CN113240284A - Business staff management method based on big data - Google Patents
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Abstract
The invention relates to the technical field related to express logistics, in particular to a large data-based waiter management method. Specifically, the method comprises the following steps: grouping and abstracting labels of the salesmen based on basic data and business data of the salesmen and related subjects; by a big data platform flink component technology, real-time calculation and analysis are carried out on waybill basic data, waybill route real-time data, real-time GIS data of a salesman, label data of the salesman and the like, the salesman dispatch flow is comprehensively optimized, the dispatch route is optimized, the working progress of the salesman today is prompted, whether the salesman has singlets which are missed to be dispatched in the current place or not and whether express mails can be collected or not is prompted, and the existing customers and valuable potential customers which need to be maintained in the dispatch range are marked.
Description
Technical Field
The invention relates to the technical field related to express logistics, in particular to a large data-based waiter management method.
Background
At present, the express industry is developed vigorously, and the management mode of the express industry is also turning from extensive management to fine management mode. The cost of the service staff is always a high-proportion component in the industry, and cost reduction, efficiency improvement and service expansion all depend on excellent service staff, so that the service staff is very necessary to be subjected to fine management and help to improve the energy efficiency through technical means.
The prior art mainly has the following defects: the service of the express industry is greatly influenced by the capability of a salesman. When the ability of the salesperson is poor, the service business of the express delivery industry is adversely affected.
Disclosure of Invention
In view of this, a business person management method based on big data is provided to solve the problem that the business person cannot be managed in a refined manner in the related art.
The invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a large data-based administrator management method, including:
acquiring basic data and business data of a salesman;
integrating and analyzing the basic data and the service data of the salesman to obtain the label data and the portrait of the salesman; calculating the value of the salesman based on the salesman figure to obtain various value labels of the salesman;
acquiring basic data of the waybill, real-time data of a waybill route and real-time GIS data of a salesman;
carrying out real-time calculation and analysis on the waybill basic data, the waybill route real-time data, the real-time GIS data of the salesman and the label data of the salesman by a big data platform flink component technology to obtain an analysis result, and outputting dispatch flow reminding, service staff working progress reminding and valuable client reminding according to the analysis result so as to facilitate the salesman to carry out distribution according to the reminding;
creating a characteristic project through various score labels and label data of the salesman, and performing weighted calculation around multiple dimensions through an algorithm model based on xgboost to obtain a comprehensive score of the salesman; and based on the comprehensive ranking, evaluating and rating the business capability of the business staff and generating a monthly business staff work report.
Optionally, the integrating and analyzing the basic data and the business data of the salesman to obtain the label data of the salesman and the salesman portrait includes:
calculating through spark component technology of a big data platform based on the basic data and the business data of the salesman to obtain a real-fact label;
based on the fact label, calculating through a technical framework based on a presto engine, a spark engine and an Hbase database, a preset analysis model, preset parameter transmission parameters and selection conditions to obtain a model label;
predicting through an xgboost algorithm, a regression and lifting tree algorithm and a corresponding risk prediction model based on the model label to obtain a prediction label;
based on the fact tag and the model tag, a waiter portrait is obtained.
Optionally, the fact tag includes: the system comprises a salesman business volume label, a salesman customer number label, a salesman complaint condition label and a salesman basic attribute label;
the model tag includes: the service quality KPI label, the service staff aging label and the service staff operation index grading label are marked;
the predictive tag includes: the system comprises a fine early warning label, a predicted sign-in time label, a complaint risk degree ranking label and a tomorrow dispatch prediction label.
Optionally, the method further includes:
acquiring adjustment data;
and adjusting the preset analysis model based on the adjustment data, and adjusting the preset parameter transmission parameters and the selection conditions.
Optionally, the method further includes:
and pushing the work report to a corresponding salesman.
