WO2019144516A1 - 坐席分配方法、电子装置及计算机可读存储介质 - Google Patents

坐席分配方法、电子装置及计算机可读存储介质 Download PDF

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WO2019144516A1
WO2019144516A1 PCT/CN2018/083070 CN2018083070W WO2019144516A1 WO 2019144516 A1 WO2019144516 A1 WO 2019144516A1 CN 2018083070 W CN2018083070 W CN 2018083070W WO 2019144516 A1 WO2019144516 A1 WO 2019144516A1
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user
score
credit
information data
users
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English (en)
French (fr)
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李永平
高凌云
牛华
李长缤
凌永辉
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/01—Customer relationship services
    • G06Q30/015—Providing customer assistance, e.g. assisting a customer within a business location or via helpdesk
    • G06Q30/016—After-sales
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations

Definitions

  • the present application relates to the field of personnel distribution, and in particular, to an agent allocation method, an electronic device, and a computer readable storage medium.
  • Agent service is an important way for the financial industry to provide services to customers through the call center system.
  • Agent service means that the agent provides the corresponding service process through the support system of the call center.
  • the traditional agent allocation methods generally include: 1) Priority Assigned to the idle agent, that is, when the incoming call request is received, the request is preferentially assigned to the idle agent without the incoming call task; 2) randomly assigned, if there are multiple incoming calls and multiple idle seats, the random call is randomly selected. The incoming request is randomly assigned to the idle agent. Both of the distribution methods are different in the matching of the incoming call and the agent's service, which results in the agent not being able to provide the business service to the customer, resulting in a decline in service quality and inefficient business processing.
  • the purpose of the present application is to provide a method for distributing a seat, an electronic device, and a computer readable storage medium, thereby further overcoming the problems existing in the prior art to some extent.
  • the present application discloses a method for assigning an agent, comprising the following steps:
  • Step 01 collecting user information data
  • Step 02 Perform class classification on the user according to the information data to form a class classification identifier, where the information data includes asset credit data and personal information data of the user;
  • Step 03 Group the users according to the level classification identifier, and users belonging to the same classification identifier are divided into the same group;
  • Step 04 Assign users in each group to the corresponding agents according to a preset allocation policy.
  • the present application also discloses an electronic device including a memory and a processor for storing an agent distribution system executed by a processor, the agent distribution system comprising:
  • a user information collection module configured to collect asset credit data and personal information data of the user
  • a user level classification module configured to classify users according to asset credit data and personal information data of the user
  • a user grouping module configured to group users with unified classification identifiers according to a classification identifier of the user
  • An agent allocation module is configured to assign users in the same group to agents with matching levels according to a preset allocation policy.
  • the present application also discloses a computer readable storage medium having an agent distribution system stored therein, the agent distribution system being executable by at least one processor to implement the following steps:
  • Step 01 collecting user information data
  • Step 02 Perform class classification on the user according to the information data to form a class classification identifier, where the information data includes asset credit data and personal information data of the user;
  • Step 03 Group the users according to the level classification identifier, and users belonging to the same classification identifier are divided into the same group;
  • Step 04 Assign users in each group to the corresponding agents according to a preset allocation policy.
  • FIG. 1 is a flow chart showing an embodiment of an agent allocation method of the present application.
  • FIG. 2 is a flow chart showing still another embodiment of the agent allocation method of the present application.
  • FIG. 3 is a schematic diagram showing the program modules of an embodiment of the agent distribution system of the present application.
  • FIG. 4 is a schematic diagram of a program module of still another embodiment of the agent distribution system of the present application.
  • FIG. 5 is a schematic diagram showing the hardware architecture of an embodiment of an electronic device of the present application.
  • FIG 1-2 shows an agent allocation method, which specifically includes the following steps:
  • Step 01 Collect user information data.
  • Step 02 Perform class classification on the user according to the information data to form a class classification identifier, where the information data includes asset credit data and personal information data of the user.
  • the asset credit data includes the user's annual income, and the personal information data includes age, occupation, and education.
  • the collection of specific data can be collected by querying user data already registered in the system, or collecting data by a third-party inquiry institution.
  • Step 22-1 Perform data evaluation on the user according to the annual income information, age information, occupation information, and academic information input by the user, and form a user's annual income score x, age score y, occupation score z, and academic score w .
