CN110807171A - Method and device for analyzing adequacy of seat personnel in business based on weight division - Google Patents
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Abstract
The application provides a method for analyzing adequacy of an agent in service based on weight division, which comprises the following steps: (1) data acquisition, namely extracting data related to the service of the seat personnel from a database; (2) data processing, including data cleaning, data integration, data transformation and data specification of the acquired data; (3) building a model, namely layering factors related to the strong business of the seat personnel according to the levels, and obtaining the weight of the measured factors in each layer by using an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model; (4) and outputting a model result, and calculating according to the model to obtain the score of the specific business of the specific seat personnel. On the other hand, the invention also provides an agent adept business analysis device based on weight division. According to the method and the system, a series of processes from data acquisition and hierarchical analysis model construction to result output are realized, and comprehensive and accurate analysis on the adequacy business of the seat personnel is realized.
Description
Technical Field
The invention relates to data analysis and data mining, in particular to a method for analyzing seating personnel adept service based on weight division.
Background
With the rapid development of mobile communication technology and the reduction of internet self-fees, more and more organizations set up remote centers or call centers to provide consulting services to customers through internet audio or video access. The remote center or call center typically provides a plurality of entries for the user to transfer manual services, corresponding to respective agent groups, each of which has a plurality of agent personnel. The operation of any remote center or call center requires the improvement of service speed and service quality, and the improvement of the service quality and service level of the remote center or call center is expected to be realized on the premise of reducing the operation cost.
In order to reduce the operation cost and ensure the customer service quality in the prior art, a management-level approach is generally adopted, for example, in a chinese patent with publication number CN110248031A, the service quality and the service level of a customer service system are ensured by predicting the number of calls of corresponding types and reasonably scheduling in combination with personal ability factors of seat personnel; and no specific operation method is given for the quantitative analysis of the adequacy of the services and the abilities of the personnel at the seat.
The publication number CN106531187A discloses a method for performing performance assessment on an agent in a call center, but only aiming at voice information of a customer service person who answers the call, there is a certain sidedness and the adequacy field of the agent cannot be comprehensively analyzed.
Disclosure of Invention
The purpose of the invention is as follows: the application aims to provide a method and a device for analyzing adequacy of an operator based on weight division, and the method and the device can be used for solving the problem that accurate quantitative analysis cannot be performed on the adequacy field of the operator in the prior art.
The technical scheme is as follows: in one aspect, the present invention provides a method for analyzing excellence of an agent in service based on weight division, including:
(1) data acquisition, namely extracting data related to the service of the seat personnel from a database to serve as a data base for subsequent analysis;
(2) data processing, including data cleaning, data integration, data transformation and data specification of the acquired data, and obtaining integrated data as input data of the model;
(3) building a model, namely layering factors related to the strong business of the seat personnel according to the levels, and obtaining the weight of the measured factors in each layer by using an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model;
(4) and outputting a model result, and calculating to obtain the score of the specific service of the specific seat personnel according to the weight of each factor in the model.
Further, in the step (1), the data related to the service of the seat personnel comprises basic attribute data, telephone traffic data, skill data, behavior data and assessment evaluation data of the seat personnel.
Further, in the step (2), the data cleansing includes: deleting repeated and irrelevant data in the collected data; deleting, filling or re-valuing missing data or abnormal data in the acquired data; deleting noise data in the collected data; carrying out standardization processing on format contents of the acquired data; the data integration comprises the steps of integrating the cleaned correlated distributed heterogeneous agent personnel data together by using a data integration method so as to access the integrated data in a transparent mode; the data transformation comprises the steps of converting text data into numerical data by constructing text mapping on the integrated data, and normalizing the numerical data so as to reflect the service capability of the seat personnel in a numerical form and prepare for subsequent modeling; the data specification can adopt a characteristic specification, a sample specification and a characteristic value specification.
