CN115063016A - Data analysis method, system and storage medium based on intelligent office - Google Patents
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Abstract
The invention discloses a data analysis method, a system and a storage medium based on intelligent office, which analyze office behavior evaluation proportion indexes corresponding to target customer service personnel by monitoring office behavior data corresponding to the target customer service personnel, thereby realizing quantitative data analysis by using an intelligent monitoring mode, improving the conformity of the office quality evaluation result of the customer service personnel with the actual situation, simultaneously extracting the electric chat information of the target customer service personnel, obtaining the content conformity, the client responsiveness and the client satisfaction of the electric chat corresponding to the target customer service personnel, analyzing the office content evaluation proportion indexes corresponding to the target customer service personnel, comprehensively analyzing the office quality evaluation coefficient corresponding to the target customer service personnel, carrying out corresponding processing according to the comparison and analysis result, and improving the accuracy and reliability of the office quality evaluation result of the customer service personnel, and a reference basis is provided for the enterprise to control the office quality of the customer service staff.
Description
Technical Field
The invention relates to the technical field of office data analysis, in particular to a data analysis method, a data analysis system and a storage medium based on intelligent office.
Background
With the technological progress, the dependence of economic society development on services is increasingly enhanced. The customer service industry plays an important role in the production and management activities of enterprises, and is a service window which closely contacts the enterprises and clients. Office data monitoring is an important means of modern enterprise management, and has positive promotion effect on promoting the real-time office quality control of customer service staff and the normalization of office behavior supervision.
At present, in order to guarantee the office quality of customer service staff, follow listening to the office chat data of the customer service staff usually through the mode of manual spot check, evaluate the office quality of the customer service staff, but not only consume huge manpower and material resources and time like this, and the evaluation efficiency is not high, and the conditions such as missed listening, wrong listening easily appear in the process of following the office chat data, thereby the office quality evaluation result that leads to the customer service staff lacks accuracy and reliability, simultaneously because the manual evaluation does not have unified evaluation standard, rely on completely with the subjective judgment of evaluators, and then lack and carry out comprehensive, systematic, scientific evaluation to the office quality of the customer service staff, can't provide the reference for the enterprise to the management and control of the office quality of the customer service staff, further can's personnel management and control demand can't be satisfied.
Meanwhile, most of the existing customer service staff office data acquisition modes are acquired through irregular patrol of enterprise managers, but office behavior data of the customer service staff cannot be monitored in real time, so that quantitative data analysis cannot be performed by using an intelligent monitoring mode, data and technical support cannot be further provided for office behavior data analysis of the customer service staff, evaluation of the office quality of the customer service staff by the enterprise is further influenced, and the situation that the office quality evaluation result of the customer service staff is inconsistent with the actual situation is caused.
Disclosure of Invention
In view of the above, to solve the problems in the background art, a data analysis method, a system and a storage medium based on intelligent office are proposed.
In order to achieve the above object, in a first aspect, the present invention provides a data analysis method based on intelligent office, including the following steps:
step one, monitoring office behaviors of customer service personnel: recording each office customer service person in an office area of an enterprise to be analyzed as each target customer service person, and monitoring the office behavior corresponding to each target customer service person in real time to obtain office behavior data corresponding to each target customer service person;
step two, analyzing office behavior data of customer service personnel: analyzing and obtaining the office behavior evaluation ratio index corresponding to each target customer service staff according to the office behavior data corresponding to each target customer service staff;
step three, customer service personnel chat information acquisition: extracting the e-chat information of each target customer service person from the enterprise customer service management center to be analyzed, wherein the e-chat information comprises the duration, content and customer evaluation score of each e-chat;
step four, customer service personnel chat information analysis: analyzing the e-chat information of each target customer service person to respectively obtain the content conformity, the customer responsiveness and the customer satisfaction degree of each e-chat corresponding to each target customer service person;
fifthly, analyzing the office content evaluation proportion index: analyzing office content evaluation proportion indexes corresponding to the target customer service personnel according to the content conformity, the customer responsiveness and the customer satisfaction of each electric chat corresponding to each target customer service personnel;
step six, evaluating the office quality of customer service staff: and obtaining office quality evaluation coefficients corresponding to the target customer service personnel according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to the target customer service personnel, and performing corresponding processing according to the comparison and analysis result.
Further, in the first step, the office behavior data corresponding to each target customer service person is obtained in a manner that:
monitoring office behaviors corresponding to target customer service personnel in real time through a high-definition camera installed in an enterprise office area to be analyzed, and counting office behavior data corresponding to the target customer service personnel, wherein the office behavior data comprises the number of times of leaving a station, the time length of each station leaving behavior, the number of times of abnormal behavior actions and the duration time of each abnormal behavior action.