  • the above scores can be divided into 5 files according to 1, 2, 3, 4, 5 points.
  • the annual income score the annual income score of the user whose annual income is less than 100,000 is recorded as 1 point, ten 2 points between 10,000 and 200,000, 3 points for 200,000 to 300,000, 4 points for 300,000 to 500,000, and 5 points for 500,000 or more.
  • the occupational scores belonging to enterprises or civil servants are 5 points, 4 points for foreign companies and private enterprises, 3 points for self-employed individuals, and 2 points for freelance occupation.
  • the division of age points and academic scores can also be based on actual conditions. User statistics are divided. ⁇ ⁇ ⁇ ⁇ ⁇
  • Step 02-2 Calculate the user's credit score based on the user's annual income score x, age score y, occupation score z, and academic score w.
  • the user's annual income score, age score, occupation score, and academic score are formed according to the user's asset credit data and personal information data, and the annual income score, age score, occupation score, and academic score are multiplied
  • the weights, ⁇ , ⁇ , ⁇ , and ⁇ are adjusted according to the needs of each department.
  • Step 02-3 classify the user according to the credit score of the user.
  • the first threshold is preferably 80
  • the second threshold is preferably 60
  • B is For a class customer, if the user's credit score is lower than 60, it is a class C client.
  • the size of the first threshold and the second threshold may also be selected according to the statistical distribution of the actual user's credit score.
  • Step 03 The users are grouped according to the level classification identifier, and users belonging to the same category identifier are classified into the same group.
  • the products marked as Class A customers are classified into Class A groups, and those marked as Class B customers are classified into Class B groups, and those marked as Class C customers are classified into Class C groups.
  • the users in the same group are allocated to the corresponding agents through separate allocation channels, and the users of the same level are grouped and distributed in a centralized manner, thereby avoiding the cross-distribution of users of different levels, so that the high-level users are assigned to the lower-level seats. And can not get a higher quality of service.
  • Step 04 Assign users in each group to the corresponding agents according to a preset allocation policy.
  • Priority is given to assigning higher-level groups to higher-level agents, including:
  • step 04-1 the agent of the corresponding level is selected from the agent database according to the level of the group to be allocated, and the agent in the agent database has a corresponding level identifier, and the level identifier is obtained according to the service history data of each agent.
  • the agent ID with the higher level is A
  • the agent with the middle level is B
  • the agent with the lower level is C. If the group to be assigned is A, the agent screening sub-module automatically filters the agent at the A level. .
  • step 04-2 the user to be assigned is assigned to the selected agent, and is preferentially assigned to the idle agent of the level agent.
  • the specific seat class classification can be classified by the agent's comprehensive business score.
  • the service comprehensive score is greater than the third threshold for the class A agent, and the business comprehensive score is between the third threshold and the fourth threshold for the class B agent. If the score is lower than the fourth threshold, it is a class C agent.
  • the third threshold is preferably 85, and the fourth threshold is preferably 75. If the agent's business comprehensive score is greater than 85, the agent is a class A agent. If the agent's overall business score is between 75 and 85, the agent is a Class B agent. If the agent's business score is less than 75, the agent is a Class C agent.
  • the third threshold, The four thresholds may also be based on historical statistics of the business comprehensive scores of all agents, wherein the calculation formula of the agent's business comprehensive score is:
  • f(x, y, z,%) is the agent business skill value function
  • N is the total number of seats
  • x i , y i , z i represent the agent business volume, user rating, and response time, respectively;
  • ⁇ x , ⁇ y , ⁇ z represent the agent traffic processing dimension, the user score processing dimension, and the response time dimension respectively;
  • k x , k y , k z respectively represent the weight coefficients of different evaluation indicators, and are usually selected according to the needs of various departments.
  • an agent distribution system which specifically includes:
  • the user information collection module 201 is configured to collect asset credit data and personal information data of the user.
  • the asset credit data includes the user's annual income
  • the personal information data includes age, occupation, and education.
  • the user level classification module 202 is configured to classify users according to the user's asset credit data and personal information data.
  • the user level classification includes a user data evaluation sub-module 2021, a user credit score calculation sub-module 2022, and a user level classification sub-module 2023.