Further, in the step (3), dividing the factors related to the excellence of the seat personnel in the business into a first layer factor and a second layer factor; the first layer factors include: personal characteristics, business capabilities, quality inspection results; the second layer factor is a sub-factor under the first layer factor.
Further, in step (4), the processed data is substituted into the hierarchical analysis model, and the data is subjected to corresponding weight calculation and added to obtain the score of the specific seat person under the business.
In another aspect, the present invention provides an apparatus for analyzing excellence of an agent in business based on weight classification, including:
the data acquisition module is used for extracting data related to the service of the seat personnel from the database and taking the data as a data base for subsequent analysis;
the data processing module is used for carrying out data cleaning, data integration, data transformation and data specification on the acquired data to obtain integrated data serving as input data of the model;
the model building module is used for layering factors related to the strong business of the seat staff according to the levels, and obtaining the weight of the measured factors in each layer by applying an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model;
and the model result output module is used for calculating the score of the specific service of the specific seat personnel according to the weight of each factor in the model.
Further, the data related to the services of the seat personnel comprise basic attribute data, telephone traffic data, skill data, behavior data and assessment and evaluation data of the seat personnel.
Further, the data cleansing includes: deleting repeated and irrelevant data in the collected data; deleting, filling or re-valuing missing data or abnormal data in the acquired data; deleting noise data in the collected data; carrying out standardization processing on format contents of the acquired data; the data integration comprises the steps of integrating the cleaned correlated distributed heterogeneous agent personnel data together by using a data integration method so as to access the integrated data in a transparent mode; the data transformation comprises the steps of converting text data into numerical data by constructing text mapping on the integrated data, and normalizing the numerical data so as to reflect the service capability of the seat personnel in a numerical form and prepare for subsequent modeling; the data specification can adopt a characteristic specification, a sample specification and a characteristic value specification.
Further, the model building module divides factors related to the excellence of the seat personnel in the business into a first layer factor and a second layer factor; the first layer factors include: personal characteristics, business capabilities, quality inspection results; the second layer factor is a sub-factor under the first layer factor.
Further, the model result output module substitutes the processed data into the hierarchical analysis model, and the data are subjected to corresponding weight calculation and added to obtain the score of the specific seat personnel under the business.
Has the advantages that: compared with the prior art, the method and the device provided by the invention have the advantages that the business capability data of the seat personnel is subjected to layered modeling according to the requirements of specific business projects and the specific weight of each capability, so that the quantitative evaluation on the business capability of the seat personnel is realized. The method of the invention realizes a series of processes from data acquisition and hierarchical analysis model construction to result output, realizes more comprehensive and accurate analysis on the adequacy business of the seat personnel, and is beneficial to improving the service quality of the seat personnel.
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Fig. 1 is a flow chart of a method for analyzing excellence of an agent in service according to the present invention.
Detailed Description
The invention is further described below with reference to the following figures and examples:
in one aspect, the present application provides a method for analyzing excellence of an agent in business based on weight division, where as shown in the figure, the method includes:
and S101, data acquisition, namely extracting data related to the service of the seat personnel from a database to be used as a data basis for subsequent analysis. In the embodiment, data related to the service ability of the seat staff can be acquired from the 95598 cloud database, and the data are usually stored in the forms of questionnaire investigation, skill evaluation, evaluation of a manager and the like. The data related to the services of the seat personnel comprise basic attribute data, telephone traffic data, skill data, behavior data and assessment and evaluation data of the seat personnel.
Wherein the basic attribute data includes: name, gender, age, native place, school calendar, professional marital status, age, post, etc.; the traffic data includes: telephone traffic type, telephone traffic quantity, telephone traffic time, etc.; the skill data includes: software tool type, tool operational familiarity, tool learning ability, etc. will be used; the behavior data includes: working behavior data (such as working processing mode, processing efficiency and the like), daily behavior data (such as amateur preference, preference degree and the like); the assessment evaluation data comprises: performance assessment data, business capability assessment data, evaluation data of a management layer, business familiarity evaluation data and the like.