Further, the office behavior evaluation proportion index analysis mode corresponding to each target customer service person in the second step is as follows:
extracting the corresponding station leaving times of each target customer service person and the duration of each station leaving behavior, and marking the station leaving times corresponding to each target customer service person as x i Wherein i is 1,2, n, i is represented as the number of the ith target customer service staff, and the time length of each target customer service staff corresponding to each leaving work station behavior is marked as t ij Wherein j is 1,2, and m, j is the number of the j-th leaving station behavior, the leaving station behavior evaluation weight index corresponding to each target customer service person is obtained through analysis, and the leaving station behavior evaluation weight index is marked as psi i 1 ；
Extracting the times of the abnormal behavior actions corresponding to the target customer service personnel and the duration of each abnormal behavior action, and marking the times of the abnormal behavior actions corresponding to the target customer service personnel as y i And marking the duration of each abnormal behavior action corresponding to each target customer service person as t' ir Wherein r is 1,2, and u, r is the number of the r-th abnormal behavior action, the abnormal behavior evaluation weight index corresponding to each target customer service person is obtained through analysis, and the abnormal behavior evaluation weight index is marked as psi i 2 ；
Evaluating the leaving station behavior evaluation weight index psi corresponding to each target customer service personnel i 1 And abnormal behavior evaluation weight index psi i 2 Substituting into formula
Obtaining the office behavior evaluation ratio index xi corresponding to each target customer service staff i Wherein e is represented by a natural constant, δ 1 、δ 2 Respectively expressed as evaluation influence factors corresponding to the preset customer service person leaving work station behavior and the customer service person abnormal behavior, wherein delta 1 +δ 2 ＝1。
Further, the obtaining method of the content conformity degree corresponding to each electric chat of each target customer service person in the fourth step includes:
extracting standard tone corresponding to each target customer service person stored in an enterprise customer service management database, screening each customer service session text in each electric chat content corresponding to each target customer service person according to each electric chat content corresponding to each target customer service person, and extracting key words from each customer service session text in each electric chat content corresponding to each target customer service person through a key word extraction technology to obtain key words of each customer service session text in each electric chat content corresponding to each target customer service person;
extracting data stored in an enterprise customer service management databaseSetting each standard key vocabulary in the contents of the e-chat topics by enterprises, carrying out contrastive statistics on the number of the customer service session texts which accord with the set e-chat topics in each e-chat content corresponding to each target customer service person, recording the number of the customer service session texts which accord with each e-chat content corresponding to each target customer service person as the number of the customer service session texts which accord with each e-chat content, and marking the number as w if F is 1,2,., g, f is expressed as the f-th electric chat; and counting the number of customer service session texts related to the set E-chat subjects in the E-chat contents corresponding to the target customer service personnel, recording the number as the number related to the customer service session texts related to the E-chat contents corresponding to the target customer service personnel, and marking the number as w' if ；
Analyzing the content conformity of each target customer service personnel corresponding to each electric chat
Wherein epsilon 1 、ε 2 Respectively expressing the corresponding electric chat content coincidence weight factors of the preset conversation text coincidence quantity and the conversation text association quantity, W if And the total number of the text of the customer service session corresponding to the f-th e chat content is represented as the ith target customer service person.
Further, the obtaining method of the customer responsiveness of each target customer service person corresponding to each chat service in the fourth step includes:
according to the content of each electric chat corresponding to each target customer service person, extracting the end time of the customer service session, the start time of the customer session and the end time of the customer session when each voice interaction corresponds to each electric chat content of each target customer service person, and respectively marking the end time of the customer service session, the start time of the customer session and the end time of the customer session when each voice interaction corresponds to each electric chat content of each target customer service person as p if a′ s 、p if b s 、p if b′ s Wherein s 1,2, d, s represents the s-th speech interaction;
analyzing the customer responsiveness of each target customer service personnel corresponding to each chat
Further, the obtaining method of the customer satisfaction corresponding to each electric chat for each target customer service person in the fourth step includes:
extracting the time length and the customer evaluation score of each target customer service person corresponding to each electric chat according to the electric chat information of each target customer service person, and respectively marking the time length and the customer evaluation score of each target customer service person corresponding to each electric chat as q if 1 、q if 2 ；
Analyzing customer satisfaction degree of each target customer service personnel corresponding to each electric chat
Wherein λ 1 、λ 2 Respectively expressed as satisfaction weight factors, q 'corresponding to preset electric chat duration and customer appraisal score' Threshold(s) Expressed as a preset customer service person chat duration threshold, Q Preparation of Expressed as a preset customer rating score threshold.
Further, in the fifth step, the office content evaluation proportion index corresponding to each target customer service person is analyzed, and the specific analysis includes:
the content corresponding to each electric chat of each target customer service personnel is conformed to the degree phi if Customer responsiveness
And customer satisfaction
Substituting into the office content evaluation proportion index analysis formula
Obtaining office content evaluation proportion index theta corresponding to each target customer service staff i In which beta is 1 、β 2 、β 3 And respectively expressing the evaluation influence weight factors corresponding to preset electric chat content conformity, client responsiveness and client satisfaction.
