CN116484108A - Staff post training course recommendation method and system - Google Patents
Staff post training course recommendation method and system Download PDFInfo
- Publication number
- CN116484108A CN116484108A CN202310550795.2A CN202310550795A CN116484108A CN 116484108 A CN116484108 A CN 116484108A CN 202310550795 A CN202310550795 A CN 202310550795A CN 116484108 A CN116484108 A CN 116484108A
- Authority
- CN
- China
- Prior art keywords
- target employee
- target
- post training
- training course
- course
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000012549 training Methods 0.000 title claims abstract description 246
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000006978 adaptation Effects 0.000 claims description 96
- 238000011156 evaluation Methods 0.000 claims description 42
- 238000004891 communication Methods 0.000 claims description 37
- 238000004458 analytical method Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 11
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 230000010354 integration Effects 0.000 claims description 9
- 238000011835 investigation Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 9
- 230000006872 improvement Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 12
- 230000009286 beneficial effect Effects 0.000 abstract description 5
- 230000008901 benefit Effects 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract description 5
- 230000001737 promoting effect Effects 0.000 abstract description 2
- 230000007547 defect Effects 0.000 description 8
- 238000010606 normalization Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000007792 addition Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036651 mood Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Educational Administration (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Technology (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of staff post training course recommendation, and particularly discloses a staff post training course recommendation method and system, wherein the method comprises the following steps: the invention can conduct more intelligent post training course recommendation according to individual differences of sales staff, avoids the phenomenon that the selected training courses cannot well fit with actual post conditions caused by subjective intention of the sales staff, thereby achieving the best post training effect, improving the matching degree between the sales staff and the post training courses actually watched, and enabling the sales staff to scientifically and reasonably improve professional skills and professional literacy of the sales staff according to the post training courses, and is beneficial to promoting professional development of the sales staff and improving the overall operation benefit of enterprises.
Description
Technical Field
The invention relates to the technical field of staff post training course recommendation, in particular to a staff post training course recommendation method and system.
Background
The internet technology provides more development space and development possibility for the progress of each enterprise of the society, because the enterprise is an organization with comprehensive characteristics, the interior of the enterprise is often constructed by a plurality of departments with different functional attributes, for the management level of the enterprise, how to effectively improve the professional skills and professional literacy of staff is a management hotspot of the enterprise, and post training courses become important implementation ways for promoting the professional development of staff, whether the staff is passive due to the management requirement of the enterprise or the staff is required to obtain the initiative of self development, and the post training courses are indispensable options.
The sales department is used as an important post of an enterprise, is positioned at the development front end of the whole enterprise, has important supporting positions in the enterprise closely related to the whole department, thus the post training of the sales department is particularly necessary, and the training courses of the sales post at present still have places needing to be perfected, and are specifically shown as follows: (1) Most of the existing sales training courses are selected only according to subjective decisions of sales department staff, the defect of strong subjectivity exists, and most of the sales staff only select the training courses according to subjective intention of the sales department staff, so that the selected training courses cannot well fit with actual post conditions of the sales department staff, and therefore an optimal post training effect cannot be achieved, professional development of the sales staff is not facilitated, and overall operation benefits of enterprises are not facilitated.
(2) The existing sales training course pushing lacks of carrying out targeted careful analysis on specific actual post conditions of sales staff, and cannot provide reliable data support basis for post training courses of follow-up actual pushing, so that more intelligent post training course recommendation cannot be carried out according to individual differences of the sales staff, and further matching degree between the sales staff and the post training courses actually watched is greatly reduced, so that the sales staff cannot scientifically and reasonably improve own professional skills and professional literacy according to the post training courses, and self professional development of the sales staff is caused to be negatively influenced.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a staff post training course recommending method and system, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: the first aspect of the invention provides a staff post training course recommendation method, which comprises the following steps: s1, acquiring statistics of target staff: and obtaining each employee belonging to the sales department of the appointed enterprise, marking the employee as a target employee, and further counting to obtain each target employee.
S2, training courses of the adaptation posts are arranged: and extracting all post training courses to which the sales department belongs from the course data cloud platform, and recording the post training courses as all adaptation post training courses.
S3, basic parameter investigation and analysis: basic parameters of each target employee are examined, wherein the basic parameters comprise basic learning conditions, position parameters, working content parameters and interest parameters, and then the basic learning conditions, position parameters, working content parameters and interest parameters of each target employee are respectively analyzed and calculated, and the fitting indexes of each adaptation post training course are respectively marked as mu im 、ε im 、φ im And theta im 。
S4, data integration processing: according to basic learning conditions, job position parameters, working content parameters and interest parameters of each target employee and the fitting indexes of each adaptation post training course, comprehensive fitting indexes of each target employee and each adaptation post training course are comprehensively calculated.
S5, intelligent recommendation of post training courses: according to the comprehensive fit indexes of each target employee and each adaptation post training course, further analyzing and screening to obtain the appointed post training course of each target employee, and recommending the appointed post training course to the corresponding target employee.