By adopting the technical scheme, the basic data and the service data of the salesman are acquired, and are integrated and analyzed to obtain the label data of the salesman; accurately, comprehensively and stereoscopically depicting a salesman; then acquiring basic data of the waybill, real-time data of a waybill route and real-time GIS data of a salesman; by a big data platform flink component technology, real-time calculation and analysis are carried out on waybill basic data, waybill route real-time data, real-time GIS data of a salesman and label data of the salesman, a salesman dispatch flow is comprehensively optimized, a dispatch route is optimized, the working progress of the salesman today is prompted, whether the salesman has singlets which are missed to be dispatched in the current place or not is reminded, whether express mails can be collected or not is reminded, existing clients needing to be maintained and valuable potential clients in a dispatch range are marked, and help is accurately, timely and minimally provided for the salesman; establishing a characteristic project through basic data and service data of a salesman, and performing weighted calculation around multiple dimensions through an algorithm model based on xgboost to obtain a salesman comprehensive score; based on the comprehensive ranking, the business capability of the business staff is evaluated and rated, and a monthly business staff working report is generated; and measuring and evaluating the comprehensive capacity of the salesman in a comprehensive score ranking mode, and finely managing the salesman in the mode. According to the scheme, the management method for comprehensive ability evaluation and energy efficiency improvement of the salesman based on big data calculation can assist the salesman in service, reduce nodes which need the service to be judged by the salesman in the service process, and enable the service provided by the salesman to be more efficient and high-quality.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a management method for comprehensive ability evaluation and energy efficiency improvement of a salesman based on big data computing according to an embodiment of the present invention;
fig. 2 is a structural diagram of a management method for comprehensive ability evaluation and energy efficiency improvement of a service provider based on big data computing according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
First, an application scenario of the embodiment of the present invention is explained, and at present, an express industry is developing vigorously, and a management mode of the express industry is also shifting from a rough management mode to a fine management mode. The cost of the service staff is always a high-proportion component in the industry, and cost reduction, efficiency improvement and service expansion all depend on excellent service staff, so that the service staff is very necessary to be subjected to fine management and help to improve the energy efficiency through technical means. The prior art mainly has the following defects: the service of the express industry is greatly influenced by the capability of a salesman. When the ability of the salesperson is poor, the service business of the express delivery industry is adversely affected.
Examples
Fig. 1 is a flowchart of a management method for comprehensive ability evaluation and energy efficiency improvement of a service provider based on big data computing according to an embodiment of the present invention. Referring to fig. 1, the method may specifically include the following steps:
s101, acquiring basic data and business data of a salesman;
s102, integrating and analyzing basic data and business data of the salesman to obtain label data of the salesman and a salesman portrait; calculating the value of the salesman based on the salesman figure to obtain various value labels of the salesman;
specifically, based on the basic data and the business data of the salesman, calculating through spark component technology of a big data platform to obtain a real-fact label;
based on the fact label, calculating through a technical framework based on a presto engine, a spark engine and an Hbase database, a preset analysis model, preset parameter transmission parameters and selection conditions to obtain a model label; further, the scheme provided by the application further comprises: acquiring adjustment data; and adjusting the preset analysis model based on the adjustment data, and adjusting the preset parameter transmission parameters and the selection conditions.
Predicting through an xgboost algorithm, a regression and lifting tree algorithm and a corresponding risk prediction model based on the model label to obtain a prediction label;
based on the fact tag and the model tag, a waiter portrait is obtained.
The fact tag includes: the system comprises a salesman business volume label, a salesman customer number label, a salesman complaint condition label and a salesman basic attribute label;
the model tag includes: the service quality KPI label, the service staff aging label and the service staff operation index grading label are marked;
the predictive tag includes: the system comprises a fine early warning label, a predicted sign-in time label, a complaint risk degree ranking label and a tomorrow dispatch prediction label.
S103, acquiring basic data of the waybill, real-time data of a waybill route and real-time GIS data of a salesman;
s104, calculating and analyzing the basic data of the waybill, the real-time data of the waybill route, the real-time GIS data of the salesman and the label data of the salesman in real time through a large data platform flink assembly technology to obtain an analysis result, and outputting a dispatch flow prompt, a salesman working progress prompt and a valuable client prompt according to the analysis result so as to facilitate the salesman to carry out distribution according to the prompt;
s105, creating a characteristic project through various score labels and label data of the salesmen, and performing weighted calculation around multiple dimensions through an algorithm model based on xgboost to obtain comprehensive scores of the salesmen; and based on the comprehensive ranking, evaluating and rating the business capability of the business staff and generating a monthly business staff work report.
