Method and device for determining call answering probability, equipment, medium and program product
By integrating customer call data and attribute data, the target call answer rate is calculated, which solves the problem of low answer rate in traditional call methods and achieves more efficient call resource allocation and prediction.
Patent Information
- Application Number
- CN202511682290.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-02-13
AI Technical Summary
Traditional calling methods have a low answer rate when calling customers during weekday working hours, resulting in wasted resources. Existing technology makes it difficult to effectively predict the probability of customers answering calls.
By acquiring customer call data and attribute data, the first call answer rate and the second call answer rate are determined respectively, and then fused together to calculate the target call answer rate in order to predict the probability of customers answering calls at different times.
It improved the accuracy of call answer rate prediction, optimized the allocation of call resources, saved call costs, and improved call efficiency.
Smart Images
Figure CN121531070A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data processing, and can be applied to the field of financial technology, and particularly relates to a call answering probability determination method and device, a medium and a program product. BACKGROUND
[0002] With the popularity of mobile devices, there are more and more mobile applications. As an important marketing method, intelligent call marketing is an important channel for enterprises to maintain customer relationships and promote financial products such as credit card binding, installment, loan, and financial products.
[0003] However, the traditional call method usually calls customers during weekdays and working hours. The call method is relatively extensive, and the customer answering rate is low, resulting in a large amount of resources wasted on unsuccessful call attempts. SUMMARY
[0004] The present application provides a call answering probability determination method, device, medium and program product to realize the prediction of the probability of customers answering calls.
[0005] Call data of at least one customer is obtained, and attribute data of each customer is obtained; the call data includes a call period and a call answering result;
[0006] According to the call data of each customer, a first call answering rate of each customer in each call period is determined;
[0007] According to the call data and attribute data of each customer, a second call answering rate of each customer in each call period is determined;
[0008] For each call period, the first call answering rate of the customer in the call period and the second call answering rate of the customer in the call period are fused to determine the target call answering rate of the customer in the call period.
[0009] In a second aspect, the present application also provides a call answering probability determination device, comprising:
[0010] The acquisition module is configured to obtain call data of at least one customer, and obtain attribute data of each customer; the call data includes a call period and a call answering result;
[0011] The first probability determination module is configured to determine a first call answering rate of each customer in each call period according to the call data of each customer;
[0012] The second probability determination module is configured to determine a second call answering rate of each customer in each call period according to the call data and attribute data of each customer;
[0013] The target probability determination module is configured to, for each call period, fuse the first call answering rate of the customer in the call period and the second call answering rate of the customer in the call period to determine a target call answering rate of the customer in the call period.
[0014] In a third aspect, an electronic device is provided, comprising:
[0015] at least one processor; and
[0016] a memory in communication with the at least one processor; wherein
[0017] 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 perform the method for determining a call answering probability according to any of the embodiments of the present application.
[0018] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a call answering probability according to any of the embodiments of the present application when the processor executes the computer instructions.
[0019] In a fifth aspect, a computer program product is provided, and the computer program product comprises a computer program, and the computer program is used to implement the method for determining a call answering probability according to any of the embodiments of the present application when the computer program is executed by a processor.
[0020] The technical solution of the embodiments of the present application comprises the following steps: obtaining call data of at least one customer and attribute data of each customer; the call data comprises a call period and a call answering result; determining a first call answering rate of each customer in each call period according to the call data of each customer; determining a second call answering rate of each customer in each call period according to the call data and the attribute data of each customer; for each call period, fusing the first call answering rate of the customer in the call period and the second call answering rate of the customer in the call period to determine a target call answering rate of the customer in the call period. The present application realizes the prediction of the probability of answering a call of a customer in each call period.
[0021] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS
[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.