As a further method, the basic learning condition of each target employee and the fitting index of each adaptation post training course are calculated according to the following specific calculation process: examining basic learning conditions of each target employee, wherein the basic learning conditions comprise the number of historical learning courses and the learning progress of each historical learning course, and calculating the learning enthusiasm assessment index of each target employeeWherein M is i And D ij The number of historic learning courses respectively expressed as the i-th target employee and the j-th historic learning course of the i-th target employeeProgress of learning, phi 1 And phi is 2 The learning enthusiasm evaluation influence factors are respectively expressed as the set number of the history learning courses and the learning enthusiasm evaluation influence factors corresponding to the progress of the history learning courses, i is expressed as the number of each target employee, i=1, 2, the number of each target employee, k, k is expressed as the number of each history learning course, j is expressed as the number of each history learning course, j=1, 2, and n, n is expressed as the number of the history learning courses.
According to the learning progress of each history learning course of each target employee, extracting and obtaining each remaining learning section of each history learning course of each target employee, matching with the associated skill lifting direction corresponding to each section of each history learning course stored in the training database, obtaining the associated skill lifting direction corresponding to each remaining learning section of each target employee, integrating and marking the associated skill lifting direction as each appointed associated skill lifting direction of each target employee, matching with the course content ratio corresponding to each associated skill lifting direction of each adaptation post training course stored in the training database, and obtaining the course content ratio corresponding to each appointed associated skill lifting direction of each adaptation post training course of each target employee, wherein k is recorded as k im d Further calculating the matching index of the associated skill improving direction of each target employee and each adaptation post training courseWherein τ 1 "the matching influence factor to which the set associated skill improvement direction belongs, m is the number of each adaptation station training course, m=1, 2,.. v is expressed as the number of adaptation post training courses, d is expressed as the number of each assigned associated skill improvement direction, d=1, 2.
Extracting learning enthusiasm assessment index intervals corresponding to fitters belonging to each adaptation post training course stored in a training database, extracting intermediate values from the learning enthusiasm assessment index intervals, taking the intermediate values as reference matching learning enthusiasm assessment indexes of each adaptation post training course, and marking the intermediate values as delta C m 。
Comprehensively calculating basic learning conditions of all target staff and all target staffFitting index of adaptation post training courseWherein E is 1 And E is 2 The weight ratio of the set learning enthusiasm and the corresponding fit influence weight of the associated skill promotion direction are respectively expressed.
As a further method, the job position parameters of each target employee and the fitting indexes of each adaptation post training course are calculated according to the following steps: according to the position parameters of each target employee, wherein the position parameters comprise position attributes and time periods, and further the position parameters are matched with the proper time periods of the position personnel with the attributes of each adapted position training course stored in a training database, so as to obtain the proper time periods of each target employee with each adapted position training course, and accordingly, the fit indexes of the position parameters of each target employee and each adapted position training course are calculatedWherein T is i "representing the length of time, deltaT, of the job for the ith target employee im Representing the proper job duration delta of the ith target employee to which the mth adaptive post training course belongs 1 The fit evaluation factor corresponding to the set position parameter is expressed, and e is expressed as a natural constant.
As a further method, the working content parameters of each target employee are examined, and the specific process is as follows: and checking working content parameters of each target employee, wherein the working content parameters comprise the communication client quantity, the effective trading client quantity, the accumulated sales and the evaluation content of each communication client.
According to the communication client quantity, the effective bargaining client quantity and the accumulated sales amount of each target employee and the job duration of each target employee, the communication client quantity, the effective bargaining client quantity and the sales amount of the corresponding unit job duration of each target employee are obtained through average calculation and respectively recorded as X i ″、Y i "and Z i Further calculate basic evaluation index of work skills of each target employeeWherein ζ 1 、ζ 2 And zeta 3 The set communication client quantity, the effective trading client quantity and the sales amount are respectively expressed as the working skill assessment influence weight factors.
According to the evaluation content of each communication client of each target employee, keyword grabbing is further carried out on each communication client of each target employee, evaluation keywords of each communication client of each target employee are further obtained, evaluation keywords of each communication client of each target employee are obtained in an integrated mode, matching is carried out on each evaluation keyword of each communication client of each target employee and each positive-direction layer keyword set and each negative-direction layer keyword set stored in a course database, the number of positive-direction layer keywords and the number of negative-direction layer keywords of each communication client of each target employee are obtained and counted, and G is recorded respectively Positive i And G Negative i Further calculating skill literacy assessment indexes corresponding to the assessment contents of the communication clients of all target staffWherein a 'and b' respectively represent skill literacy assessment correction values corresponding to the set positive-going level keywords and negative-going level keywords.