According to the scheme, by the aid of the management method for comprehensive ability evaluation and energy efficiency improvement of the salesman based on big data calculation, fine management is performed on the salesman in the mode, the salesman can be assisted in service, nodes which need to be judged by the salesman in the service process are reduced, and accordingly service provided by the salesman is more efficient and high in quality.
Further, the scheme provided by the application further comprises: and pushing the work report to a corresponding salesman. Therefore, the service staff can know the advantages and the disadvantages of the service staff in the work, help the service staff to overcome the disadvantages and improve the efficiency.
Further, referring to fig. 2, the data warehouse classical modeling theory based on dimension modeling according to the scheme provided by the application models business basic data according to different subjects, calculates and forms a business member data mart including subject data of business member basis, network point basis, business member business behavior, business member business volume, business member client and fan, business member aging, business member operation quality and preference, business member service quality, business member finance and the like through spark component, then clearly designs caliber and abstracts into corresponding basic labels, enables users to immediately inquire and construct derivative index labels through an image analysis system based on presto engine + spark + Hbase + AI based on the basic labels, thereby flexibly, accurately, comprehensively and stereoscopically depicting a business member, thereby more accurately and objectively judging the value of the business member and analyzing the advantages and disadvantages thereof, technical means are adopted to help the energy efficiency of the device to be improved.
The technical implementation scheme of the core function is as follows:
the method comprises the steps of grouping and abstracting labels of operators based on basic data and service data of operators and related subjects, calculating by using spark component technology of a big data platform to obtain fact labels, enabling users to flexibly select analysis models through a presto engine + spark + Hbase-based technical framework, setting different parameter transmission parameters and selection conditions, and calculating to obtain model labels. And then obtaining a predicted label result through algorithms such as regression and lifting tree and the like through a corresponding risk prediction model based on the xgboost. Specifically, firstly, acquiring off-line data of a big data platform, extracting relevant data of a salesman in the off-line data, modeling the relevant data of the salesman, and calculating by using spark component technology of the big data platform based on basic data and business data of the salesman to obtain a real-fact label; based on the fact label, calculating through a technical framework based on a presto engine, a spark engine and an Hbase database, a preset analysis model, preset parameter transmission parameters and selection conditions to obtain a model label; predicting through an xgboost algorithm, a regression and lifting tree algorithm and a corresponding risk prediction model based on the model label to obtain a prediction label; based on the fact tag and the model tag, a waiter portrait is obtained. And calculating the values of the salesmen based on the salesmen figure to obtain various value labels of the salesmen.
For the case of label grouping, a brief example follows: the actual affair label comprises the service volume of the service staff, the number of customers of the service staff, the complaint condition of the service staff, the basic attribute of the service staff and the like; model labels, namely KPI scores, aging scores, operation index scores and the like; and prediction labels comprise fine early warning, predicted sign-in time, complaint risk degree ranking, tomorrow dispatch prediction and the like.
By a big data platform flink component technology, real-time calculation and analysis are carried out on waybill basic data, waybill route real-time data, real-time GIS data of a salesman, label data of the salesman and the like, a salesman dispatch flow is comprehensively optimized, a dispatch route is optimized, the working progress of today is prompted, whether the salesman has singlets which are missed to be dispatched in the current place or can take express mails or not is reminded, the overall dispatch efficiency is improved, the existing clients and valuable potential clients which need to be maintained in the dispatch range are marked, the salesman is helped to maintain business relations, bind protocol clients and improve performance. Specifically, real-time data of the big data platform is obtained, real-time calculation and analysis are carried out, dispatch optimization strategies, dispatch optimization and risk reminding are completed, and messages of the dispatch optimization strategies, the dispatch optimization and the risk reminding are pushed to an operator.