[0023] Figure 1 is a flow chart of a call answering probability determination method according to an embodiment of the present application;
[0024] Figure 2 is a flow chart of a call answering probability determination method according to an embodiment of the present application;
[0025] Figure 3 is a structural schematic diagram of a call answering probability determination device according to an embodiment of the present application;
[0026] Figure 4 is a structural diagram of an electronic device for implementing a call answering probability determination method according to an embodiment of the present application. DETAILED DESCRIPTION
[0027] In order to make the person skilled in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of the present application.
[0028] It should be noted that the terms "first", "second", "third", and "auxiliary" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.
[0029] In the technical solutions of the embodiments of the present application, the acquisition, storage and application of call time period, attribute data and call answering result, etc. are in line with the relevant legal regulations and do not violate public order and good customs.
[0030] Embodiment one
[0031] Figure 1 A flowchart of a call answering probability determination method provided by Embodiment one of the present application, the embodiment can be applied to the case of determining the probability of a customer answering a call, and the method can be executed by a call answering probability determination device, which can be realized in the form of hardware and / or software and specifically configured in an electronic device.
[0032] Referring to Figure 1 The call answering probability determination method shown in the figure comprises:
[0033] S101, call data of at least one customer is acquired, and attribute data of each customer is acquired; the call data comprises a call period and a call answering result.
[0034] S102, according to the call data of each customer, a first call answering rate of each customer in each call period is determined.
[0035]
[0035] S103, according to the call data and attribute data of each customer, a second call answering rate of each customer in each call period is determined.
[0036] S104, for each call period, the first call answering rate of the customer in the call period and the second call answering rate of the customer in the call period are fused to determine a target call answering rate of the customer in the call period.
[0037] In this embodiment, the call data comprises a call period and a call answering result; wherein the call period can be a time period of calling a customer. The call answering result can include but is not limited to answering and not answering, etc. It should be noted that the time length of each call period is the same, and the specific time length can be set by the technical personnel according to actual needs or practical experience, and the present application does not limit this.
[0038] The attribute data of the customer can include but is not limited to the age, occupation and asset level of the customer, etc.; it should be noted that the attribute data of the customer is the data authorized by the customer when the customer independently carries out the financial product recommendation business; the financial product corresponding to the call is the financial product covered in the financial product recommendation business independently carried out by the customer.
[0039] The first call answering rate is the call answering rate determined according to the call data of each customer; the second call answering rate is the call answering rate determined according to the call data and attribute data of each customer; the target call answering rate is the call answering rate obtained by fusing the first call answering rate and the second call answering rate. Wherein, the call answering rate can be the probability of the customer answering the call.
[0040] Specifically, call data of at least one customer is acquired, and attribute data of each of the customers is acquired; the call data includes call time periods and call answering results; a certain algorithm is used to determine a first call answering rate of each customer in each of the call time periods according to the call data of each of the customers; a certain algorithm is used to determine a second call answering rate of each customer in each of the call time periods according to the call data and the attribute data of each of the customers; and a certain algorithm is used to fuse the first call answering rate of the customer in the call time period and the second call answering rate of the customer in the call time period to determine a target call answering rate of the customer in the call time period.
[0041] Optionally, the determining of the first call answering rate of each customer in each of the call time periods according to the call data of each of the customers includes: for each customer, determining a third call answering rate of the customer in each of the call time periods according to the call data of the customer; for each of the remaining customers, determining a similarity between the customer and the remaining customer according to the third call answering rate of the customer in each of the call time periods and the third call answering rate of the remaining customer in each of the call time periods; the remaining customer is a customer other than the customer; and determining the first call answering rate of the customer in each of the call time periods according to the similarity between the customer and each of the remaining customers and the third call answering rate of each of the remaining customers.
[0042] Specifically, for each customer, the call answering results of the customer are classified according to call time periods; for each call time period, a third call answering rate of the customer in the call time period is determined according to the call answering results corresponding to the call time period; and exemplarily, a first quantity in the call time period can be counted; the first quantity is a total number of call answering results; a second quantity in the call time period can be counted; the second quantity is a number of call answering results that are answered; a ratio between the second quantity and the first quantity is calculated; and the calculated ratio is determined as the third call answering rate.