As a further method, the working content parameters of each target employee and the fitting index of each adaptation post training course are calculated as follows: comprehensively calculating comprehensive evaluation indexes of working content parameters of all target staff according to the basic evaluation indexes of the working skills of all target staff and the skill literacy evaluation indexes corresponding to the evaluation contents of communication clients of all target staff
Extracting comprehensive evaluation index intervals corresponding to working content parameters of fitter belonging to each fitting post training course stored in a training database, extracting intermediate values from the comprehensive evaluation index intervals, taking the intermediate values as matching indexes corresponding to reference working content parameters of each fitting post training course, and marking the matching indexes as delta N m From this, calculate each target employeeFitting index of working content parameters and each adaptation post training course
As a further method, the calculating of the fitting index of interest parameters of each target employee and each adaptation post training course comprises the following specific calculating process: analyzing interest parameters of each target employee, wherein the interest parameters are history index contents of each time, performing entry grabbing on the history index contents of each time of each target employee to obtain each history index entry of each target employee, further matching each associated entry of each adaptation post training course stored in a training database, and accordingly, sorting to obtain the number of coincident index entries of each target employee and each adaptation post training course, wherein the number is recorded as CL im Accordingly, the fitting index of interest parameters of each target employee and each adaptation post training course is calculatedWherein->The corresponding fit influence factors are expressed as the set number of the coincident index entries.
As a further method, the comprehensive fitting index of each target employee and each adaptation post training courseWherein-> And->Respectively expressed as the set basic learning condition, position parameter, working content parameter and interest parameterA factor.
The second aspect of the present invention provides an employee post training course recommendation system, comprising: the target employee acquisition statistics module is used for acquiring each employee belonging to the sales department of the appointed enterprise, marking the employee as a target employee, and further counting to obtain each target employee.
The adaptation post training course rearranging module is used for extracting each post training course which the sales department belongs to from the course data cloud platform and recording the post training courses as each adaptation post training course.
The basic parameter investigation analysis module is used for investigating basic parameters of each target employee, wherein the basic parameters comprise basic learning conditions, position parameters, working content parameters and interest parameters, and further, the basic learning conditions, position parameters, working content parameters and interest parameters of each target employee and the fitting indexes of each adaptation post training course are respectively analyzed and calculated.
The data integration processing module is used for comprehensively calculating the comprehensive fit index of each target employee and each adaptation post training course according to the basic learning condition, the job position parameters, the working content parameters and the interest parameters of each target employee and the fit index of each adaptation post training course.
The post training course intelligent recommending module is used for analyzing and screening to obtain designated post training courses of all target staff according to comprehensive fit indexes of all target staff and all adaptation post training courses, and recommending the designated post training courses to corresponding target staff.
The course data cloud platform is used for storing all post training courses to which the sales department belongs.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: (1) The staff post training course recommending method and system can conduct intelligent post training course recommending according to sales staff, effectively overcomes the defect that most of the existing sales training courses are selected only according to subjective decisions of the sales department staff, overcomes the defect of strong subjectivity of the prior art, avoids the phenomenon that the selected training courses cannot be well matched with actual post conditions due to the fact that most of the sales staff only depend on subjective intention of the sales staff, achieves the best post training effect, is beneficial to professional development of the sales staff, and is beneficial to improving the overall operation benefit of enterprises.
(2) According to the invention, basic parameters of each target employee are inspected, the comprehensive fit index of each target employee and each adaptation post training course is finally calculated, the appointed post training course of each target employee is further analyzed and screened, and is recommended to the corresponding target employee, so that the defect that the existing sales training course is lack of carrying out targeted careful analysis on the specific actual post conditions of the sales employees is overcome, a reliable data support basis can be provided for the post training courses which are actually pushed later, further more intelligent post training course recommendation can be carried out according to the individuation difference of the sales employees, the matching degree between the sales employees and the post training courses which are actually watched is greatly improved, the professional skills and professional literacy of the sales employees can be scientifically and reasonably improved according to the post training courses, and negative effects on the professional development of the sales employees are avoided.
(3) According to the invention, through the number of the historic learning courses of each target employee and the learning progress of each historic learning course, the fitting index of the basic learning condition of each target employee and each adaptation post training course is further analyzed, the missing course learning content can be reasonably analyzed according to the historic learning condition of each target employee, so that a more stable supporting basis is provided for screening and recommending the subsequent appointed post training courses, and the recommendation result is more accurate and targeted.
(4) According to the invention, through analyzing the historical index content of each target employee, the fitting index of interest parameters of each target employee and each adaptation post training course is calculated, the course learning interest orientation of the target employee is used as the dimension of analysis consideration, so that the analysis layering is richer and more comprehensive, the recommended post training course can take the interests of the target employee as the guide, and the enthusiasm of the target employee for post training course learning is greatly improved.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of the method steps of the present invention.
Fig. 2 is a schematic diagram of system configuration connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a first aspect of the present invention provides a method for recommending employee post training courses, including: s1, acquiring statistics of target staff: and obtaining each employee belonging to the sales department of the appointed enterprise, marking the employee as a target employee, and further counting to obtain each target employee.