And S102, data processing, including data cleaning, data integration, data transformation and data specification of the acquired data, to obtain integrated data as input data of the model.
Due to the fact that data collected by the cloud end is used for the agent to specialize in business analysis, the source is diversified, and besides structural data stored in the database, comment-type text data exist, data needs to be processed, and the main content includes data cleaning, data integration, data transformation and data protocols.
The data cleaning comprises the following steps: deleting repeated and irrelevant data in the collected data; deleting, filling or re-valuing missing data or abnormal data in the acquired data; deleting noise data in the collected data; and carrying out standardization processing on format content of the acquired data. Common methods include deletion method, substitution method, interpolation method, etc.
The data integration comprises the following steps: and integrating the cleaned correlated distributed heterogeneous agent personnel data together by using a data integration method so as to access the integrated data in a transparent manner. The specific integration method can adopt a mode integration method, a data replication method and a comprehensive integration method.
And the data transformation comprises the steps of converting the integrated data into numerical data by constructing text mapping, and normalizing the numerical data so as to reflect the service capability of the agent personnel in a numerical form and prepare for subsequent modeling.
The data specification can adopt a characteristic specification, a sample specification and a characteristic value specification. The data protocol can better play the model effect and improve the modeling quality.
And S103, constructing a model, namely layering the factors related to the excellence of the seat personnel in the business according to the levels, and obtaining the weight of the measured factors in each layer by using an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model.
In the embodiment of the invention, before the establishment of the operator adequacy business model, according to the analysis requirements of the operator adequacy business and the existing data condition, all evaluation factors related to the operator adequacy business are layered according to different attributes: dividing factors related to the excellence of the seat personnel in the business into a first layer factor and a second layer factor; the first layer factors include: personal characteristics, business capabilities, quality inspection results; the second layer factor is a sub-factor under the first layer factor. The factor stratification is shown in table 1.
TABLE 1
Constructing a pair comparison judgment matrix: starting from the second layer of the hierarchical model, a pair-wise comparison matrix is constructed by a pair-wise comparison method for the factors of the same layer subordinate to each factor of the previous layer, and up to the lowest layer. The expert determines the relative importance of the index to the ability. The relative importance of each layer of two-by-two factor Kij to the previous layer Ki is quantified. Wherein, i represents the number of the first layer factor, and i belongs to {1,2,3 }; j denotes the number of the second layer factor, j ∈ {1,2,3 …, n }, n being a positive integer. From the structure of table 1, 3 decision matrices can be listed. For example, for the factor "K2 service capability" in the first layer, it includes six factors "K21 satisfaction rate" - "K26 average case length" in the second layer, and a comparison judgment matrix is constructed, as shown in table 2.
TABLE 2
| K2 | K21 | K22 | K23 | K24 | K25 | K26 |
| K21 | 1 | 3 | 2 | 2 | 5 | 5 |
| K22 | 1/3 | 1 | 2 | 2 | 4 | 4 |
| K23 | 1/2 | 1/2 | 1 | 1 | 3 | 3 |
| K24 | 1/2 | 1/2 | 1 | 1 | 4 | 4 |
| K25 | 1/5 | 1/4 | 1/3 | 1/4 | 1 | 1/2 |
| K26 | 1/5 | 1/4 | 1/3 | 1/4 | 2 | 1 |
In the decision matrix shown in table 2, each numerical value represents the ratio of the importance of the two corresponding indices. For example: k21: k22 ═ 3: a value of 1, i.e., 3 in the first row and second column of the value region, indicates that the K21 (satisfaction) indicator is more important than the K22 (first resolution) indicator by a degree of 3. Corresponding K22: k21 ═ 1: 3, the value 1/3 in the first column of the second row of the value area.