  • the user data evaluation sub-module 2021 is configured to perform data evaluation on the user according to annual income information, age information, occupation information, and academic information input by the user, and form an annual income score x, an age score y, and a professional score of the user. And the academic score w.
  • the user credit score calculation sub-module 2022 is configured to calculate a user's personal credit score according to the user's annual income score, occupation score, age score, and academic score.
  • the user's credit score is calculated based on the user's annual income score x, age score y, occupation score z, and academic score w.
  • the user's annual income score, age score, occupation score, and academic score are formed according to the user's asset credit data and personal information data, and the annual income score, age score, occupation score, and academic score are multiplied
  • the user's credit score is obtained by adding the respective weights and then adding them.
  • ⁇ ⁇ *x+ ⁇ *y+ ⁇ *z+ ⁇ *w
  • ⁇ , ⁇ , ⁇ are the annual income score x, the age score y, the occupation score z, and the academic score w
  • the weights, ⁇ , ⁇ , ⁇ , and ⁇ are adjusted according to the needs of each department.
  • the user level classification sub-module 2023 is configured to classify the user's rating according to the user's personal credit score. Calculating the user's credit score according to the user's asset credit data and personal information data, and class A customers whose credit score is greater than the first threshold, and the credit score between the first threshold and the second threshold is a class B customer, credit score Less than the second threshold is a class C client, and the A, B, and C form a classification identifier for each user.
  • the first threshold is preferably 80, and the second threshold is preferably 60.
  • the actual first threshold and the second threshold may also be selected according to the statistical distribution of the actual user's credit score.
  • the user grouping module 203 is configured to group users with unified classification identifiers according to the classification identifier of the user.
  • the products marked as Class A customers are classified into Class A groups, and those marked as Class B customers are classified into Class B groups, and those marked as Class C customers are classified into Class C groups.
  • the users in the same group are allocated to the corresponding agents through separate allocation channels, and the users of the same level are grouped and distributed in a centralized manner, thereby avoiding the cross-distribution of users of different levels, so that the high-level users are assigned to the lower-level seats. And can not get a higher quality of service.
  • the agent allocation module 204 is configured to allocate users in the same group to the seats of the level matching according to the preset allocation policy.
  • the agent allocation module includes: a seat screening sub-module 2041 and an agent allocation sub-module 2042.
  • the agent screening sub-module 2041 is configured to filter agents that match their levels according to the level of users in the same group.
  • the agent of the corresponding level is selected from the agent database according to the level of the group to be allocated, and the agent in the agent database has a corresponding level identifier, and the level identifier is obtained according to the service history data of each agent, including a higher level.
  • the agent ID is A
  • the agent ID is the B level
  • the lower agent ID is C. If the group to be assigned is A, the agent screening sub-module automatically filters the A-level agent.
  • the agent allocation sub-module 2042 is configured to assign the same group of users to agents of the corresponding level. Assign the users to be assigned to the filtered agents and prioritize them to the free agents of the level agents.
  • the embodiment provides an electronic device. It is a schematic diagram of the hardware architecture of an embodiment of the electronic device of the present application.
  • the electronic device 2 is an apparatus capable of automatically performing numerical calculation and/or information processing in accordance with an instruction set or stored in advance.
  • it can be a smartphone, a tablet, a laptop, a desktop computer, a rack server, a blade server, a tower server, or a rack server (including a stand-alone server, or a server cluster composed of multiple servers).
  • the electronic device 2 includes at least, but not limited to, a memory 21, a processor 22, a network interface 23, and an agent distribution system 20 that are communicably coupled to one another via a system bus. among them:
  • the memory 21 includes at least one type of computer readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), Static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 21 may be an internal storage module of the electronic device 2, such as a hard disk or a memory of the electronic device 2.
  • the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk equipped on the electronic device 2, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • the memory 21 can also include both the internal storage module of the electronic device 2 and its external storage device.
  • the memory 21 is generally used to store an operating system installed in the electronic device 2 and various types of application software, such as program codes of the agent distribution system 20. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 22 is typically used to control the overall operation of the electronic device 2, such as performing control and processing associated with data interaction or communication with the electronic device 2.
  • the processor 22 is configured to run program code or process data stored in the memory 21, such as running the agent distribution system 20 and the like.