[0043] For each of the remaining customers, a similarity between the customer and the remaining customer is determined according to the third call answering rate of the customer in each of the call time periods and the third call answering rate of the remaining customer in each of the call time periods; and exemplarily, the similarity between the customer and the remaining customer can be calculated by the following formula:
[0044] ;
[0045] wherein, similarity between the customer and the remaining customer ; similarity between the customer in the call time period a third call answer rate of the customer; representing the rest of the customers in the call period a third call answer rate of the customer; representing the total number of call periods.
[0046] According to the similarity between the customer and each of the rest of the customers, and the third call answer rate of each of the rest of the customers, the first call answer rate of the customer in each of the call periods is determined; for example, the first call answer rate can be determined by the following formula:
[0047]
[0048] wherein, representing the customer in the call period a first call answer rate of the customer; representing the rest of the customers in the call period a third call answer rate of the customer; representing the total number of the rest of the customers k.
[0049] It can be understood that by using the above technical solution, the similarity of the call answer situation between each two customers can be calculated through the third call answer rate between the two customers; by multiplying the similarity and the third call answer rate, a higher weight is given to the third call answer rate of the rest of the customers who are similar to the customer in the call answer situation, and a lower weight is given to the third call answer rate of the rest of the customers who are not similar to the customer in the call answer situation, so as to comprehensively determine the first call answer rate, thereby improving the accuracy of the first call answer rate.
[0050] Optionally, the method further comprises: selecting a call period with the maximum target call answer rate of the customer as a target period; and pushing the target period to a call system, so that the call system calls the customer in the target period.
[0051] wherein the call system can be used to call the customer. It can be understood that by using the above technical solution, the call system can call the customer in the period when the customer is most likely to answer the call, thereby avoiding calling the customer in the period when the customer is not likely to answer the call, saving call resources, and improving call efficiency.
[0052] The technical scheme of the embodiment of the present application obtains the call data of at least one customer and the attribute data of each customer, wherein the call data comprises a call period and a call receiving result; determines a first call receiving rate of each customer in each call period according to the call data of each customer; determines a second call receiving rate of each customer in each call period according to the call data and the attribute data of each customer; and fuses the first call receiving rate of the customer in the call period and the second call receiving rate of the customer in the call period to determine a target call receiving rate of the customer in the call period for each call period. The present application realizes the prediction of the probability of the customer receiving a call in each call period.
[0053] Embodiment two
[0054] Figure 2 The flowchart of the call receiving probability determination method provided by the embodiment two of the present application, the embodiment of the present application optimizes and improves the determination operation of the second call receiving rate on the basis of the technical scheme of the above-mentioned embodiment.
[0055] Further, the determination of the second call receiving rate of each customer in each call period according to the call data and the attribute data of each customer is refined as the following: dividing each customer according to the attribute data of each customer to obtain at least one customer group; and determining the second call receiving rate of each customer in each call period according to the call data of each customer in the customer group to which the customer belongs, so as to perfect the determination operation of the first detection result and the determination operation of the second detection result.
[0056] It should be noted that the parts not described in detail in the embodiment of the present application can be referred to the description of the above-mentioned embodiment.
[0057] Referring to Figure 2 The call receiving probability determination method shown in the figure comprises:
[0058] S201, obtaining the call data of at least one customer and the attribute data of each customer; the call data comprises a call period and a call receiving result.
[0059] S202, determining a first call receiving rate of each customer in each call period according to the call data of each customer.
[0060] S203, dividing each customer according to the attribute data of each customer to obtain at least one customer group.
[0061] S204, determining a second call receiving rate of each customer in each call period according to the call data of each customer in the customer group to which the customer belongs.
[0062] S205, for each call period, fusing the first call answering rate of the customer in the call period and the second call answering rate of the customer in the call period to determine the target call answering rate of the customer in the call period.