S2, training courses of the adaptation posts are arranged: and extracting all post training courses to which the sales department belongs from the course data cloud platform, and recording the post training courses as all adaptation post training courses.
S3, basic parameter investigation and analysis: basic parameters of each target employee are examined, wherein the basic parameters comprise basic learning conditions, position parameters, working content parameters and interest parameters, and then the basic learning conditions, position parameters, working content parameters and interest parameters of each target employee are respectively analyzed and calculated, and the fitting indexes of each adaptation post training course are respectively marked as mu im 、ε im 、φ im And theta im 。
Specifically, the basic learning condition of each target employee and each adaptation post training classThe process fitting index is calculated by the following steps: examining basic learning conditions of each target employee, wherein the basic learning conditions comprise the number of historical learning courses and the learning progress of each historical learning course, and calculating the learning enthusiasm assessment index of each target employeeWherein M is i And D ij The number of historic learning courses of the ith target employee and the learning progress of the jth historic learning course of the ith target employee, phi 1 And phi is 2 The learning enthusiasm evaluation influence factors are respectively expressed as the set number of the history learning courses and the learning enthusiasm evaluation influence factors corresponding to the progress of the history learning courses, i is expressed as the number of each target employee, i=1, 2, the number of each target employee, k, k is expressed as the number of each history learning course, j is expressed as the number of each history learning course, j=1, 2, and n, n is expressed as the number of the history learning courses.
It should be explained that, the learning progress of the history learning course of each target employee is specifically a ratio of the learned content of the history learning course to the total learning content of the corresponding history learning course.
According to the learning progress of each history learning course of each target employee, extracting and obtaining each remaining learning section of each history learning course of each target employee, matching with the associated skill lifting direction corresponding to each section of each history learning course stored in the training database, obtaining the associated skill lifting direction corresponding to each remaining learning section of each target employee, integrating and marking the associated skill lifting direction as each appointed associated skill lifting direction of each target employee, matching with the course content ratio corresponding to each associated skill lifting direction of each adaptation post training course stored in the training database, and obtaining the course content ratio corresponding to each appointed associated skill lifting direction of each adaptation post training course of each target employee, wherein k is recorded as k im d Further calculating the matching index of the associated skill improving direction of each target employee and each adaptation post training courseWherein τ 1 "the matching influence factor to which the set associated skill improvement direction belongs, m is the number of each adaptation station training course, m=1, 2,.. v is expressed as the number of adaptation post training courses, d is expressed as the number of each assigned associated skill improvement direction, d=1, 2.
As one example, the above-described associated skill improvement directions are specifically skill improvement directions that are divided according to the content of the lesson section, such as sales skills, sales thinking, sales moods, and customer maintenance.
Extracting learning enthusiasm assessment index intervals corresponding to fitters belonging to each adaptation post training course stored in a training database, extracting intermediate values from the learning enthusiasm assessment index intervals, taking the intermediate values as reference matching learning enthusiasm assessment indexes of each adaptation post training course, and marking the intermediate values as delta C m 。
Comprehensively calculating the basic learning condition of each target employee and the fitting index of each adaptation post training courseWherein E is 1 And E is 2 The weight ratio of the set learning enthusiasm and the corresponding fit influence weight of the associated skill promotion direction are respectively expressed.
In a specific embodiment, the invention analyzes the fitting index of the basic learning condition of each target employee and each adaptation post training course through the number of the history learning courses of each target employee and the learning progress of each history learning course, can reasonably analyze the missing course learning content according to the history learning condition of each target employee, further provides a more stable supporting basis for screening and recommending the subsequent appointed post training courses, and ensures that the recommendation result is more accurate and targeted.
Specifically, the calculation process of the fitting index of the position parameters of each target employee and each adaptation post training course is as follows: according to the position parameters of each target employee, wherein the position parameters comprise position attributes and time length,matching the proper job duration of each attribute job personnel of each adaptation post training course stored in the training database to obtain the proper job duration of each target employee of each adaptation post training course, and calculating the fit index of the job parameters of each target employee and each adaptation post training course according to the proper job duration of each target employee of each adaptation post training courseWherein T is i "representing the length of time, deltaT, of the job for the ith target employee im Representing the proper job duration delta of the ith target employee to which the mth adaptive post training course belongs 1 The fit evaluation factor corresponding to the set position parameter is expressed, and e is expressed as a natural constant.
It should be noted that the job attributes are classified by the sales job level, and include sales manager, sales consultant, sales professional, etc.
Specifically, the working content parameters of each target employee are examined, and the specific process is as follows: and checking working content parameters of each target employee, wherein the working content parameters comprise the communication client quantity, the effective trading client quantity, the accumulated sales and the evaluation content of each communication client.
