CN115081729A - Bank outlet personnel allocation prediction method and device - Google Patents
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Abstract
The invention relates to the technical field of big data, and particularly provides a bank outlet personnel allocation prediction method, which comprises the following steps: s1, transacting business pipeline data through a bank, recording the work information and transacting business type of visiting clients, and matching the weather condition, local epidemic situation data, network marketing activities and pipeline data in the period according to dates; s2, determining a prediction period, and determining a historical data range for prediction according to the prediction period; and S3, calculating the number and the post of the personnel arrangement of the forecast website according to the forecast number of the visitors calculated in the step S2. Compared with the prior art, the method and the system can reasonably allocate the personnel number and the work function ratio of each network according to the prediction data, improve the operation efficiency of the network, reduce the operation cost overhead of the network, further reduce the waiting time of customers and improve the customer service satisfaction degree of the network.
Description
Technical Field
The invention relates to the technical field of big data, and particularly provides a bank outlet personnel allocation prediction method and device.
Background
In the operation process of a bank outlet, the waiting staff in the outlet is too much and the flow of people is too dense due to the explosive growth of customers in a certain day or a certain time period. The long waiting time can lead to the customer's tolerance value to drop, and then leads to customer satisfaction to reduce, and simultaneously, too intensive traffic of people also can increase the risk that the epidemic situation spreads. In view of the above problems, those skilled in the art are urgently required to solve the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a bank outlet personnel allocation prediction method with strong practicability.
The invention further provides a bank outlet personnel allocation prediction device which is reasonable in design, safe and applicable.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a bank outlet staffing prediction method comprises the following steps:
s1, transacting business pipeline data through a bank, recording the work information and transacting business type of visiting clients, and matching the weather condition, local epidemic situation data, network marketing activities and pipeline data in the period according to dates;
s2, determining a prediction period, and determining a historical data range for prediction according to the prediction period;
and S3, calculating the number and the post of the personnel arrangement of the forecast website according to the forecast number of the visitors calculated in the step S2.
Further, in step S1, based on the bank flow data, extracting the work information of the visiting customer, and dividing the work information of the visiting customer into two types, one type being a counter service, and the other type being a non-fixed work time;
extracting the service types of the clients to be divided into counter service and non-counter service, and determining the proportion P of the fixed working time clients to the non-fixed working time clients in the network client group Customer group Determining the ratio P of counter service to non-counter service of network node Business 。
Further, the number N of the businesses in the rainy or snowy severe weather is taken Bad weather Number of businesses in good weatherN Good weather Comparing, and calculating the influence coefficient C of the rain and snow weather on the visiting clients Weather conditions ＝N Bad weather /N Good weather 。
Further, the number of visitors is N when there are cases on the local day There are cases The number of visitors in the same period when there is no case is N No case And (5) comparing, and calculating to obtain an influence coefficient of the epidemic situation on the number of visiting clients:
C cases of disease ＝N There is a case of disease /N No case 。
Further, the number of visitors in the website is N when the website carries out marketing activities With marketing campaigns The number of visitors of the same period network when the network does not carry out marketing activities is N No marketing campaign And comparing, and calculating the marketing coefficient of the marketing activity to the number of visitors:
C marketing campaign ＝N With marketing campaigns /N No marketing campaign 。
Further, in step S2, the expected number of visitors in the period under normal conditions is determined according to the historical data, and the influence factors of weather, epidemic situation and marketing activities are calculated respectively, so as to calculate the expected number of visitors N in the expected period Prediction of As the amount of data increases, the predicted data is compared with the actual data, and the influence coefficient is continuously adjusted.
Further, in step S2, a prediction cycle is determined, and a history data range for prediction is determined according to the prediction cycle, which is specifically divided into:
(1) setting a prediction period, wherein the prediction period is divided according to three dimensions of a month, a week and a day;
(2) classifying the prediction period again, and dividing daily data into working days and non-working days; the ratio P of the customer groups of the branch points needed for the working day pretest Customer group ；
(3) According to the prediction period, calling the number of visitors in the current period in the time period, eliminating influences of factors such as weather and the like on the data as much as possible, and calculating the data N of the number of visitors predicted under the conventional condition General of ；
(4) Calculating the final predicted number of visitors by combining the influence coefficients of factors such as weather and epidemic situation
N Prediction ＝N General of *C Weather (weather) *C Cases of disease *C Marketing campaign ；
(5) With the enlargement of data scale, various influence coefficients are continuously adjusted.
