outbound call list to be called, and determine the re-call times corresponding to all call-failed users in the outbound call list to be called; group the call-failed users based on the re-call times to determine the groups to be re-called; and re-call the groups to be re-called through the predictive outbound call system, so that users with outbound call failures can be redialed based on the re-call times, further improving the success rate of outbound calls.

[0128] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the predictive outbound call method of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0129] This application also provides a predictive outbound call device. Please refer to Figure 9 , and the predictive outbound call device includes:

[0130] A time prediction module 10, configured to determine the optimal call connection time interval of the user according to the user's historical outbound call information;

[0131] A dynamic batch division module 20, configured to dynamically divide the user based on the optimal call connection time interval to obtain several optimal call connection time sub-intervals;

[0132] A time interval detection module 30, configured to detect in real time whether the time interval between the current moment and the optimal call connection time sub-interval is lower than a preset time interval threshold;

[0133] An outbound call file generation module 40, configured to, if so, generate a user information outbound call file corresponding to the optimal call connection time sub-interval;

[0134] An outbound call task execution module 50, configured to execute an outbound call task through the predictive outbound call system based on the user information outbound call file.

[0135] The predictive outbound calling device provided by the present application adopts the predictive outbound calling method in the above-mentioned embodiment, which can solve the technical problems in the prior art that the predictive outbound calling system usually makes batch calls to users in the call list in the same time period, resulting in low calling efficiency and low connection rate. Compared with the prior art, the beneficial effects of the predictive outbound calling device provided by the present application are the same as those of the predictive outbound calling method provided by the above-mentioned embodiment, and other technical features in the predictive outbound calling device are the same as those disclosed in the method of the above-mentioned embodiment, which will not be elaborated here.

[0136] The present application provides a predictive outbound calling device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the predictive outbound calling method in the first embodiment above.

[0137] Reference is made below to Figure 10 , which shows a schematic structural diagram of a predictive outbound calling device suitable for implementing the embodiments of the present application. The predictive outbound calling device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 10 The predictive outbound calling device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0138] As Figure 10As shown, the predictive outbound calling device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the predictive outbound calling device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the predictive outbound calling device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a predictive outbound calling device with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be implemented or had alternatively.

[0139] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0140] The predictive outbound calling device provided by the present application adopts the predictive outbound calling method in the above embodiments and can solve the technical problems of predictive outbound calling. Compared with the prior art, the beneficial effects of the predictive outbound calling device provided by the present application are the same as those of the predictive outbound calling method provided by the above embodiments, and other technical features in the predictive outbound calling device are the same as the features disclosed in the method of the previous embodiment, which will not be elaborated here.

[0141] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0142] As described above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0143] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the predictive outbound calling method in the above embodiments.

[0144] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0145] The above computer-readable storage medium can be included in the predictive outbound calling device; it can also exist alone without being assembled into the predictive outbound calling device.

[0146] The above computer-readable storage medium carries one or more programs, which, when executed by a predictive outbound calling device, cause the predictive outbound calling device to: determine the optimal connection time interval of the user according to the user's historical outbound calling information; dynamically divide the user based on the optimal connection time interval to obtain a plurality of optimal connection time sub-intervals; detect in real time whether the time interval between the current moment and the optimal connection time sub-interval is lower than a preset time interval threshold; if so, generate a user information outbound calling file corresponding to the optimal connection time sub-interval; and execute an outbound calling task based on the user information outbound calling file through a predictive outbound calling system.

[0147] Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0148] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0149] The modules involved in the embodiments of the present application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0150] The readable storage medium provided by the present application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above predictive outbound calling method, which can solve the technical problems in the prior art that the predictive outbound calling system usually makes batch calls to users in the call list during the same time period, resulting in low call efficiency and low connection rate. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the predictive outbound calling method provided by the above embodiments, and will not be elaborated here.