Calculating weight vectors and performing consistency check: on one hand, due to the complexity of the objective world and the diversity of the recognition problems of people, and on the other hand, due to the absence of fixed reference objects, people can make judgment violating the common sense when performing comparison, and the matrix judgment may not have consistency and can be checked by using a random consistency ratio. (CR ═ CI/RI, where CI represents the consistency index, RI represents the average random consistency index, and when CR < ═ 0.10, the hierarchical single ordering is valid; when CR > is 0.10, the difference is too large and is not effective). Taking the matrix of table 3 as an example, the consistency ratio CR is 0.036<0.1 as calculated by the judgment matrix, and the consistency is satisfied. And calculate the normalized weight coefficient, we can obtain the K2 weight partition, as described in table 3:
calculating a combination weight vector and carrying out combination consistency check: and calculating a combined weight vector of the lowest layer to the target, and performing combined consistency check according to a formula. After passing the inspection, multiplying the weight of each index of the ability obtained by the seat staff by the score for summation, namely
The above formula represents the comprehensive score of the data analysis capability of the employee, wherein k2i is the score of the ith index of the employee under k2Moiety aiI.e. the weight of the term.
And S104, outputting a model result, and calculating to obtain the score of the specific service of the specific seat personnel according to the weight of each factor in the model: and substituting the processed data into a hierarchical analysis model, performing corresponding weight calculation on each data, adding to obtain the score of a specific seat person under the service, and realizing the assignment of the ability factors of each level of the seat person. Table 4 shows examples of operator excellence in business analysis results.
TABLE 4
In another aspect, the present invention provides an apparatus for analyzing excellence of an agent in business based on weight classification, including:
and the data acquisition module is used for extracting data related to the service of the seat personnel from the database and taking the data as a data base for subsequent analysis. The data related to the services of the seat personnel comprise basic attribute data, telephone traffic data, skill data, behavior data and assessment and evaluation data of the seat personnel.
And the data processing module is used for carrying out data cleaning, data integration, data transformation and data specification on the acquired data to obtain integrated data serving as input data of the model. The data cleaning comprises the following steps: deleting repeated and irrelevant data in the collected data; deleting, filling or re-valuing missing data or abnormal data in the acquired data; deleting noise data in the collected data; carrying out standardization processing on format contents of the acquired data; the data integration comprises the steps of integrating the cleaned correlated distributed heterogeneous agent personnel data together by using a data integration method so as to access the integrated data in a transparent mode; the data transformation comprises the steps of converting text data into numerical data by constructing text mapping on the integrated data, and normalizing the numerical data so as to reflect the service capability of the seat personnel in a numerical form and prepare for subsequent modeling; the data specification can adopt a characteristic specification, a sample specification and a characteristic value specification.
The model building module is used for layering factors related to the strong business of the seat staff according to the levels, and obtaining the weight of the measured factors in each layer by applying an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model: dividing factors related to the excellence of the seat personnel in the business into a first layer factor and a second layer factor; the first layer factors include: personal characteristics, business capabilities, quality inspection results; the second layer factor is a sub-factor under the first layer factor.
And the model result output module is used for substituting the processed data into the hierarchical analysis model according to the weight of each factor in the model, calculating the corresponding weight of each data, and adding the calculated weight to obtain the score of the specific seat personnel under the service.
Through the method and the device, the ability of the seat personnel under a certain service can be evaluated, the higher the ability is, the higher the score is, and the quantification of the adequacy of the seat personnel in the service is realized.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for analyzing the adequacy of an agent in service based on weight division is characterized by comprising the following steps:
(1) data acquisition, namely extracting data related to the service of the seat personnel from a database to serve as a data base for subsequent analysis;
(2) data processing, including data cleaning, data integration, data transformation and data specification of the acquired data, and obtaining integrated data as input data of the model;
(3) building a model, namely layering factors related to the strong business of the seat personnel according to the levels, and obtaining the weight of the measured factors in each layer by using an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model;
(4) and outputting a model result, and calculating to obtain the score of the specific service of the specific seat personnel according to the weight of each factor in the model.