Further, in step S3, the predicted passenger flow volume N is calculated based on the prediction coefficient P calculated in the different tag prediction cycles Preparation of ＝P*N Theory of things ；
According to the historical data of the network points, the proportion P of the business volume to the number of the personnel is confirmed, and then the proportion P of the personnel needed by the two business types is calculated according to the proportion Counter surface 、P Non cabinet surface Finally, the number of staff required by the counter service is obtained;
N counter surface ＝N Prediction *P*P Counter surface Number of employees N required for business other than counter Non cabinet surface ＝N Prediction (1-P) P is not counter.
A banking outlet staffing prediction device comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute a bank outlet staffing prediction method.
Compared with the prior art, the bank outlet personnel allocation prediction method and the device have the following outstanding beneficial effects:
the invention starts from the service flow of bank outlets and the information data of customers, and the bank outlet personnel configuration prediction system takes big data as a base point. With the help of the system, the bank can reasonably allocate the number of personnel and the work function ratio of each network according to the forecast data, so that the running efficiency of the network is improved, the running cost of the network is reduced, the waiting time of customers is reduced, and the customer service satisfaction of the network is improved.
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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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow diagram of a method for predicting staffing of banking outlets.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments in order to better understand the technical solutions of the present invention. 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 embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A preferred embodiment is given below:
as shown in fig. 1, the method for predicting staffing of a banking outlet in this embodiment includes the following steps:
s1, transacting business pipeline data through a bank, recording the work information and transacting business type of visiting clients, and matching the weather condition, local epidemic situation data, network marketing activities and pipeline data in the period according to dates;
(1) based on bank flow data, extracting the work information of visiting clients, and dividing the work information of the visiting clients into two types, wherein one type is counter business, and the other type is non-fixed working time;
extracting the service types of the clients to be divided into counter service and non-counter service, and determining the proportion P of the fixed working time clients to the non-fixed working time clients in the network client group Customer group Determining the ratio P of counter service to non-counter service of network node Business 。
(2) Get the business quantity N of the rainy and snowy bad weather Bad weather Number of services N in good weather Good weather Ratio of performanceAnd calculating the influence coefficient C of the rain and snow weather on the number of visiting clients Weather conditions ＝N Bad weather /N Good weather 。
(3) Taking the number N of local visitors who have cases on the same day There are cases The number of visitors in the same period when there is no case is N No case And (5) comparing, and calculating to obtain an influence coefficient of the epidemic situation on the number of visiting clients:
C cases of disease ＝N There is a case of disease /N No case 。
(4) Number of visitors of the website when the website carries out marketing activities With marketing campaigns The number of visitors of the same period network when the network does not carry out marketing activities is N No marketing campaign And comparing, and calculating the marketing coefficient of the marketing activity to the number of visitors:
C marketing campaign ＝N With marketing campaigns /N No marketing campaign 。
S2, determining a prediction period, and determining a historical data range for prediction according to the prediction period; and determining the predicted number of visitors under normal conditions in the period according to the historical data, and respectively calculating influence factors of weather, epidemic situation and marketing activities so as to calculate the predicted number of visitors N in the predicted period. As the amount of data increases, the predicted data is compared to the actual data and the impact coefficients are continually adjusted.
(1) Setting a prediction period, wherein the prediction period is divided according to three dimensions of month, week and day;
(2) classifying the prediction period again, and dividing daily data into working days and non-working days; the ratio P of the customer groups of the branch points needed for the working day pretest Customer group ；
(3) According to the prediction period, calling the number of visitors in the current period in the time period, eliminating influences of factors such as weather and the like on the data as much as possible, and calculating the data N of the number of visitors predicted under the conventional condition General of ；
(4) Calculating the final predicted number of visitors by combining the influence coefficients of factors such as weather and epidemic situation
N Prediction ＝N General of *C Weather (weather) *C Cases of disease *C Marketing campaign ；
(5) With the enlargement of data scale, various influence coefficients are continuously adjusted.
S3, calculating the number and the post of the arrangement of the personnel at the forecast website according to the forecast number of the visitors calculated in the step S2;
according to the prediction coefficients P calculated under different label prediction periods, the predicted passenger flow N is calculated Preparation of ＝P*N Theory of things ；
According to the historical data of the network points, the proportion P of the business volume to the number of the personnel is confirmed, and then the proportion P of the personnel needed by the two business types is calculated according to the proportion Counter surface 、P Non cabinet surface Finally, the number of staff required by the counter service is obtained;
N counter surface ＝N Prediction *P*P Counter surface Number of employees N required for non-counter business Non cabinet surface ＝N Prediction (1-P) P is not counter.