Further, the office quality evaluation coefficient analysis formula corresponding to each target customer service person in the sixth step is
Wherein phi i The evaluation coefficient is expressed as an office quality evaluation coefficient corresponding to the ith target customer service staff, and e is expressed as a natural constant.
In a second aspect, the present invention further provides a data analysis system based on intelligent office, including:
the system comprises a customer service staff office behavior monitoring module, a data analysis module and a data analysis module, wherein the customer service staff office behavior monitoring module is used for recording each working customer service staff in an enterprise office area to be analyzed as each target customer service staff, and monitoring the office behavior corresponding to each target customer service staff in real time to obtain office behavior data corresponding to each target customer service staff;
the customer service staff office behavior data analysis module is used for analyzing and obtaining the office behavior evaluation proportion index corresponding to each target customer service staff according to the office behavior data corresponding to each target customer service staff;
the enterprise customer service management database is used for storing standard tone corresponding to each target customer service person and each standard key vocabulary in the enterprise set e-chat subject content;
the system comprises a customer service staff chatting information acquisition module, a service staff chatting information acquisition module and a service staff chatting information analysis module, wherein the customer service staff chatting information acquisition module is used for extracting chatting information of each target customer service staff from an enterprise customer service management center to be analyzed, and the chatting information comprises the duration, the content and the customer evaluation score of each chatting;
the customer service staff e-chat information analysis module is used for analyzing the e-chat information of each target customer service staff to respectively obtain the content conformity, the customer responsiveness and the customer satisfaction degree of each e-chat corresponding to each target customer service staff;
the office content evaluation proportion index analysis module is used for analyzing the office content evaluation proportion index corresponding to each target customer service staff according to the content conformity, the customer responsiveness and the customer satisfaction of each electric chat corresponding to each target customer service staff;
and the customer service staff office quality evaluation module is used for obtaining the office quality evaluation coefficient corresponding to each target customer service staff according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to each target customer service staff and performing corresponding processing according to the comparison and analysis result.
In a third aspect, the present invention further provides a data analysis storage medium based on intelligent office, where a computer program is burned in the computer storage medium, and when the computer program runs in a memory of a server, the data analysis storage medium implements the data analysis method based on intelligent office.
Compared with the prior art, the data analysis method, the data analysis system and the storage medium based on intelligent office have the following beneficial effects:
according to the method and the system, office behavior data corresponding to the target customer service staff are obtained by monitoring office behaviors corresponding to the target customer service staff in real time, and the office behavior evaluation scale index corresponding to the target customer service staff is obtained through analysis, so that the office behavior data of the customer service staff can be monitored in real time, quantitative data analysis is realized by using an intelligent monitoring mode, data and technical support are further provided for office behavior data analysis of the customer service staff, the conformity of the office quality evaluation result of the customer service staff with the actual situation is further improved, and the accuracy of the office quality evaluation result of the customer service staff by later-stage enterprises is increased.
According to the method, the content conformity, the client responsiveness and the client satisfaction degree of each electric chat corresponding to each target customer service person are obtained by extracting the electric chat information of each target customer service person and analyzing, the office content evaluation proportion index corresponding to each target customer service person is further analyzed, so that a large amount of manpower, material resources and time cost can be saved, the office quality evaluation efficiency of an enterprise on the customer service persons is improved, the situations of missed listening and wrong listening in the manual listening following process are effectively avoided, meanwhile, the office quality evaluation coefficient corresponding to each target customer service person is obtained according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to each target customer service person, and corresponding processing is performed according to the comparative analysis result, so that the accuracy and reliability of the office quality evaluation result of the customer service persons are improved, and the office quality of the customer service persons is comprehensively carried out, Systematic and scientific evaluation is carried out, reference basis is further provided for the management and control of the office quality of customer service staff by enterprises, and the personnel management and control requirements of the enterprises are met to the greatest extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
fig. 2 is a system module connection diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a first aspect of the present invention provides a data analysis method based on intelligent office, including the following steps:
step one, monitoring office behaviors of customer service personnel: and recording each office customer service person in the office area of the enterprise to be analyzed as each target customer service person, and monitoring the office behavior corresponding to each target customer service person in real time to obtain the office behavior data corresponding to each target customer service person.
On the basis of the above embodiment, in the step one, the office behavior data corresponding to each target customer service person is obtained in the following manner:
monitoring office behaviors corresponding to target customer service personnel in real time through a high-definition camera installed in an enterprise office area to be analyzed, and counting office behavior data corresponding to the target customer service personnel, wherein the office behavior data comprises the number of times of leaving a station, the time length of each station leaving behavior, the number of times of abnormal behavior actions and the duration time of each abnormal behavior action.