According to the communication client quantity, the effective bargaining client quantity and the accumulated sales amount of each target employee and the job duration of each target employee, the communication client quantity, the effective bargaining client quantity and the sales amount of the corresponding unit job duration of each target employee are obtained through average calculation and respectively recorded as X i ″、Y i "and Z i Further calculate basic evaluation index of work skills of each target employeeWherein ζ 1 、ζ 2 And zeta 3 The set communication client quantity, the effective trading client quantity and the sales amount are respectively expressed as the working skill assessment influence weight factors.
According to the evaluation content of each communication client of each target employee, further capturing keywords to obtainEvaluation keywords of communication clients of all target staff are obtained, the evaluation keywords of the communication clients of all target staff are obtained, the evaluation keywords are matched with a keyword set of all positive layers and a keyword set of all negative layers stored in a course database, the number of the keywords of the positive layers and the number of the keywords of the negative layers of the communication clients of all target staff are obtained and counted, and the number of the keywords of the positive layers and the number of the keywords of the negative layers are recorded as G respectively Positive i And G Negative i Further calculating skill literacy assessment indexes corresponding to the assessment contents of the communication clients of all target staffWherein a 'and b' respectively represent skill literacy assessment correction values corresponding to the set positive-going level keywords and negative-going level keywords.
Specifically, the working content parameters of each target employee and the fitting indexes of each adaptation post training course are calculated according to the following steps: comprehensively calculating comprehensive evaluation indexes of working content parameters of all target staff according to the basic evaluation indexes of the working skills of all target staff and the skill literacy evaluation indexes corresponding to the evaluation contents of communication clients of all target staff
Extracting comprehensive evaluation index intervals corresponding to working content parameters of fitter belonging to each fitting post training course stored in a training database, extracting intermediate values from the comprehensive evaluation index intervals, taking the intermediate values as matching indexes corresponding to reference working content parameters of each fitting post training course, and marking the matching indexes as delta N m Accordingly, the fitting index of the working content parameters of each target employee and each adaptation post training course is calculated
Specifically, the calculating the fitting index of interest parameters of each target employee and each adaptation post training course includes the following specific calculating process: analyzing interest parameters of each target employeeThe interest parameters in the training database are history index contents of each time, the history index contents of each target employee are subjected to entry grabbing to obtain each history index entry of each target employee, and then the history index entries are matched with each associated entry of each adaptation post training course stored in the training database, so that the number of overlapping index entries of each target employee and each adaptation post training course is obtained by normalization, and the number is marked as CL im Accordingly, the fitting index of interest parameters of each target employee and each adaptation post training course is calculatedWherein->The corresponding fit influence factors are expressed as the set number of the coincident index entries.
In a specific embodiment, by analyzing the historical index content of each target employee, the invention calculates the fit index of interest parameters of each target employee and each adaptation post training course, realizes taking the course learning interest orientation of the target employee as the dimension of analysis consideration, and ensures that the analysis is richer and more comprehensive, thereby ensuring that the recommended post training course can take the interests of the target employee as the guide, and greatly improving the enthusiasm of the target employee for post training course learning.
S4, data integration processing: according to basic learning conditions, job position parameters, working content parameters and interest parameters of each target employee and the fitting indexes of each adaptation post training course, comprehensive fitting indexes of each target employee and each adaptation post training course are comprehensively calculated.
Specifically, the comprehensive fitting index of each target employee and each adaptation post training courseWherein->And->Respectively expressed as the set basic learning condition, position parameter, working content parameter and interest parameter.
S5, intelligent recommendation of post training courses: according to the comprehensive fit indexes of each target employee and each adaptation post training course, further analyzing and screening to obtain the appointed post training course of each target employee, and recommending the appointed post training course to the corresponding target employee.
It should be explained that the above screening obtains the specified post training course of each target employee, and the specific process is as follows: sequencing the comprehensive fit indexes of each target employee and each fit training course in sequence from big to small, extracting the fit training course of the first position of the comprehensive fit index sequence, and marking the fit training course as the specified position training course of each target employee.
In a specific embodiment, basic parameters of each target employee are inspected, the comprehensive fit index of each target employee and each adaptation post training course is finally calculated, the designated post training course of each target employee is further analyzed and screened, and is recommended to the corresponding target employee, the defect that the existing sales training course is lack of carrying out targeted careful analysis on specific actual post conditions of the sales employees is overcome, a reliable data support basis can be provided for the post training courses which are actually pushed, further more intelligent post training course recommendation can be carried out according to individual differences of the sales employees, the matching degree between the sales employees and the post training courses which are actually watched is greatly improved, the professional skills and professional literacy of the sales employees can be scientifically and reasonably improved according to the post training courses, and negative influences on the self development of the sales employees are avoided.
Referring to fig. 2, a second aspect of the present invention provides an employee post training course recommendation system, comprising: the system comprises a target employee acquisition statistics module, an adaptive post training course normalization module, a basic parameter investigation analysis module, a data integration processing module, a post training course intelligent recommendation module, a course data cloud platform and a training database.