  • the network interface 23 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 2 and other electronic devices.
  • the network interface 23 is configured to connect the electronic device 2 to an external terminal through a network, establish a data transmission channel, a communication connection, and the like between the electronic device 2 and an external terminal.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.
  • FIG. 5 only shows the electronic device with components 20-24, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.
  • the agent distribution system 20 stored in the memory 21 may also be divided into one or more program modules, the one or more program modules being stored in the memory 21 and composed of one or more
  • the processor this embodiment is processor 22
  • FIG. 3 is a schematic diagram of a program module of the first embodiment of the agent distribution system 20.
  • the agent-based distribution system 20 can be divided into a user information collection module 201, a user level classification module 202, User grouping module 203, agent allocation module 204.
  • the program module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function. The specific functions of the program modules 201-204 are described in detail in the second embodiment, and details are not described herein again.
  • the embodiment provides a computer readable storage medium on which the agent distribution system 20 is stored, and the agent distribution system 20 is implemented by one or more processors to implement the above-described agent distribution method or electronic device. Operation.

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Abstract

本申请公开一种坐席分配方法、电子装置及计算机可读存储介质，包括如下步骤：步骤01、采集用户的信息数据；步骤02、根据所述信息数据对用户进行等级分类，形成等级分类标识，所述信息数据包括用户的资产信用数据和个人信息数据；步骤03、根据所述等级分类标识对所述用户进行分组，属于同一分类标识的用户分为同一组；步骤04、将每个分组中的用户按预设的分配策略分配至相应的坐席。本方法根据用户的个人数据智能分配坐席，提高了用户与坐席的匹配性，提高业务服务效率。

Description

坐席分配方法、电子装置及计算机可读存储介质
本申请申明享有2018年1月25日递交的申请号为201810076554.8、名称为“坐席分配方法、电子装置及计算机可读存储介质”的中国专利申请的优先权，该中国专利申请的整体内容以参考的方式结合在本申请中。