As a specific embodiment of the present invention, the statistical manner of the office behavior data corresponding to each target customer service staff is as follows:
according to the office behavior monitoring video of each target customer service person, counting the number of station leaving times corresponding to each target customer service person, extracting the leaving time and the sitting time of each station leaving behavior corresponding to each target customer service person, and comparing the leaving time of each station leaving behavior corresponding to each target customer service person with the corresponding sitting time to obtain the time length of each station leaving behavior corresponding to each target customer service person;
extracting each office behavior action image in the office behavior monitoring video of each target customer service person according to the office behavior monitoring video of each target customer service person, recording the office behavior action image as each office behavior action image corresponding to each target customer service person, comparing each office behavior action image corresponding to each target customer service person with each preset abnormal behavior action image, counting the similarity between each office behavior action image corresponding to each target customer service person and each preset abnormal behavior action image, recording the office behavior action corresponding to a target customer service person as an abnormal behavior action if the similarity between the office behavior action image corresponding to the target customer service person and a preset abnormal behavior action image is larger than or equal to a preset similarity threshold value, counting the number of times of the abnormal behavior action corresponding to each target customer service person, and extracting the starting time and the ending time of each abnormal behavior action corresponding to each target customer service person, and comparing to obtain the duration of each abnormal behavior action corresponding to each target customer service person.
Step two, analyzing office behavior data of customer service personnel: and analyzing to obtain the office behavior evaluation ratio index corresponding to each target customer service staff according to the office behavior data corresponding to each target customer service staff.
On the basis of the above embodiment, the office behavior evaluation proportion index analysis mode corresponding to each target customer service person in the second step is as follows:
extracting the corresponding station leaving times of each target customer service person and the duration of each station leaving behavior, and marking the station leaving times corresponding to each target customer service person as x i Wherein, i is 1,2, and n, i is the number of the ith target customer service person, and the time length corresponding to each leaving work station behavior of each target customer service person is marked as t ij Wherein j is 1,2, and m, j is the number of the j-th leaving station behavior, the leaving station behavior evaluation weight index corresponding to each target customer service person is obtained through analysis, and the leaving station behavior evaluation weight index is marked as psi i 1 ；
Extracting the times of the abnormal behavior actions corresponding to the target customer service personnel and the duration of each abnormal behavior action, and marking the times of the abnormal behavior actions corresponding to the target customer service personnel as y i And marking the duration of each abnormal behavior action corresponding to each target customer service person as t' ir Wherein r is 1,2, and u, r is the number of the r-th abnormal behavior action, the abnormal behavior evaluation weight index corresponding to each target customer service person is obtained through analysis, and the abnormal behavior evaluation weight index is marked as psi i 2 ；
Evaluating the leaving station behavior evaluation weight index psi corresponding to each target customer service personnel i 1 And abnormal behavior evaluation weight index psi i 2 Substitution formula
Obtaining the office behavior evaluation ratio index xi corresponding to each target customer service staff i Wherein e is represented by a natural constant, δ 1 、δ 2 Respectively expressed as evaluation influence factors corresponding to the preset customer service person leaving work station behavior and the customer service person abnormal behavior, wherein delta 1 +δ 2 ＝1。
As a specific embodiment of the invention, the evaluation weight index analysis formula of the leaving station behavior corresponding to each target customer service person is
Wherein mu 1 Expressed as a preset correction factor, delta t, for the behavior of the customer service personnel leaving the station Allow for Indicating a preset allowable time length T for a customer service person to leave a station once Duration of time Expressed as the preset total office time, k, of the customer service staff corresponding to the enterprise to be analyzed 1 Expressed as the frequency of allowed departures of a preset customer service staff office time unit.
As a specific embodiment of the present invention, the abnormal behavior evaluation weight index analysis formula corresponding to each target customer service person is
Wherein mu 2 Is expressed as a preset customer service person abnormal behavior evaluation correction factor delta t' Allow for Expressed as a preset duration, k, of abnormal behavior of the customer service person 2 And the allowable abnormal behavior action frequency of the office time of the customer service staff is expressed as preset.
In this embodiment, the office behavior corresponding to each target customer service person is monitored in real time, the office behavior data corresponding to each target customer service person is obtained, and the office behavior evaluation scale index corresponding to each target customer service person is obtained through analysis, so that the office behavior data of the customer service persons can be monitored in real time, quantitative data analysis is realized by using an intelligent monitoring mode, data and technical support are further provided for the office behavior data analysis of the customer service persons, the conformity of the office quality evaluation results of the customer service persons with actual conditions is further improved, and the accuracy of the office quality evaluation results of the customer service persons by later-stage enterprises is increased.
Step three, customer service personnel chat information acquisition: and E-chat information of each target customer service person is extracted from the enterprise customer service management center to be analyzed, wherein the E-chat information comprises the duration, content and customer evaluation score of each E-chat.
Step four, customer service personnel chat information analysis: and analyzing the e-chat information of each target customer service person to respectively obtain the content conformity, the customer responsiveness and the customer satisfaction of each e-chat corresponding to each target customer service person.