According to the foregoing method, an apparatus for predicting staffing of a banking outlet in this embodiment includes: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute a bank outlet staffing prediction method.
The above embodiments are only specific ones of the present invention, and the scope of the present invention includes but is not limited to the above embodiments, and any suitable changes or substitutions by those skilled in the art and in the method and apparatus claims for predicting staffing of bank outlets according to the present invention shall fall within the scope of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A bank outlet staffing prediction method is characterized by comprising the following steps:
s1, transacting business pipeline data through a bank, recording the work information and transacting business type of visiting clients, and matching the weather condition, local epidemic situation data, network marketing activities and pipeline data in the period according to dates;
s2, determining a prediction period, and determining a historical data range for prediction according to the prediction period;
and S3, calculating the number of the personnel arrangement and the post of the prediction website according to the predicted number of the visitors calculated in the step S2.
2. The method for predicting staffing allocation of a banking outlet as claimed in claim 1, wherein in step S1, the job information of the visiting customer is extracted based on the bank flow data, and the job information of the visiting customer is divided into two categories, one category is counter service and the other category is non-fixed working time;
extracting the service types of the clients to be divided into counter service and non-counter service, and determining the proportion P of the fixed working time clients to the non-fixed working time clients in the network client group Customer group Determining the ratio P of counter service to non-counter service of network node Business 。
3. A banking outlet staffing prediction method according to claim 1 or 2, characterized in that the number N of rainy or snowy severe weather services is taken Bad weather Number of services N in good weather Good weather Comparing, and calculating influence coefficient C of the rain and snow weather on visiting clients Weather (weather) ＝N Bad weather /N Good weather 。
4. A banking outlet staffing prediction method as claimed in claim 3,
taking the number of local visitors when there is a case on the day There are cases The number of visitors in the same period when there is no case is N No case And (5) comparing, and calculating to obtain an influence coefficient of the epidemic situation on the number of visiting clients:
C cases of disease ＝N There are cases /N No case 。
5. The method as claimed in claim 4, wherein the number of visitors is N when the website carries out marketing activities With marketing campaigns The number of visitors of the website when the marketing activities are not carried out by the website in the same period is N No marketing campaign And comparing, and calculating the marketing coefficient of the marketing activity to the number of visitors:
C marketing campaign ＝N With marketing campaigns /N No marketing campaign 。
6. The method as claimed in claim 5, wherein in step S2, the number of visitors expected in normal condition in the period is determined according to the historical data, and the influence factors of weather, epidemic situation and marketing activity are calculated respectively, so as to calculate the number N of visitors expected in the expected period Prediction of As the amount of data increases, the predicted data is compared with the actual data, and the influence coefficient is continuously adjusted.
7. The method as claimed in claim 6, wherein in step S2, determining a prediction period, and determining a historical data range for prediction according to the prediction period, the method is specifically divided into:
(1) setting a prediction period, wherein the prediction period is divided according to three dimensions of month, week and day;
(2) classifying the prediction period again, and dividing daily data into working days and non-working days; the ratio P of the customer groups of the branch points needed for the working day pretest Customer group ；
(3) According to the prediction period, the number of visitors in the current period within the time period is called, the influence of factors such as weather and the like on the data is eliminated as much as possible, and the predicted number in the conventional situation is calculatedNumber of visitors data N General of ；
(4) Calculating the final predicted number of visitors by combining the influence coefficients of factors such as weather and epidemic situation
N Prediction ＝N General of *C Weather (weather) *C Cases of disease *C Marketing campaign ；
(5) With the enlargement of data scale, various influence coefficients are continuously adjusted.
8. The method as claimed in claim 7, wherein in step S3, the predicted passenger flow N is calculated according to the prediction coefficients P calculated in different tag prediction periods Preparation of ＝P*N Theory of things ；
According to the historical data of the network points, the proportion P of the business volume to the number of the personnel is confirmed, and then the proportion P of the personnel needed by the two business types is calculated according to the proportion Counter surface 、P Non cabinet surface Finally, the number of staff required by the counter service is obtained;
N counter surface ＝N Prediction *P*P Counter surface Number of employees N required for non-counter business Non cabinet surface ＝N Prediction (1-P) P is not counter.
9. A banking outlet staffing prediction device, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program, to perform the method of any of claims 1 to 8.
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