And finally, obtaining an objective comprehensive score of the salesman by weighting calculation around multiple dimensions through an algorithm model based on xgboost, and evaluating and rating the service capability of the salesman based on the ranking of the comprehensive score and the threshold setting of normal distribution. And monthly service staff working reports are formed in parallel, so that the service staff can know the advantages and the disadvantages of the service staff in the work, help the service staff to overcome the disadvantages and improve the efficiency. Specifically, an analysis report is obtained based on real-time data of a big data platform, a salesman portrait and salesman score information processed by a comprehensive capacity evaluation model; and calculating the values of the salesmen based on the salesmen figure, inputting the information of each value label of the salesmen into the comprehensive capability evaluation model to obtain the comprehensive grading result of the salesmen, and sending dispatch optimization, risk reminding, an analysis report and the comprehensive grading result to a manager. So that the manager can perform further fine management on the business.
In conclusion, the invention has the advantages that by integrating various novel mature technologies, utilizing a big data technology and an AI model, and by the design and construction of layered labels, the invention depends on the flexibility based on the user on-site query, comprehensively and timely depicts the information of a salesman and the working form of the salesman at the current stage, and depends on a more objective scoring model and mode, thereby avoiding the evaluation deviation caused by the conditions of holidays, promotion of E-commerce and the like, further more stably evaluating the comprehensive service capability of the salesman at the current stage, identifying good salesman, and improving the whole service energy efficiency.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (5)
1. A big data-based business administrator management method is characterized by comprising the following steps:
acquiring basic data and business data of a salesman;
integrating and analyzing basic data and service data of the salesman to obtain label data and a salesman portrait of the salesman; calculating the value of the salesman based on the salesman figure to obtain various value labels of the salesman;
acquiring basic data of the waybill, real-time data of a waybill route and real-time GIS data of a salesman;
carrying out real-time calculation and analysis on the waybill basic data, the waybill route real-time data, the real-time GIS data of the salesman and the label data of the salesman by a big data platform flink component technology to obtain an analysis result, and outputting dispatch flow reminding, service staff working progress reminding and valuable client reminding according to the analysis result so as to facilitate the salesman to carry out distribution according to the reminding;
creating a characteristic project through various score labels and label data of the salesman, and performing weighted calculation around multiple dimensions through an algorithm model based on xgboost to obtain a comprehensive score of the salesman; and based on the comprehensive ranking, evaluating and rating the business capability of the business staff and generating a monthly business staff work report.
2. The business person management method based on big data as claimed in claim 1, wherein said integrating and analyzing the basic data and business data of the business person to obtain the label data and the picture of the business person comprises:
calculating through spark component technology of a big data platform based on the basic data and the business data of the salesman to obtain a real-fact label;
based on the fact label, calculating through a technical framework based on a presto engine, a spark engine and an Hbase database, a preset analysis model, preset parameter transmission parameters and selection conditions to obtain a model label;
predicting through an xgboost algorithm, a regression and lifting tree algorithm and a corresponding risk prediction model based on the model label to obtain a prediction label;
based on the fact tag and the model tag, a waiter portrait is obtained.
3. The big-data based salesman management method according to claim 2, wherein said fact tag comprises: the system comprises a salesman business volume label, a salesman customer number label, a salesman complaint condition label and a salesman basic attribute label;
the model tag includes: the service quality KPI label, the service staff aging label and the service staff operation index grading label are marked;
the predictive tag includes: the system comprises a fine early warning label, a predicted sign-in time label, a complaint risk degree ranking label and a tomorrow dispatch prediction label.
4. The big data-based salesman management method according to claim 2, further comprising:
acquiring adjustment data;
and adjusting the preset analysis model based on the adjustment data, and adjusting the preset parameter transmission parameters and the selection conditions.
5. The big data-based salesman management method according to claim 1, further comprising:
and pushing the work report to a corresponding salesman.
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| CN114841570A (en) * | 2022-05-07 | 2022-08-02 | 金腾科技信息(深圳)有限公司 | Data processing method, device, equipment and medium for customer relationship management system |
| CN114841570B (en) * | 2022-05-07 | 2023-07-25 | 金腾科技信息(深圳)有限公司 | Data processing method, device, equipment and medium for customer relationship management system |
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