技术领域
本申请涉及人员分配领域，具体涉及一种坐席分配方法、电子装置及计算机可读存储介质。
背景技术
坐席服务是金融业通过呼叫中心系统为客户提供服务的一种重要方式，坐席服务是指坐席人员通过呼叫中心的支撑系统为客户提供相应的服务过程，传统的坐席分配方法一般有：1)优先分配至空闲坐席，即当收到呼入请求时，优先将该请求分配至没有呼入任务的空闲坐席；2)随机分配，若存在多个呼入请求以及多个空闲坐席时，随机抽取呼入请求并随机分配至空闲坐席，这两种分配方式都在呼入客户与坐席的业务匹配差异性，导致坐席不能很好的为客户提供业务服务，导致服务质量下降，业务办理效率低。
发明内容
本申请的目的在于提供一种坐席分配方法、电子装置以及计算机可读存储介质，进而在一定程度上克服现有技术中存在的问题。
本申请是通过下述技术方案来解决上述技术问题：
本申请公开了一种坐席分配方法，包括如下步骤：
步骤01、采集用户的信息数据；
步骤02、根据所述信息数据对用户进行等级分类，形成等级分类标识，所述信息数据包括用户的资产信用数据和个人信息数据；
步骤03、根据所述等级分类标识对所述用户进行分组，属于同一分类标识的用户分为同一组；
步骤04、将每个分组中的用户按预设的分配策略分配至相应的坐席。
本申请还公开了一种电子装置，包括存储器和处理器，所述存储器用于存储被处理器执行的坐席分配系统，所述坐席分配系统包括：
用户信息采集模块，用于采集用户的资产信用数据和个人信息数据；
用户等级分类模块，用于根据所述用户的资产信用数据和个人信息数据对用户进行等级分类；
用户分组模块，用于根据用户的分类标识对具有统一分类标识的用户进行分组；
坐席分配模块，用于根据预设的分配策略将同一组中的用户分配至级别匹配的坐席。
本申请还公开了一种计算机可读存储介质，所述计算机可读存储介质内存储有坐席分配系统，所述坐席分配系统可被至少一个处理器所执行，以实现以下步骤：
步骤01、采集用户的信息数据；
步骤02、根据所述信息数据对用户进行等级分类，形成等级分类标识，所述信息数据包括用户的资产信用数据和个人信息数据；
步骤03、根据所述等级分类标识对所述用户进行分组，属于同一分类标识的用户分为同一组；
步骤04、将每个分组中的用户按预设的分配策略分配至相应的坐席。
附图说明
图1示出了本申请坐席分配方法一实施例的流程图。
图2示出了本申请坐席分配方法又一实施例的流程图。
图3示出了本申请坐席分配系统一实施例的程序模块示意图。
图4示出了本申请坐席分配系统又一实施例的程序模块示意图。
图5示出了本申请电子装置一实施例的硬件架构示意图。
具体实施方式
实施例一
图1-2示出了一种坐席分配方法，具体包括如下步骤：
步骤01、采集用户的信息数据。
步骤02、根据所述信息数据对用户进行等级分类，形成等级分类标识，所述信息数据包括用户的资产信用数据和个人信息数据。
资产信用数据包括用户的年收入，个人信息数据包括年龄、职业、学历。具体数据的采集可通过查询已经登记在系统内的用户数据进行采集，或者通过第三方查询机构进行数据的采集。
具体包括：
步骤02-1、根据用户输入的年收入信息、年龄信息、职业信息以及学历信息对用户进行数据评估，形成用户的年收入分值x、年龄分值y、职业分值z以及学历分值w。
其中，上述分值均可按1、2、3、4、5分分为5档，具体来说，对于年收入分值，年收入小于十万的用户年收入分值记为1分，十万到二十万之间的为2分，二十万到三十万的为3分，三十万到五十万的为4分，五十万以上的为5分，对于职业分值，隶属于企事业或公务员的职业分值为5分，外企、私营企业的为4分，个体户的为3分，自由职业的为2分，至于年龄分值和学历分值的划分也可根据实际用户的统计数据进行划分。α β γ δ
步骤02-2、根据用户的年收入分值x、年龄分值y、职业分值z以及学 历分值w计算用户的信用评分∑。
根据用户的资产信用数据和个人信息数据形成用户的年收入分值、年龄分值、职业分值以及学历分值，将所述年收入分值、年龄分值、职业分值以及学历分值乘以各自的权重后再相加得到所述用户的信用评分∑。即∑＝α*x+β*y+γ*z+δ*w，其中α、β、γ、δ分别为年收入分值x、年龄分值y、职业分值z以及学历分值w的权重，α、β、γ、δ的取值根据各部门的需求进行调整。
步骤02-3、根据用户的信用评分对用户进行等级划分。
根据用户的资产信用数据和个人信息数据计算用户的信用评分，并按信用评分大于第一阈值的为A类客户，信用评分在第一阈值和第二阈值之间的为B类客户，信用评分小于第二阈值的为C类客户，且所述A、B、C形成每个用户的分类标识。在该实施例中，第一阈值优选为80，第二阈值优选为60，若用户的信用评分大于80，则为A类客户，若用户的信用评分介于60-80之间，则为B类客户，若用户的信用评分低于60，则为C类客户，在其它实施例中，第一阈值、第二阈值的大小也可根据实际用户的信用评分的统计分布进行选择。