On the basis of the above embodiment, the obtaining method of the content conformity degree of each electric chat corresponding to each target customer service person in the fourth step includes:
extracting standard tone corresponding to each target customer service person stored in an enterprise customer service management database, screening each customer service session text in each electric chat content corresponding to each target customer service person according to each electric chat content corresponding to each target customer service person, and extracting key words from each customer service session text in each electric chat content corresponding to each target customer service person through a key word extraction technology to obtain key words of each customer service session text in each electric chat content corresponding to each target customer service person;
extracting each standard key vocabulary in the contents of the enterprise set chatting theme stored in the enterprise customer service management database, comparing and counting the number of customer service session texts which accord with the set chatting theme in each chatting content corresponding to each target customer service person, recording the number of the customer service session texts which accord with each chatting content corresponding to each target customer service person as the number of the customer service session texts, and marking the number as w if F is 1,2,., g, f is expressed as the f-th electric chat; and counting the number of customer service session texts related to the set E-chat subjects in the E-chat contents corresponding to the target customer service personnel, recording the number as the number related to the customer service session texts related to the E-chat contents corresponding to the target customer service personnel, and marking the number as w' if ；
Analyzing the content conformity of each target customer service personnel corresponding to each electric chat
Wherein epsilon 1 、ε 2 Respectively expressing the corresponding electric chat content coincidence weight factors of the preset conversation text coincidence quantity and the conversation text association quantity, W if And the total number of the text of the customer service session corresponding to the f-th e chat content is represented as the ith target customer service person.
It should be noted that, in the above description, the screening method of each customer service session text in each chat content corresponding to each target customer service person is as follows:
and if the tone of a certain session text in the electric chat content corresponding to a certain target customer service person is the same as the standard tone of the corresponding target customer service person, the session text in the electric chat content corresponding to the target customer service person is the customer service session text, and the service session texts in the electric chat content corresponding to the target customer service person are counted.
As a specific embodiment of the present invention, the statistical manner of the number of the customer service session texts corresponding to each e-chat content and conforming to the set e-chat topic of each target customer service person is as follows:
and comparing the key vocabulary of each customer service session text in each electric chat content corresponding to each target customer service person with each standard key vocabulary in the content of the enterprise set electric chat theme, if the key vocabulary of a certain customer service session text in a certain electric chat content corresponding to a certain target customer service person is the same as the standard key vocabulary, conforming the customer service session text in the electric chat content corresponding to the target customer service person to the set electric chat theme, and counting the number of the customer service session texts conforming to the set electric chat theme in each electric chat content corresponding to each target customer service person.
Further, the statistical manner of the number of the customer service session texts in which the target customer service personnel associates and sets the e-chat topics in the e-chat content is as follows:
comparing key words of each customer service session text in each electric chat content corresponding to each target customer service person with each standard key word in the content of the enterprise set electric chat subject, if the key words of a certain customer service session text in a certain electric chat content corresponding to a certain target customer service person are different from each standard key word, the corresponding customer service session text in the electric chat content corresponding to the target customer service person does not accord with the set electric chat subject, counting each customer service session text which does not accord with the set electric chat subject in each electric chat content corresponding to each target customer service person, recording the corresponding customer service session text in each electric chat content corresponding to each target customer service person, comparing the key words of each appointed customer service session text in each electric chat content corresponding to each target customer service person with each preset standard key word corresponding to each similar word, and comparing the key words of the appointed customer service session text in the electric chat content corresponding to a certain target customer service person with a certain preset standard key word, if the corresponding to a certain target customer service person corresponds to a certain electric chat content And if the corresponding word is the same, the target customer service personnel sets the e-chat theme in association with the specified customer service session text in the e-chat content, and counts the number of the customer service session texts of the associated set e-chat themes in the e-chat content corresponding to each target customer service personnel.
On the basis of the above embodiment, the obtaining method of the customer responsiveness of each target customer service person corresponding to each chat in the fourth step includes:
according to the content of each electric chat corresponding to each target customer service person, extracting the customer service session ending time, the customer session starting time and the customer session ending time of each voice interaction in each electric chat content corresponding to each target customer service person, and respectively marking the customer service session ending time, the customer session starting time and the customer session ending time of each voice interaction in each electric chat content corresponding to each target customer service person as p if a′ s 、p if b s 、p if b′ s Wherein s is 1,2, a, d, s represents the s-th voice interaction;
analyzing the customer responsiveness of each target customer service personnel corresponding to each chat
On the basis of the above embodiment, the obtaining method of the customer satisfaction corresponding to each chat by each target customer service person in the fourth step includes:
extracting the time length and the customer evaluation score of each electric chat corresponding to each target customer service person according to the electric chat information of each target customer service person, and respectively marking the time length and the customer evaluation score of each electric chat corresponding to each target customer service person as q if 1 、q if 2 ；
Analyzing customer satisfaction degree of each target customer service personnel corresponding to each electric chat
Wherein λ 1 、λ 2 Respectively expressed as satisfaction weight factors q 'corresponding to preset electric chat duration and customer evaluation score' Threshold(s) Expressed as a preset customer service person chat duration threshold, Q Preparation of Expressed as a preset customer rating score threshold.