2. The method for analyzing the adequacy of the operator based on the weight division as claimed in claim 1, wherein in the step (1), the data related to the operation of the operator comprises basic attribute data, traffic data, skill data, behavior data and assessment evaluation data of the operator.
3. The method for weight-based profiling of human agent adept traffic as claimed in claim 1, wherein in step (2), the data cleansing comprises: deleting repeated and irrelevant data in the collected data; deleting, filling or re-valuing missing data or abnormal data in the acquired data; deleting noise data in the collected data; carrying out standardization processing on format contents of the acquired data;
the data integration comprises the steps that the cleaned distributed heterogeneous agent personnel data which are associated with each other are integrated together by a data integration method so that the integrated data can be accessed in a transparent mode;
the data transformation comprises the steps of converting text data into numerical data by constructing text mapping on the integrated data, and normalizing the numerical data so as to reflect the service capability of the agent personnel in a numerical form and prepare for subsequent modeling;
the data specification can adopt a characteristic specification, a sample specification and a characteristic value specification.
4. The weight division-based agent adept service analysis method according to claim 1, wherein in the step (3), factors related to the agent adept service are divided into a first layer factor and a second layer factor; the first layer factors include: personal characteristics, business capabilities, quality inspection results; the second tier factor is a sub-factor under the first tier factor.
5. The method for analyzing the adequacy of an operator in business based on weight division as claimed in claim 1, wherein in step (4), the processed data is substituted into the hierarchical analysis model, and the data is subjected to corresponding weight calculation and added to obtain the score of a specific operator in the business.
6. An agent excellence business analysis apparatus based on weight classification, characterized by comprising:
the data acquisition module is used for extracting data related to the service of the seat personnel from the database and taking the data as a data base for subsequent analysis;
the data processing module is used for carrying out data cleaning, data integration, data transformation and data specification on the acquired data to obtain integrated data serving as input data of the model;
the model building module is used for layering factors related to the strong business of the seat staff according to the levels, and obtaining the weight of the measured factors in each layer by applying an analytic hierarchy process so as to obtain a corresponding analytic hierarchy model;
and the model result output module is used for calculating the score of the specific service of the specific seat personnel according to the weight of each factor in the model.
7. The apparatus for analyzing the adequacy of an operator based on weight classification as claimed in claim 6, wherein the data related to the operation of the operator comprises basic attribute data, traffic data, skill data, behavior data, assessment data of the operator.
8. The apparatus for weight-based profiling of human agent adept traffic of claim 6, wherein the data cleansing comprises: deleting repeated and irrelevant data in the collected data; deleting, filling or re-valuing missing data or abnormal data in the acquired data; deleting noise data in the collected data; carrying out standardization processing on format contents of the acquired data;
the data integration comprises the steps that the cleaned distributed heterogeneous agent personnel data which are associated with each other are integrated together by a data integration method so that the integrated data can be accessed in a transparent mode;
the data transformation comprises the steps of converting text data into numerical data by constructing text mapping on the integrated data, and normalizing the numerical data so as to reflect the service capability of the agent personnel in a numerical form and prepare for subsequent modeling;
the data specification can adopt a characteristic specification, a sample specification and a characteristic value specification.
9. The apparatus for analyzing the adept service of the operator based on the weight division as claimed in claim 6, wherein the model construction module divides factors related to the adept service of the operator into a first layer factor and a second layer factor; the first layer factors include: personal characteristics, business capabilities, quality inspection results; the second tier factor is a sub-factor under the first tier factor.
10. The apparatus for analyzing the adequacy of an operator in service based on weight division as claimed in claim 6, wherein the model result output module substitutes the processed data into the hierarchical analysis model, and the data are subjected to corresponding weight calculation and added to obtain the score of a specific operator in the service.
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