步骤03、根据所述等级分类标识对所述用户进行分组，属于同一分类标识的用户分为同一组。
将标记为A类客户的分入A级组，标记为B类客户分入B级组，标记为C类客户分入C级组。同一组的用户集中通过独立的分配通道分配至相应的坐席，对同一等级的用户进行分组并集中分配，避免了不同级别的用户相互交叉分配导致高级别的用户被分配至等级较低的坐席，而不能获得较高质量的服务质量。
步骤04、将每个分组中的用户按预设的分配策略分配至相应的坐席。
优先将等级较高的分组分配至级别较高的坐席，具体包括：
步骤04-1、根据待分配的分组的级别从坐席数据库中筛选相应级别的坐席，坐席数据库中的坐席具有相应的级别标识，所述级别标识根据每个坐席的服务历史数据进行统计而获得，包括级别较高的坐席标识为A级，级别中等的坐席标识为B级，级别较低的坐席标识为C级，若待分配的分组为A级，则坐席筛选子模块自动筛选A级的坐席。
步骤04-2、将待分配的用户分配至经过筛选的坐席，并优先分配至该级别坐席的空闲坐席。
具体的坐席等级分类可通过坐席的业务综合评分进行分类，业务综合评分大于第三阈值的为A类坐席，业务综合评分介于第三阈值、第四阈值之间的为B类坐席，业务综合评分低于第四阈值的为C类坐席，在该实施例中，第三阈值优先为85，第四阈值优选为75，若坐席的业务综合评分大于85，则该坐席为A类坐席，若坐席的业务综合评分介于75和85之间，则该坐席为B类坐席，若坐席的业务综合评分低于75，则该坐席为C类坐席，在其它实施例中，第三阈值、第四阈值也可根据所有坐席的业务综合评分的历史统计数据而定，其中坐席的业务综合评分的计算公式为：
Figure PCTCN2018083070-appb-000001
其中，
Figure PCTCN2018083070-appb-000002
f(x，y，z，...)为坐席业务技能值函数；
N为坐席总人数；
x i，y i，z i分别代表坐席业务量、用户评分、响应时间；
Figure PCTCN2018083070-appb-000003
分别代表坐席业务量平均值、用户评分平均值、响应时间平均值；
Δ x，Δ y，Δ z分别代表坐席业务量处理量纲、用户评分处理量纲、响应时间量纲；
k x，k y，k z分别代表不同考核指标权重系数，通常根据各部门需求进行选择。
实施例二
参阅图3-4，本申请还公开了一种坐席分配系统，具体包括：
用户信息采集模块201，用于采集用户的资产信用数据和个人信息数据。资产信用数据包括用户的年收入，个人信息数据包括年龄、职业、学历。
用户等级分类模块202，用于根据所述用户的资产信用数据和个人信息数据对用户进行等级分类。
所述用户等级分类包括：用户数据评估子模块2021、用户信用评分计算子模块2022以及用户等级分类子模块2023。
所述用户数据评估子模块2021用于根据用户输入的年收入信息、年龄信息、职业信息以及学历信息对用户进行数据评估，形成用户的年收入分值x、年龄分值y、职业分值z以及学历分值w。
所述用户信用评分计算子模块2022用于根据所述用户的年收入分值、职业分值、年龄分值以及学历分值计算用户的个人信用评分。根据用户的年收入分值x、年龄分值y、职业分值z以及学历分值w计算用户的信用评分∑。根据用户的资产信用数据和个人信息数据形成用户的年收入分值、年龄分值、职业分值以及学历分值，将所述年收入分值、年龄分值、职业分值以及学历分值乘以各自的权重后再相加得到所述用户的信用评分∑。即∑＝α*x+β*y+γ*z+δ*w，其中α、β、γ、δ分别为年收入分值x、年龄分值y、职业分值z以及学历分值w的权重，α、β、γ、δ的取值根据各部门的需求进行调整。
所述用户等级分类子模块2023用于根据用户的个人信用评分对所述用 户的等级进行分类。根据用户的资产信用数据和个人信息数据计算用户的信用评分，并按信用评分大于第一阈值的为A类客户，信用评分在第一阈值和第二阈值之间的为B类客户，信用评分小于第二阈值的为C类客户，且所述A、B、C形成每个用户的分类标识。其中第一阈值优选为80，第二阈值优选为60，实际第一阈值、第二阈值的大小也可根据实际用户的信用评分的统计分布进行选择。
用户分组模块203，用于根据用户的分类标识对具有统一分类标识的用户进行分组。将标记为A类客户的分入A级组，标记为B类客户分入B级组，标记为C类客户分入C级组。同一组的用户集中通过独立的分配通道分配至相应的坐席，对同一等级的用户进行分组并集中分配，避免了不同级别的用户相互交叉分配导致高级别的用户被分配至等级较低的坐席，而不能获得较高质量的服务质量。