Fifthly, analyzing the office content evaluation proportion index: and analyzing the office content evaluation proportion index corresponding to each target customer service staff according to the content conformity, the customer responsiveness and the customer satisfaction of each electric chat corresponding to each target customer service staff.
On the basis of the above embodiment, the analyzing of the office content evaluation proportion index corresponding to each target customer service person in the fifth step includes:
the content corresponding to each electric chat of each target customer service personnel is conformed to the degree phi if Customer responsiveness
And customer satisfaction
Substituting into office content evaluation proportion index analysis formula
Obtaining office content evaluation proportion index theta corresponding to each target customer service staff i Wherein beta is 1 、β 2 、β 3 And respectively expressing the evaluation influence weight factors corresponding to preset electric chat content conformity, client responsiveness and client satisfaction.
Step six, evaluating the office quality of customer service staff: and obtaining office quality evaluation coefficients corresponding to the target customer service personnel according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to the target customer service personnel, and performing corresponding processing according to the comparison and analysis result.
On the basis of the above embodiment, the office quality evaluation coefficient analysis formula corresponding to each target customer service person in the sixth step is
Wherein phi i The evaluation coefficient is expressed as an office quality evaluation coefficient corresponding to the ith target customer service person, and e is expressed as a natural constant.
As a specific embodiment of the present invention, in the above, the office quality evaluation coefficient corresponding to each target customer service person is compared with a preset customer service person office quality evaluation coefficient threshold, and if the office quality evaluation coefficient corresponding to a certain target customer service person is smaller than the preset customer service person office quality evaluation coefficient threshold, which indicates that the office quality corresponding to the target customer service person does not meet the requirement, the enterprise manager to be analyzed is notified to perform tracing processing on the target customer service person.
In this embodiment, the method extracts and analyzes the chat information of each target customer service person to obtain the content conformity, the client responsiveness and the client satisfaction degree of each chat corresponding to each target customer service person, and further analyzes the office content evaluation proportion index corresponding to each target customer service person, so that a large amount of manpower, material resources and time cost can be saved, the office quality evaluation efficiency of an enterprise on the customer service persons can be improved, the situations of missed listening and wrong listening in the manual listening following process can be effectively avoided, meanwhile, the office quality evaluation coefficient corresponding to each target customer service person can be obtained according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to each target customer service person, and corresponding processing is performed according to the comparative analysis result, so that the accuracy and reliability of the office quality evaluation result of the customer service person can be improved, and the comprehensive office quality of the customer service person can be realized, Systematic and scientific evaluation is carried out, reference basis is further provided for the management and control of the office quality of customer service staff by enterprises, and the personnel management and control requirements of the enterprises are met to the greatest extent.
Referring to fig. 2, a second aspect of the present invention provides a data analysis system based on smart office, including a customer service staff office activity monitoring module, a customer service staff office activity data analysis module, an enterprise customer service management database, a customer service staff e-chat information acquisition module, a customer service staff e-chat information analysis module, an office content evaluation proportion index analysis module, and a customer service staff office quality evaluation module.
The customer service staff office behavior monitoring module is respectively connected with the customer service staff office behavior data analysis module and the customer service staff electric chat information acquisition module, the customer service staff electric chat information analysis module is respectively connected with the customer service staff electric chat information acquisition module, the enterprise customer service management database and the office content evaluation proportion index analysis module, and the customer service staff office quality evaluation module is respectively connected with the customer service staff office behavior data analysis module and the office content evaluation proportion index analysis module.
The customer service staff office behavior monitoring module is used for recording each office customer service staff in the enterprise office area to be analyzed as each target customer service staff, monitoring the office behavior corresponding to each target customer service staff in real time, and obtaining the office behavior data corresponding to each target customer service staff.
The customer service staff office behavior data analysis module is used for analyzing and obtaining the office behavior evaluation proportion index corresponding to each target customer service staff according to the office behavior data corresponding to each target customer service staff.
And the enterprise customer service management database is used for storing the standard tone corresponding to each target customer service person and each standard key vocabulary in the content of the enterprise set e-chat topic.
The customer service staff e-chat information acquisition module is used for extracting e-chat information of each target customer service staff from the enterprise customer service management center to be analyzed, wherein the e-chat information comprises the duration, content and customer evaluation score of each e-chat.
The customer service staff e-chat information analysis module is used for analyzing the e-chat information of each target customer service staff to respectively obtain the content conformity, the customer responsiveness and the customer satisfaction degree of each e-chat corresponding to each target customer service staff.
The office content evaluation proportion index analysis module is used for analyzing the office content evaluation proportion index corresponding to each target customer service staff according to the content conformity, the customer responsiveness and the customer satisfaction of each electric chat corresponding to each target customer service staff.
The customer service staff office quality evaluation module is used for obtaining office quality evaluation coefficients corresponding to the target customer service staff according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to the target customer service staff and carrying out corresponding processing according to the comparison and analysis result.