The course data cloud platform is connected with the adaptive post training course normalization module, the target staff acquisition statistics module, the adaptive post training course normalization module and the training database are connected with the basic parameter investigation analysis module, the data integration processing module is connected with the basic parameter investigation analysis module, and the post training course intelligent recommendation module is connected with the data integration processing module.
The target employee acquisition and statistics module is used for acquiring each employee belonging to the sales department of the appointed enterprise, marking the employee as a target employee, and further counting to obtain each target employee.
The adaptation post training course rearranging module is used for extracting each post training course which the sales department belongs to from the course data cloud platform and recording the training courses as each adaptation post training course.
The basic parameter investigation analysis module is used for investigating basic parameters of each target employee, wherein the basic parameters comprise basic learning conditions, position parameters, working content parameters and interest parameters, and further, the basic learning conditions, position parameters, working content parameters and interest parameters of each target employee and the fitting indexes of each adaptation post training course are respectively analyzed and calculated.
The data integration processing module is used for comprehensively calculating the comprehensive fit index of each target employee and each adaptation post training course according to the basic learning condition, the job position parameters, the working content parameters and the interest parameters of each target employee and the fit index of each adaptation post training course.
The post training course intelligent recommending module is used for analyzing and screening to obtain designated post training courses of all target staff according to comprehensive fit indexes of all target staff and all adaptation post training courses, and recommending the designated post training courses to corresponding target staff.
The course data cloud platform is used for storing all post training courses to which the sales department belongs.
It should be explained that, the system of the invention further comprises a training database, the training database is used for storing the relevant skill lifting directions corresponding to the chapters to which various courses belong, storing the course content proportion of the relevant skill lifting directions corresponding to the adaptive post training courses, storing the learning enthusiasm assessment index interval and the comprehensive assessment index interval to which the working content parameters belong, storing the appropriate job duration of the attribute position personnel to which the adaptive post training courses belong, storing the keyword sets to which the positive layers belong and the keyword sets to which the negative layers belong, and storing the relevant vocabulary items to which the adaptive post training courses belong.
In a specific embodiment, the staff post training course recommending method and system can conduct intelligent post training course recommending according to sales staff, effectively make up for the defect that the existing sales training courses are selected only according to subjective decisions of the sales staff, overcome the defect of strong subjectivity in the prior art, avoid the phenomenon that the selected training courses cannot be well matched with the actual post conditions of the sales staff only according to subjective intention of the sales staff, achieve the best post training effect, and are beneficial to professional development of the sales staff and overall operation benefit of enterprises.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (8)
1. The staff post training course recommending method is characterized by comprising the following steps of:
s1, acquiring statistics of target staff: each employee belonging to the sales department of the appointed enterprise is obtained, marked as a target employee, and then each target employee is obtained through statistics;
s2, training courses of the adaptation posts are arranged: extracting each post training course to which the sales department belongs from the course data cloud platform, and recording the training courses as each adaptation post training course;
s3, basic parameter investigation and analysis: basic parameters of each target employee are examined, wherein the basic parameters comprise basic learning conditions, position parameters, working content parameters and interest parameters, and then the basic learning conditions, position parameters, working content parameters and interest parameters of each target employee are respectively analyzed and calculated, and the fitting indexes of each adaptation post training course are respectively marked as mu im 、ε im 、φ im And
s4, data integration processing: according to basic learning conditions, job position parameters, working content parameters and interest parameters of each target employee and the fitting indexes of each adaptation post training course, comprehensive fitting indexes of each target employee and each adaptation post training course are comprehensively calculated;
s5, intelligent recommendation of post training courses: according to the comprehensive fit indexes of each target employee and each adaptation post training course, further analyzing and screening to obtain the appointed post training course of each target employee, and recommending the appointed post training course to the corresponding target employee.
2. The employee post training course recommendation method of claim 1 wherein: the basic learning condition of each target employee and the fitting index of each adaptation post training course are calculated according to the following steps:
examining basic learning conditions of each target employee, wherein the basic learning conditions comprise the number of historical learning courses and the learning progress of each historical learning course, and calculating the learning enthusiasm assessment index of each target employeeWherein M is i And D ij The number of historic learning courses respectively expressed as the ith target employee and the ith target employeeLearning progress, phi of j history learning courses 1 And phi is 2 The learning enthusiasm assessment influence factors are respectively expressed as the set number of the history learning courses and the learning enthusiasm assessment influence factors corresponding to the progress of the history learning courses, i is expressed as the number of each target employee, i=1, 2, the number of k, k is expressed as the number of target employees, j is expressed as the number of each history learning course, j=1, 2, the number of n, n is expressed as the number of the history learning courses;
according to the learning progress of each history learning course of each target employee, extracting and obtaining each remaining learning section of each history learning course of each target employee, matching with the associated skill lifting direction corresponding to each section of each history learning course stored in the training database, obtaining the associated skill lifting direction corresponding to each remaining learning section of each target employee, integrating and marking the associated skill lifting direction as each appointed associated skill lifting direction of each target employee, matching with the course content ratio corresponding to each associated skill lifting direction of each adaptation post training course stored in the training database, and obtaining the course content ratio corresponding to each appointed associated skill lifting direction of each adaptation post training course of each target employee, wherein k is recorded as k im d Further calculating the matching index of the associated skill improving direction of each target employee and each adaptation post training courseWherein τ 1 "the matching influence factor to which the set associated skill improvement direction belongs, m is the number of each adaptation station training course, m=1, 2,.. v is expressed as the number of adaptation post training courses, d is expressed as the number of each specified associated skill improvement direction, d=1, 2.