坐席分配模块204，用于根据预设的分配策略将同一组中的用户分配至级别匹配的坐席。所述坐席分配模块包括：坐席筛选子模块2041和坐席分配子模块2042。
所述坐席筛选子模块2041用于根据同一组中用户的等级筛选与其级别相匹配的坐席。根据待分配的分组的级别从坐席数据库中筛选相应级别的坐席，坐席数据库中的坐席具有相应的级别标识，所述级别标识根据每个坐席的服务历史数据进行统计而获得，包括级别较高的坐席标识为A级，级别中等的坐席标识为B级，级别较低的坐席标识为C级，若待分配的分组为A级，则坐席筛选子模块自动筛选A级的坐席。
所述坐席分配子模块2042用于将同一组用户分配至相应级别的坐席。将待分配的用户分配至经过筛选的坐席，并优先分配至该级别坐席的空闲坐席。
实施例三
参阅图5，本实施例提供一种电子装置。是本申请电子装置一实施例的硬件架构示意图。本实施例中，所述电子装置2是一种能够按照事先设定或者存储的指令，自动进行数值计算和/或信息处理的设备。例如，可以是智能手机、平板电脑、笔记本电脑、台式计算机、机架式服务器、刀片式服务器、塔式服务器或机柜式服务器(包括独立的服务器，或者多个服务器所组成的服务器集群)等。如图所示，所述电子装置2至少包括，但不限于，可通过系统总线相互通信连接存储器21、处理器22、网络接口23、以及坐席分配系统20。其中：
所述存储器21至少包括一种类型的计算机可读存储介质，所述可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘等。在一些实施例中，所述存储器21可以是所述电子装置2的内部存储模块，例如该电子装置2的硬盘或内存。在另一些实施例中，所述存储器21也可以是所述电子装置2的外部存储设备，例如该电子装置2上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。当然，所述存储器21还可以既包括所述电子装置2的内部存储模块也包括其外部存储设备。本实施例中，所述存储器21通常用于存储安装于所述电子装置2的操作系统和各类应用软件，例如所述坐席分配系统20的程序代码等。此外，所述存储器21还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器22在一些实施例中可以是中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器、或其他数据处理芯片。该处理 器22通常用于控制所述电子装置2的总体操作，例如执行与所述电子装置2进行数据交互或者通信相关的控制和处理等。本实施例中，所述处理器22用于运行所述存储器21中存储的程序代码或者处理数据，例如运行所述的坐席分配系统20等。
所述网络接口23可包括无线网络接口或有线网络接口，该网络接口23通常用于在所述电子装置2与其他电子装置之间建立通信连接。例如，所述网络接口23用于通过网络将所述电子装置2与外部终端相连，在所述电子装置2与外部终端之间的建立数据传输通道和通信连接等。所述网络可以是企业内部网(Intranet)、互联网(Internet)、全球移动通讯系统(Global System of Mobile communication，GSM)、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)、4G网络、5G网络、蓝牙(Bluetooth)、Wi-Fi等无线或有线网络。
需要指出的是，图5仅示出了具有部件20-24的电子装置，但是应理解的是，并不要求实施所有示出的部件，可以替代的实施更多或者更少的部件。
在本实施例中，存储于存储器21中的所述坐席分配系统20还可以被分割为一个或者多个程序模块，所述一个或者多个程序模块被存储于存储器21中，并由一个或多个处理器(本实施例为处理器22)所执行，以完成本申请。
例如，图3示出了所述坐席分配系统20第一实施例的程序模块示意图，该实施例中，所述基于坐席分配系统20可以被划分为用户信息采集模块201、用户等级分类模块202、用户分组模块203、坐席分配模块204。其中，本申请所称的程序模块是指能够完成特定功能的一系列计算机程序指令段。所述程序模块201-204的具体功能在实施例二中已有详细描述，在此不再赘述。
实施例四
本实施例提供一种计算机可读存储介质，该计算机可读存储介质上存储有所述坐席分配系统20，该坐席分配系统20被一个或多个处理器执行时实现上述坐席分配方法或电子装置的操作。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (15)

  1. 一种坐席分配方法，其特征在于，包括如下步骤：
    步骤01、采集用户的信息数据；
    步骤02、根据所述信息数据对用户进行等级分类，形成等级分类标识，所述信息数据包括用户的资产信用数据和个人信息数据；