[0151] The above are only some embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A predictive outbound calling method, characterized in that: The method includes: Determine the best connection time interval for the user according to the user's historical outbound call information; Dynamically dividing the user based on the optimal connection time interval to obtain a plurality of optimal connection time subintervals; Real-time detection of whether the time interval between the current moment and the optimal connection time subinterval is lower than a preset time interval threshold; If yes, then generating a user information outbound call file corresponding to the optimal connection time subinterval; Executing an outbound calling task based on the user information outbound calling file by a predictive outbound calling system; The step of determining the optimal connection time interval of the user according to the historical outbound call information of the user comprises: Determine the user's historical connection times, user preference information and calling time period according to the user's historical outbound call information; Based on the historical connection times, the user preference information and the call time period, determining the target connection success rate of the user corresponding to different time intervals through a preset time prediction model; Determining the optimal connection time interval for the user according to the target connection success rate; The step of executing the outbound calling task based on the user information outbound calling file by the predictive outbound calling system includes: Determine the task execution priority corresponding to the target user in the optimal connection time subinterval based on the user information outbound call file; Executing an outbound calling task for the target user based on the task execution priority by a predictive outbound calling system; After the step of executing the outbound calling task for the target user based on the task execution priority by the predictive outbound calling system, the method further includes: Determine the connection success rate corresponding to the optimal connection time subinterval according to the outbound call task execution result of the target user; Determine the actual connection status of the target user according to the execution result of the outbound call task; Constructing a target optimization function based on the actual connection situation and the connection success rate; Optimizing the preset time prediction model by using the target optimization function; Wherein, the objective optimization function is: In the formula, Optimize the function for the objective, is the actual connection situation, is the connection success rate, is the optimal on-time subinterval, is the model parameter.

2. The method according to claim 1, characterized in that Before the step of dynamically dividing the user based on the optimal connection time interval to obtain a plurality of optimal connection time subintervals, the method further includes: Determine the number of batch groups based on the total number of users and the number of users in the target batch; The step of dynamically dividing the user based on the optimal connection time interval to obtain a plurality of optimal connection time subintervals includes: The users are dynamically divided based on the batch grouping quantity and the optimal connection time interval to obtain a plurality of optimal connection time subintervals.

3. The method according to claim 1, characterized in that After the step of executing the outbound calling task for the target user based on the task execution priority by the predictive outbound calling system, the method further includes: Determine a call failure user from the target users according to the outbound call task execution result of the target users; Adding the call failure users to a waiting outbound call list, and determining the re-call time corresponding to all the call failure users in the waiting outbound call list; Grouping the call failure users based on the re-call time to determine a group to be re-called; The group to be recalled is called again through a predictive outbound calling system.

4. The method according to claim 1, characterized in that Before the step of determining the optimal connection time interval of the user according to the historical outbound call information of the user, the method further includes: Determine the user's outbound calling status based on historical outbound calling task execution results; When the user's outbound call status is a successfully connected state, determining the successful call time period and historical call duration corresponding to the current successfully connected task; Determine the outbound call connection rate and the outbound call loss rate based on the user's outbound call status and the historical call duration; The initial time prediction model is trained based on the successful call time period, the outbound call connection rate and the outbound call loss rate to obtain a preset time prediction model.

5. A predictive outbound calling device, characterized in that: The device comprises: A time prediction module, used to determine the optimal connection time interval of the user according to the user's historical outbound call information; A dynamic batch division module, used for dynamically dividing the users based on the optimal connection time interval to obtain a plurality of optimal connection time subintervals; A time interval detection module, used for detecting in real time whether the time interval between the current moment and the optimal connection time subinterval is lower than a preset time interval threshold; An outbound call file generation module, configured to generate a user information outbound call file corresponding to the optimal connection time subinterval; An outbound call task execution module, used for executing an outbound call task based on the user information outbound call file through a predictive outbound call system; The time prediction module is further used to determine the user's historical connection times, user preference information and call time period according to the user's historical outbound call information; based on the historical connection times, the user preference information and the call time period, determine the target connection success rate of the user corresponding to different time intervals through a preset time prediction model; determine the user's optimal connection time interval according to the target connection success rate; The outbound call task execution module is further used to determine the task execution priority corresponding to the target user in the optimal connection time subinterval based on the user information outbound call file; execute the outbound call task for the target user based on the task execution priority through the predictive outbound call system; determine the connection success rate corresponding to the optimal connection time subinterval according to the outbound call task execution result of the target user; determine the actual connection situation of the target user according to the outbound call task execution result; construct a target optimization function based on the actual connection situation and the connection success rate; optimize the preset time prediction model through the target optimization function; wherein the target optimization function is: In the formula, Optimize the function for the objective, is the actual connection situation, is the connection success rate, is the optimal on-time subinterval, is the model parameter.

6. A predictive outbound calling device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the predictive outbound calling method according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the predictive outbound calling method according to any one of claims 1 to 4 are implemented.

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