[0063] In this embodiment, at least one customer can be included in the customer group. Specifically, a machine learning clustering algorithm or a business rule-based classification method can be used to classify each customer according to the attribute data of each customer, and at least one customer group is obtained. Specifically, for each customer, the second call answering rate of the customer in each call period is determined according to the call data of each customer in the customer group to which the customer belongs.
[0064] Optionally, the second call answering rate of the customer in each call period is determined according to the call data of each customer in the customer group to which the customer belongs, comprising: for each call period, determining the auxiliary call answering probability of the auxiliary customer group in the call period according to the call answering results of each customer in the auxiliary customer group in the call period; the auxiliary customer group is the customer group to which the customer belongs; the auxiliary call answering probability of the auxiliary customer group in the call period is determined as the second call answering rate of the customer in the call period in the auxiliary customer group.
[0065] Specifically, for each call period, the total number of call answering results of each customer in the auxiliary customer group in the call period is counted, and the number of call answering results that are answered in the call answering results of each customer in the auxiliary customer group in the call period is counted; the ratio between the number of call answering results that are answered and the total number of call answering results is determined as the auxiliary call answering probability; the auxiliary call answering probability of the auxiliary customer group in the call period is determined as the second call answering rate of the customer in the call period in the auxiliary customer group.
[0066] It can be understood that by using the above technical solution, the customers with similar attribute data can be divided into the same group by dividing the customers, and the second call answering rate of the customer is determined by comprehensively determining the call answering results of each customer in the auxiliary customer group with similar attributes, thereby improving the accuracy of the second call answering rate.
[0067] Optionally, the first call answering rate of the customer in the call period and the second call answering rate of the customer in the call period are fused to determine the target call answering rate of the customer in the call period, comprising: standardizing the first call answering rate of the customer in the call period to obtain a first standard answering rate; standardizing the second call answering rate of the customer in the call period to obtain a second standard answering rate; fusing the first standard answering rate and the second standard answering rate to obtain the target call answering rate of the customer in the call period.
[0068] Specifically, a maximum value of the first call answering rate of the customer in each call period is determined; a ratio between the first call answering rate of the customer in the call period and the maximum value of the first call answering rate of the customer is determined as the first standard answering rate; a maximum value of the second call answering rate of the customer in each call period is determined; and a ratio between the second call answering rate of the customer in the call period and the maximum value of the second call answering rate of the customer is determined as the second standard answering rate.
[0069] For example, the target call answering rate is determined by using the following formula:
[0070] ;
[0071] Wherein, represents the target call answering rate of the customer in the call period; represents the first standard answering rate of the customer in the call period; represents the second standard answering rate of the customer in the call period.
[0072] It can be understood that, by using the above technical solution, the first call answering rate and the second call answering rate are standardized, and the standardized results are fused, so as to unify the dimension, reduce the numerical difference between the first call answering rate and the second call answering rate, and improve the accuracy of the target call answering rate.
[0073] According to the attribute data of each customer, the customers are divided to obtain at least one customer group; for each customer, the second call answering rate of the customer in each call period is determined according to the call data of each customer in the customer group to which the customer belongs, the customers can be grouped according to the attribute data, and the second call answering rate of the customer is comprehensively determined according to the call data of each customer in the group, so as to improve the accuracy of the second call answering rate.
[0074] Embodiment three
[0075] Figure 3 A structural schematic diagram of a call answering probability determination device provided by the embodiment three of the application. The embodiment of the application can be applied to the case of determining the probability of answering a call of a customer. The device can execute the call answering probability determination method. The call answering probability determination device can be realized in the form of hardware and / or software. The device can be configured in an electronic device.
[0076] Referring to Figure 3 The call receiving probability determination device shown comprises an acquisition module 301, a first probability determination module 302, a second probability determination module 303 and a target probability determination module 304, wherein,
[0077] The acquisition module 301 is configured to acquire call data of at least one customer and attribute data of each of the customers, wherein the call data comprises a call period and a call receiving result.