In a third aspect, the present invention further provides a data analysis storage medium based on intelligent office, where a computer program is burned in the computer storage medium, and when the computer program runs in a memory of a server, the data analysis storage medium implements the data analysis method based on intelligent office.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (10)
1. A data analysis method based on intelligent office is characterized by comprising the following steps:
step one, monitoring office behaviors of customer service personnel: recording each office customer service person in an office area of an enterprise to be analyzed as each target customer service person, and monitoring the office behavior corresponding to each target customer service person in real time to obtain office behavior data corresponding to each target customer service person;
step two, analyzing office behavior data of customer service personnel: analyzing and obtaining the office behavior evaluation ratio index corresponding to each target customer service staff according to the office behavior data corresponding to each target customer service staff;
step three, customer service personnel chat information acquisition: extracting the e-chat information of each target customer service person from the enterprise customer service management center to be analyzed, wherein the e-chat information comprises the duration, content and customer evaluation score of each e-chat;
step four, customer service personnel chat information analysis: analyzing the e-chat information of each target customer service person to respectively obtain the content conformity, the customer responsiveness and the customer satisfaction degree of each e-chat corresponding to each target customer service person;
fifthly, analyzing the office content evaluation proportion index: analyzing office content evaluation proportion indexes corresponding to the target customer service personnel according to the content conformity, the customer responsiveness and the customer satisfaction of each electric chat corresponding to each target customer service personnel;
step six, evaluating the office quality of customer service staff: and obtaining office quality evaluation coefficients corresponding to the target customer service personnel according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to the target customer service personnel, and performing corresponding processing according to the comparison and analysis result.
2. The intelligent office-based data analysis method of claim 1, wherein: in the first step, the office behavior data corresponding to each target customer service person is obtained in the following mode:
monitoring office behaviors corresponding to target customer service personnel in real time through a high-definition camera installed in an enterprise office area to be analyzed, and counting office behavior data corresponding to the target customer service personnel, wherein the office behavior data comprises the number of times of leaving a station, the time length of each station leaving behavior, the number of times of abnormal behavior actions and the duration time of each abnormal behavior action.
3. The intelligent office based data analysis method of claim 2, wherein: the office behavior evaluation proportion index analysis mode corresponding to each target customer service staff in the step two is as follows:
extracting the corresponding leaving station number of each target customer service staffThe number and the time length of each station leaving behavior mark the station leaving times corresponding to each target customer service staff as x i Wherein, i is 1,2, and n, i is the number of the ith target customer service person, and the time length corresponding to each leaving work station behavior of each target customer service person is marked as t ij Wherein j is 1,2, the words, m and j are expressed as the serial number of the j-th leaving work station behavior, the leaving work station behavior evaluation weight index corresponding to each target customer service staff is obtained through analysis, and the leaving work station behavior evaluation weight index is marked as psi i 1 ；
Extracting the times of the abnormal behavior actions corresponding to the target customer service personnel and the duration of each abnormal behavior action, and marking the times of the abnormal behavior actions corresponding to the target customer service personnel as y i And marking the duration of each abnormal behavior action corresponding to each target customer service person as t' ir Wherein r is 1,2, and u, r is the number of the r-th abnormal behavior action, the abnormal behavior evaluation weight index corresponding to each target customer service person is obtained through analysis, and the abnormal behavior evaluation weight index is marked as psi i 2 ；
Evaluating the leaving station behavior evaluation weight index psi corresponding to each target customer service personnel i 1 And abnormal behavior evaluation weight index psi i 2 Substitution formula
Obtaining the office behavior evaluation ratio index xi corresponding to each target customer service staff i Wherein e is represented by a natural constant, δ 1 、δ 2 Respectively expressed as evaluation influence factors corresponding to preset customer service personnel station leaving behaviors and customer service personnel abnormal behaviors, wherein delta 1 +δ 2 ＝1。
4. The intelligent office-based data analysis method of claim 1, wherein: the content conformity obtaining mode corresponding to each electric chat of each target customer service person in the fourth step comprises the following steps:
extracting standard tone corresponding to each target customer service person stored in an enterprise customer service management database, screening each customer service session text in each electric chat content corresponding to each target customer service person according to each electric chat content corresponding to each target customer service person, and extracting key words from each customer service session text in each electric chat content corresponding to each target customer service person through a key word extraction technology to obtain key words of each customer service session text in each electric chat content corresponding to each target customer service person;
extracting each standard key vocabulary in the contents of the enterprise set chatting theme stored in the enterprise customer service management database, comparing and counting the number of customer service session texts which accord with the set chatting theme in each chatting content corresponding to each target customer service person, recording the number of the customer service session texts which accord with each chatting content corresponding to each target customer service person as the number of the customer service session texts, and marking the number as w if F is 1,2, g, f represents the f-th chat; and counting the number of customer service session texts related to the set E-chat subjects in the E-chat contents corresponding to the target customer service personnel, recording the number as the number related to the customer service session texts related to the E-chat contents corresponding to the target customer service personnel, and marking the number as w' if ；
Analyzing the content conformity of each target customer service personnel corresponding to each electric chat
Wherein epsilon 1 、ε 2 Respectively expressing the corresponding electric chat content coincidence weight factors of the preset conversation text coincidence quantity and the conversation text association quantity, W if And the total number of the text of the customer service session corresponding to the f-th e chat content is represented as the ith target customer service person.