Extracting learning enthusiasm assessment index intervals corresponding to fitters belonging to each adaptation post training course stored in a training database, extracting intermediate values from the learning enthusiasm assessment index intervals, taking the intermediate values as reference matching learning enthusiasm assessment indexes of each adaptation post training course, and marking the intermediate values as delta C m ；
Comprehensive calculation of each orderBasic learning condition of staff and fitting index of training courses of all fitting postsWherein E is 1 And E is 2 The weight ratio of the set learning enthusiasm and the corresponding fit influence weight of the associated skill promotion direction are respectively expressed.
3. The employee post training course recommendation method of claim 1 wherein: the calculation process of the fitting index of the position parameters of each target employee and each adaptation post training course is as follows:
according to the position parameters of each target employee, wherein the position parameters comprise position attributes and time periods, and further the position parameters are matched with the proper time periods of the position personnel with the attributes of each adapted position training course stored in a training database, so as to obtain the proper time periods of each target employee with each adapted position training course, and accordingly, the fit indexes of the position parameters of each target employee and each adapted position training course are calculatedWherein T is i "representing the length of time, deltaT, of the job for the ith target employee im Representing the proper job duration delta of the ith target employee to which the mth adaptive post training course belongs 1 The fit evaluation factor corresponding to the set position parameter is expressed, and e is expressed as a natural constant.
4. The employee post training course recommendation method of claim 1 wherein: the working content parameters of each target employee are examined, and the specific process is as follows:
the working content parameters of all target staff are checked, wherein the working content parameters comprise the communication client quantity, the effective trading client quantity, the accumulated sales and the evaluation content of all communication clients;
according to the communication client quantity of each target staff, effectively transacting clientsThe amount and the accumulated sales and the time length of each target employee are calculated by means to obtain the communication client amount, the effective transaction client amount and the sales of the time length of each target employee corresponding to the unit time length, which are respectively marked as X i ″、Y i "and Z i Further calculate basic evaluation index of work skills of each target employeeWherein ζ 1 、ζ 2 And zeta 3 Respectively representing the set communication client quantity, the effective transaction client quantity and the working skill assessment influence weight factors of sales;
according to the evaluation content of each communication client of each target employee, keyword grabbing is further carried out on each communication client of each target employee, evaluation keywords of each communication client of each target employee are further obtained, evaluation keywords of each communication client of each target employee are obtained in an integrated mode, matching is carried out on each evaluation keyword of each communication client of each target employee and each positive-direction layer keyword set and each negative-direction layer keyword set stored in a course database, the number of positive-direction layer keywords and the number of negative-direction layer keywords of each communication client of each target employee are obtained and counted, and G is recorded respectively Positive i And G Negative i Further calculating skill literacy assessment indexes corresponding to the assessment contents of the communication clients of all target staffWherein a 'and b' respectively represent skill literacy assessment correction values corresponding to the set positive-going level keywords and negative-going level keywords.
5. The employee position training course recommendation method of claim 4 wherein: the working content parameters of each target employee and the fitting indexes of each adaptation post training course are calculated according to the following steps: comprehensively calculating the working content parameters of each target staff according to the basic working skill evaluation index of each target staff and the skill literacy evaluation index corresponding to the evaluation content of the communication client of each target staffComprehensive evaluation index
Extracting comprehensive evaluation index intervals corresponding to working content parameters of fitter belonging to each fitting post training course stored in a training database, extracting intermediate values from the comprehensive evaluation index intervals, taking the intermediate values as matching indexes corresponding to reference working content parameters of each fitting post training course, and marking the matching indexes as delta N m Accordingly, the fitting index of the working content parameters of each target employee and each adaptation post training course is calculated
6. The employee post training course recommendation method of claim 1 wherein: the method comprises the following steps of calculating the fitting index of interest parameters of each target employee and each adaptation post training course, wherein the specific calculation process is as follows:
analyzing interest parameters of each target employee, wherein the interest parameters are history index contents of each time, performing entry grabbing on the history index contents of each time of each target employee to obtain each history index entry of each target employee, further matching each associated entry of each adaptation post training course stored in a training database, and accordingly, sorting to obtain the number of coincident index entries of each target employee and each adaptation post training course, wherein the number is recorded as CL im Accordingly, the fitting index of interest parameters of each target employee and each adaptation post training course is calculatedWherein->The corresponding fit influence factors are expressed as the set number of the coincident index entries.