[0078] The first probability determination module 302 is configured to determine a first call receiving rate of each of the customers in each of the call periods according to the call data of each of the customers.
[0079] The second probability determination module 303 is configured to determine a second call receiving rate of each of the customers in each of the call periods according to the call data and the attribute data of each of the customers.
[0080] The target probability determination module 304 is configured to fuse the first call receiving rate of the customer in the call period and the second call receiving rate of the customer in the call period to determine a target call receiving rate of the customer in the call period for each of the call periods.
[0081] The technical scheme of the embodiment of the application comprises the following steps: acquiring call data of at least one customer and attribute data of each of the customers by the acquisition module, wherein the call data comprises a call period and a call receiving result; determining a first call receiving rate of each of the customers in each of the call periods according to the call data of each of the customers by the first probability determination module; determining a second call receiving rate of each of the customers in each of the call periods according to the call data and the attribute data of each of the customers by the second probability determination module; and fusing the first call receiving rate of the customer in the call period and the second call receiving rate of the customer in the call period to determine a target call receiving rate of the customer in the call period for each of the call periods by the target probability determination module. The application realizes the prediction of the probability of the customer receiving a call in each of the call periods.
[0082] Optionally, the first probability determination module 302 is specifically configured to:
[0083] determine a third call receiving rate of each of the customers in each of the call periods according to the call data of the customer;
[0084] determine a similarity between the customer and each of the remaining customers according to the third call receiving rate of the customer in each of the call periods and the third call receiving rate of the remaining customer in each of the call periods, wherein the remaining customers are the customers other than the customer.
[0085] The first call answering rate of the customer in each of the call time periods is determined according to the similarity between the customer and each of the remaining customers and the third call answering rate of each of the remaining customers.
[0086] Optionally, the second probability determining module comprises:
[0087] The dividing unit is configured to divide the customers according to the attribute data of the customers, and obtain at least one customer group.
[0088] The determining unit is configured to determine, for each customer, the second call answering rate of the customer in each of the call time periods according to the call data of the customers in the customer group to which the customer belongs.
[0089] Optionally, the determining unit is specifically configured to:
[0090] For each call time period, the auxiliary call answering probability of the auxiliary customer group in the call time period is determined according to the call answering results of the customers in the auxiliary customer group in the call time period, and the auxiliary customer group is the customer group to which the customer belongs.
[0091] The auxiliary call answering probability of the auxiliary customer group in the call time period is determined as the second call answering rate of the customer in the call time period.
[0092] Optionally, the target probability determining module 304 comprises:
[0093] The first call answering rate of the customer in the call time period is standardized to obtain a first standard answering rate.
[0094] The second call answering rate of the customer in the call time period is standardized to obtain a second standard answering rate.
[0095] The first standard answering rate and the second standard answering rate are fused to obtain the target call answering rate of the customer in the call time period.
[0096] Optionally, the apparatus further comprises:
[0097] The selecting module is configured to select the call time period with the maximum target call answering rate of the customer as a target time period.
[0098] The sending module is configured to push the target time period to a call system, so that the call system calls the customer in the target time period.
[0099] The call answering probability determining apparatus provided in the embodiments of the present application can execute the call answering probability determining method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of executing the call answering probability determining method.
[0100] Example 4
[0101] Figure 4 A schematic diagram of a call answer probability determination device 410, which can be used to implement embodiments of the present invention, is shown. The call answer probability determination device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The call answer probability determination device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0102] like Figure 4 As shown, the call answering probability determination device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the call answering probability determination device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.
[0103] Multiple components in the call answer probability determination device 410 are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless transceiver, etc. The communication unit 419 allows the call answer probability determination device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0104] The processor 411 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, and the like. The processor 411 performs various methods and processes described above, such as the call answer probability determination method.