5. The intelligent office-based data analysis method of claim 4, wherein: the method for obtaining the customer responsiveness of each target customer service person corresponding to each chat in the fourth step comprises the following steps:
according to the content of each electric chat corresponding to each target customer service person, extracting the end time of the customer service session, the start time of the customer session and the end time of the customer session when each voice interaction corresponds to each electric chat content in each target customer service person, and corresponding each target customer service person to each electric chatThe end time of the customer service session, the start time of the customer session and the end time of the customer session in each voice interaction are respectively marked as p if a′ s 、p if b s 、p if b′ s Wherein s 1,2, d, s represents the s-th speech interaction;
analyzing the customer responsiveness of each target customer service personnel corresponding to each chat
6. The intelligent office-based data analysis method of claim 5, wherein: the method for obtaining the customer satisfaction corresponding to each chat in the fourth step for each target customer service person comprises the following steps:
extracting the time length and the customer evaluation score of each electric chat corresponding to each target customer service person according to the electric chat information of each target customer service person, and respectively marking the time length and the customer evaluation score of each electric chat corresponding to each target customer service person as q if 1 、q if 2 ；
Analyzing the customer satisfaction degree of each target customer service personnel corresponding to each electric chat
Wherein λ 1 、λ 2 Respectively expressed as satisfaction weight factors q 'corresponding to preset electric chat duration and customer evaluation score' Threshold(s) Expressed as a preset customer service person chat duration threshold, Q Preparation of Expressed as a preset customer rating score threshold.
7. The intelligent office-based data analysis method of claim 6, wherein: analyzing the office content evaluation proportion index corresponding to each target customer service staff in the fifth step, wherein the concrete analysis comprises the following steps:
the content corresponding to each electric chat of each target customer service personnel is conformed to the degree phi if Customer responsiveness
And customer satisfaction
Substituting into the office content evaluation proportion index analysis formula
Obtaining office content evaluation proportion index theta corresponding to each target customer service staff i Wherein beta is 1 、β 2 、β 3 And respectively expressing the evaluation influence weight factors corresponding to preset electric chat content conformity, client responsiveness and client satisfaction.
8. The intelligent office-based data analysis method of claim 1, wherein: the office quality evaluation coefficient analysis formula corresponding to each target customer service staff in the step six is
Wherein phi i The evaluation coefficient is expressed as an office quality evaluation coefficient corresponding to the ith target customer service person, and e is expressed as a natural constant.
9. The utility model provides a data analysis system based on wisdom official working which characterized in that:
the system comprises a customer service staff office behavior monitoring module, a data analysis module and a data analysis module, wherein the customer service staff office behavior monitoring module is used for recording each working customer service staff in an enterprise office area to be analyzed as each target customer service staff, and monitoring the office behavior corresponding to each target customer service staff in real time to obtain office behavior data corresponding to each target customer service staff;
the customer service staff office behavior data analysis module is used for analyzing and obtaining the office behavior evaluation proportion index corresponding to each target customer service staff according to the office behavior data corresponding to each target customer service staff;
the enterprise customer service management database is used for storing standard tone corresponding to each target customer service person and each standard key vocabulary in the enterprise set e-chat subject content;
the system comprises a customer service staff chatting information acquisition module, a service staff chatting information acquisition module and a service staff chatting information analysis module, wherein the customer service staff chatting information acquisition module is used for extracting chatting information of each target customer service staff from an enterprise customer service management center to be analyzed, and the chatting information comprises the duration, the content and the customer evaluation score of each chatting;
the customer service staff e-chat information analysis module is used for analyzing the e-chat information of each target customer service staff to respectively obtain the content conformity, the customer responsiveness and the customer satisfaction degree of each e-chat corresponding to each target customer service staff;
the office content evaluation proportion index analysis module is used for analyzing the office content evaluation proportion index corresponding to each target customer service staff according to the content conformity, the customer responsiveness and the customer satisfaction of each electric chat corresponding to each target customer service staff;
and the customer service staff office quality evaluation module is used for obtaining the office quality evaluation coefficient corresponding to each target customer service staff according to the office behavior evaluation proportion index and the office content evaluation proportion index corresponding to each target customer service staff and carrying out corresponding processing according to the comparison and analysis result.
10. A data analysis storage medium based on intelligent office, characterized in that: the computer storage medium is burned with a computer program, and the computer program realizes the intelligent office based data analysis method of any one of the above claims 1-8 when running in the memory of the server.
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