7. According to claim 1The staff post training course recommending method is characterized by comprising the following steps of: the comprehensive fitting index of each target employee and each adaptation post training courseWherein->And->Respectively expressed as the set basic learning condition, position parameter, working content parameter and interest parameter.
8. A staff post training course recommendation system is characterized in that: comprising the following steps:
the target employee acquisition statistics module is used for acquiring each employee belonging to the sales department of the appointed enterprise, marking the employee as a target employee, and further carrying out statistics to obtain each target employee;
the adaptation post training course rearranging module is used for extracting each post training course which the sales department belongs to from the course data cloud platform and recording the post training courses as each adaptation post training course;
the basic parameter investigation analysis module is used for investigating basic parameters of each target employee, wherein the basic parameters comprise basic learning conditions, position parameters, working content parameters and interest parameters, and further, the basic learning conditions, position parameters, working content parameters and interest parameters of each target employee and the fitting indexes of each adaptation post training course are respectively analyzed and calculated;
the data integration processing module is used for comprehensively calculating the comprehensive fit index of each target employee and each adaptation post training course according to the basic learning condition, the job position parameters, the working content parameters and the interest parameters of each target employee and the fit index of each adaptation post training course;
the post training course intelligent recommending module is used for analyzing and screening to obtain designated post training courses of all target staff according to the comprehensive fit indexes of all target staff and all adaptation post training courses, and recommending the designated post training courses to the corresponding target staff;
the course data cloud platform is used for storing all post training courses to which the sales department belongs.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310550795.2A CN116484108A (en) | 2023-05-16 | 2023-05-16 | Staff post training course recommendation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310550795.2A CN116484108A (en) | 2023-05-16 | 2023-05-16 | Staff post training course recommendation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116484108A true CN116484108A (en) | 2023-07-25 |
Family
ID=87213901
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310550795.2A Withdrawn CN116484108A (en) | 2023-05-16 | 2023-05-16 | Staff post training course recommendation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116484108A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957523A (en) * | 2023-09-18 | 2023-10-27 | 山邮数字科技(山东)有限公司 | Enterprise management system and method based on big data analysis |
| CN117114633A (en) * | 2023-09-08 | 2023-11-24 | 杭州今元标矩科技有限公司 | Digital human resource management method, device, electronic equipment and medium |
-
2023
- 2023-05-16 CN CN202310550795.2A patent/CN116484108A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117114633A (en) * | 2023-09-08 | 2023-11-24 | 杭州今元标矩科技有限公司 | Digital human resource management method, device, electronic equipment and medium |
| CN117114633B (en) * | 2023-09-08 | 2024-03-26 | 杭州今元标矩科技有限公司 | Digital human resource management method, device, electronic equipment and medium |
| CN116957523A (en) * | 2023-09-18 | 2023-10-27 | 山邮数字科技(山东)有限公司 | Enterprise management system and method based on big data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116484108A (en) | Staff post training course recommendation method and system | |
| CN107403285A (en) | Operator's marketing channel Valuation Method based on optimal segmentation | |
| CN113256169B (en) | Talent storage and management system for recruitment platform based on Internet | |
| Tarigan | An evaluation of the relationship between alignment of strategic priorities and manufacturing performance | |
| CN108830492B (en) | Method for determining spot-check merchants based on big data | |
| Chojnacka-Komorowska et al. | Improving the quality control process using the PDCA cycle | |
| CN116307928A (en) | Object supervision management system | |
| Sillanpää et al. | The literature review of supply chain performance measurement in the manufacturing industry | |
| CN113918707A (en) | Policy convergence and enterprise image matching recommendation method | |
| CN113781006A (en) | Human resource management system and method based on big data | |
| Yudiatmaja | How does public service motivation contribute to service orientation? Testing mediation models | |
| CN111459805A (en) | Method and system for managing talents in outsourcing test | |
| CN114139065A (en) | Talent screening and recommending method and system based on big data and readable storage medium | |
| CN113554316A (en) | Staff training system based on Internet of things | |
| Kaganski et al. | Selecting key performance indicators with support of enterprise analyze model | |
| CN116205613B (en) | On-line monitoring method and system for automatic tour inspection system for traveling | |
| Kalinouskaya | Selection of training programs for textile industry personnel using artificial intelligence | |
| Coraggio et al. | JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality | |
| CN113706178B (en) | Supervision method for product inspection process | |
| CN115130872A (en) | Recruitment risk assessment system based on deep learning judgment | |
| CN111565121A (en) | Method and device for evaluating IT (information technology) support degree by personnel and technology | |
| Tabaroki et al. | Identification and prioritisation of the critical success factors for research project-based organisations using fuzzy analytic hierarchy process | |
| CN117035692B (en) | Intelligent assessment management system and method based on multidimensional data | |
| CN116596255B (en) | Big data-based user consultation ordering method | |
| CN111091153B (en) | Soft measurement method for bulk of paper sheet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20230725 |