[0105] In some embodiments, the call answer probability determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed onto the call answer probability determination device 410 via the ROM 412 and / or the communication unit 419. When the computer program is loaded onto the RAM 413 and executed by the processor 411, one or more steps of the call answer probability determination method described above can be performed. Alternatively, in other embodiments, the processor 411 can be configured to perform the call answer probability determination method by any other suitable means, such as by means of firmware.
[0106] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
[0107] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.
[0108] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0109] To provide for interaction with a user, the systems and techniques described here can be implemented on a call answer probability determination device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the call answer probability determination device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
[0110] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.
[0111] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS (Virtual Private Server) service.
[0112] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.
[0113] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. A method for determining the probability of call answering, characterized in that, include: The system acquires call data from at least one customer, as well as attribute data for each customer; the call data includes call duration and call answering results. Based on the call data of each customer, determine the first call answer rate for each customer during each call period; Based on the call data and attribute data of each customer, determine the second call answer rate of each customer in each of the call periods; For each call period, the customer's first call answer rate and second call answer rate for that call period are combined to determine the customer's target call answer rate for that call period.
2. The method according to claim 1, characterized in that, The step of determining the first call answer rate for each customer during each call period based on the call data of each customer includes: For each customer, the third call answer rate for that customer in each of the aforementioned call periods is determined based on that customer's call data; For each remaining customer, the similarity between the customer and the remaining customers is determined based on the customer's third call answer rate during each of the aforementioned call periods and the remaining customers' third call answer rates during each of the aforementioned call periods; the remaining customers are customers other than the customer in question. The first call answering rate of the customer in each of the call periods is determined based on the similarity between the customer and each of the other customers, and the third call answering rate of each of the other customers.
3. The method according to claim 1, characterized in that, The step of determining the second call answer rate for each customer during each call period based on the call data and attribute data of each customer includes: Based on the attribute data of each customer, the customers are divided into at least one customer group; For each customer, the second call answer rate for that customer during each of the aforementioned call periods is determined based on the call data of each customer in the customer group to which that customer belongs.
4. The method according to claim 3, characterized in that, The step of determining the second call answer rate of the customer during each call period based on the call data of each customer in the customer group to which the customer belongs includes: For each call period, the auxiliary call answering probability of the auxiliary customer group is determined based on the call answering results of each customer in the auxiliary customer group during that call period; the auxiliary customer group is the customer group to which the customer belongs. The auxiliary call answering probability of the auxiliary customer group during the call period is determined as the second call answering rate of the customer in the auxiliary customer group during the call period.
5. The method according to claim 1, characterized in that, The process of fusing the customer's first call answer rate and second call answer rate during the call period to determine the customer's target call answer rate during the call period includes: The first call answer rate of this customer during the call period is standardized to obtain the first standard answer rate; The second call answer rate of this customer during the call period is standardized to obtain the second standard answer rate; The first standard call answer rate and the second standard call answer rate are fused together to obtain the target call answer rate for the customer during the call period.
6. The method according to claim 1, characterized in that, The method further includes: Select the time period with the highest target call answer rate for this customer as the target time period; The target time period is pushed to the call system so that the call system can call the customer during the target time period.
7. A device for determining the probability of call answering, characterized in that, The device includes: The acquisition module is used to acquire call data of at least one customer, and to acquire attribute data of each customer; the call data includes call time period and call answering result; The first probability determination module is used to determine the first call answer rate of each customer in each of the call periods based on the call data of each customer. The second probability determination module is used to determine the second call answer rate of each customer in each of the call periods based on the call data and attribute data of each customer. The target probability determination module is used to fuse the customer's first call answer rate and the customer's second call answer rate for each call period to determine the customer's target call answer rate for that call period.
8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a method for determining the probability of call answering according to any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the call answer probability as described in any one of claims 1-6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for determining the call answer probability as described in any one of claims 1-6.