    步骤03、根据所述等级分类标识对所述用户进行分组，属于同一分类标识的用户分为同一组；
    步骤04、将每个分组中的用户按预设的分配策略分配至相应的坐席。
  2. 根据权利要求1所述的方法，其特征在于，步骤02中的资产信用数据包括用户的年收入，个人信息数据包括年龄、职业、学历。
  3. 根据权利要求2所述的方法，其特征在于，步骤02包括：根据用户的资产信用数据和个人信息数据计算用户的信用评分，并按信用评分大于第一阈值的为A类客户，信用评分在第一阈值和第二阈值之间的为B类客户，信用评分小于第二阈值的为C类客户，且所述A、B、C形成每个用户的分类标识。
  4. 根据权利要求3所述的方法，其特征在于，步骤02包括：根据用户的资产信用数据和个人信息数据形成用户的年收入分值、年龄分值、职业分值以及学历分值，将所述年收入分值、年龄分值、职业分值以及学历分值乘以各自的权重后再相加得到所述用户的信用评分。
  5. 根据权利要求1所述的方法，其特征在于，步骤03包括：将标记为A类客户的分入A级组，标记为B类客户分入B级组，标记为C类客户分入C级组。
  6. 根据权利要求1所述的方法，其特征在于，步骤04包括：优先将等级较高的分组分配至级别较高的坐席。
  7. 一种电子装置，包括存储器和处理器，其特征在于，所述存储器用 于存储被处理器执行的坐席分配系统，所述坐席分配系统包括：
    用户信息采集模块，用于采集用户的资产信用数据和个人信息数据；
    用户等级分类模块，用于根据所述用户的资产信用数据和个人信息数据对用户进行等级分类；
    用户分组模块，用于根据用户的分类标识对具有统一分类标识的用户进行分组；
    坐席分配模块，用于根据预设的分配策略将同一组中的用户分配至级别匹配的坐席。
  8. 根据权利要求7所述的电子装置，其特征在于，所述用户等级分类模块包括：用户数据评估子模块、用户信用评分计算子模块以及用户等级分类子模块，所述用户数据评估子模块用于根据用户的资产信用数据和个人信息数据评估用户的年收入分值、职业分值、年龄分值以及学历分值；所述用户信用评分计算子模块用于根据所述用户的年收入分值、职业分值、年龄分值以及学历分值计算用户的个人信用评分，所述用户等级分类子模块用于根据用户的个人信用评分对所述用户的等级进行分类。
  9. 根据权利要求7所述的电子装置，其特征在于，所述坐席分配模块包括：坐席筛选子模块和坐席分配子模块，所述坐席筛选子模块用于根据同一组中用户的等级筛选与其级别相匹配的坐席；所述坐席分配子模块用于将同一组用户分配至相应级别的坐席。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质内存储有坐席分配系统，所述坐席分配系统可被至少一个处理器所执行，以实现以下步骤：
    步骤01、采集用户的信息数据；
    步骤02、根据所述信息数据对用户进行等级分类，形成等级分类标识，所述信息数据包括用户的资产信用数据和个人信息数据；
    步骤03、根据所述等级分类标识对所述用户进行分组，属于同一分类标 识的用户分为同一组；
    步骤04、将每个分组中的用户按预设的分配策略分配至相应的坐席。
  11. 根据权利要求10所述的计算机可读存储介质，其特征在于，步骤02中的资产信用数据包括用户的年收入，个人信息数据包括年龄、职业、学历。
  12. 根据权利要求11所述的计算机可读存储介质，其特征在于，步骤02包括：根据用户的资产信用数据和个人信息数据计算用户的信用评分，并按信用评分大于第一阈值的为A类客户，信用评分在第一阈值和第二阈值之间的为B类客户，信用评分小于第二阈值的为C类客户，且所述A、B、C形成每个用户的分类标识。
  13. 根据权利要求12所述的计算机可读存储介质，其特征在于，步骤02包括：根据用户的资产信用数据和个人信息数据形成用户的年收入分值、年龄分值、职业分值以及学历分值，将所述年收入分值、年龄分值、职业分值以及学历分值乘以各自的权重后再相加得到所述用户的信用评分。
  14. 根据权利要求10所述的计算机可读存储介质，其特征在于，步骤03包括：将标记为A类客户的分入A级组，标记为B类客户分入B级组，标记为C类客户分入C级组。
  15. 根据权利要求10所述的计算机可读存储介质，其特征在于，步骤04包括：优先将等级较高的分组分配至级别较高的坐席。
PCT/CN2018/083070 2018-01-25 2018-04-13 坐席分配方法、电子装置及计算机可读存储介质 Ceased WO2019144516A1 (zh)

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