Sales personnel scheduling method and system, intelligent terminal and storage medium

Abstract

The invention relates to a sales personnel scheduling method and system, an intelligent terminal and a storage medium, and relates to the technical field of computers, and the method comprises the steps: collecting shift data and sales indexes in a historical period, the shift data comprising sales personnel and working periods corresponding to the sales personnel, the sales indexes comprise sales data, sales activities, payment modes and passenger flow data; classifying the sales indexes according to the working periods to obtain segmented sales indexes corresponding to the working periods; an employee portrait of the salesman is generated; training the scheduling influence factor model by taking the working time period, the employee portrait and the segmented sales index as training samples; after training of the scheduling influence factor model is completed, obtaining prediction influence factors of a future time period; and calling the scheduling influence factor model, and carrying out data processing on the predicted influence factors to obtain a scheduling scheme of the salesman. The method and the device have the effect of improving the sales conversion rate of sales personnel.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a salesperson scheduling method, system, intelligent terminal, and storage medium. Background Art

[0002] In the retail business, reasonable sales staff scheduling directly affects store operating efficiency and customer experience.

[0003] Related technology compiles daily sales metrics for each store, including receipts, discounts, coupon usage, sales by product category, and receipts by payment method, and generates reports. Managers use these results to schedule sales staff.

[0004] Regarding the above-mentioned related technologies, the scheduling plan is generated based on the manager's experience, which limits the degree of matching between sales personnel and sales business needs, thereby reducing the sales conversion rate of sales personnel. Summary of the Invention

[0005] In order to improve the sales conversion rate of sales personnel, the present application provides a sales personnel scheduling method, system, intelligent terminal and storage medium.

[0006] In the first aspect, the present application provides a sales staff scheduling method, which adopts the following technical solution: A sales staff scheduling method, comprising: Collecting shift data and sales indicators within a historical period, wherein the shift data includes sales personnel and their corresponding working hours, and the sales indicators include sales data, sales activities, payment methods, and customer flow data; Classify the sales indicators according to the working periods to obtain segmented sales indicators corresponding to each working period; generating an employee profile of the salesperson; Using the working hours, the employee profiles, and the segmented sales indicators as training samples, a scheduling influencing factor model is trained, wherein the scheduling influencing factor model is used to predict the impact of the working hours and the sales indicators on the salesperson; After the scheduling influencing factor model is trained, the predicted influencing factors for future time periods are obtained; The scheduling influencing factor model is called, and data processing is performed on the predicted influencing factors to obtain a scheduling plan for the salesperson.

[0007] By adopting the above technical solutions and utilizing existing shift data, sales indicators, and employee portraits, a relationship between sales personnel and sales indicators is established, enabling the scheduling plan to achieve an accurate match between staffing and business needs, thereby improving the accuracy and feasibility of the scheduling plan and increasing sales conversion rates.

[0008] Optionally, the sales personnel are classified based on the employee portrait as a classification standard to obtain a first sales personnel and a second sales personnel, wherein the skill level of the first sales personnel is greater than a preset level threshold, and the skill level of the second sales personnel is not greater than the preset level threshold; Screening out a second target salesperson from the second salespersons; determining a first target salesperson associated with the second target salesperson from among the first salespersons; In the scheduling plan, determining a first scheduling period for the first target salesperson and a second scheduling period for the second target salesperson; A target time period in the first scheduling time period is selected, the passenger flow data prediction value corresponding to the target time period is greater than the passenger flow data threshold, and the target time period does not overlap with the second scheduling time period; Selecting a swap period from the second scheduled period based on the duration of the target period; The target period is added to the second shift period, and the exchange period is removed from the second shift period.

[0009] By adopting the above technical solution, the scheduling time periods of the second target salesperson are reallocated, the target time periods are added to the scheduling time periods of the second target salesperson, the scheduling time periods of the second target salesperson are optimized, the skill level of the salesperson is improved, and the overall balance of manpower supply and demand is optimized.

[0010] Optionally, obtaining historical sales data of the second salesperson; converting the historical sales data into sales performance of the second salesperson; Taking the target sales performance whose sales performance is greater than the first performance threshold and less than the second performance threshold, and determining a second candidate salesperson corresponding to the target sales performance; Counting the working hours of the second candidate salesperson; The second candidate salesperson whose working hours are less than the working hours threshold is selected as the second target salesperson.

[0011] By adopting the above technical solution, the second target salesperson is screened based on sales performance and working hours. On the premise of reasonably meeting the salesperson's rest time, the salesperson is provided with opportunities to learn and improve, which helps to improve the salesperson's work enthusiasm.

[0012] Optionally, in the event of a sales vacancy, determining a first employee profile of the sales vacancy; Determine, based on the first employee portrait, a candidate supplementary salesperson from the remaining salespersons, wherein the similarity between the second employee portrait of the candidate supplementary salesperson and the first employee portrait is greater than a similarity threshold; Determine the impact time period corresponding to the vacant salesperson from the scheduling plan; Determine a recent impact period in the impact period according to the current moment; Obtaining recent customer flow data and recent sales activities corresponding to the recent impact period; Screening the candidate supplementary sales personnel based on the recent customer flow data and the recent sales activities to obtain target supplementary sales personnel; The recent impact period is added to the scheduling plan for the target supplementary sales staff.

[0013] By adopting the above technical solution, when there is a vacancy in sales staff, a replacement sales staff is selected based on the vacant sales staff's schedule and employee portrait to ensure the normal operation of the store and the sales conversion rate of the entire store.

[0014] Optionally, if the candidate supplementary salesperson is not determined, determine a nearby store of the current store; Obtaining salesperson information of the nearby stores to obtain nearby salespersons; Obtaining the nearby shift scheduling plans for the nearby stores during the recent impact period; The target supplementary salesperson is selected from the nearby salespersons according to the nearby scheduling plan.

[0015] By adopting the above technical solutions and analyzing the sales data of different stores, human resources can be shared among multiple stores, optimizing the overall balance of human supply and demand, and avoiding human resource shortages in some stores.

[0016] Optionally, extracting advantageous skill features of the first user portrait; Sort the second user portraits in descending order according to the advantageous skill features to obtain a user portrait ranking; Obtain substitute sales employees based on the top n ranked user portraits; Extracting a first recent impact period and a second recent impact period from the recent impact period in chronological order; Adding the first recent impact period to the scheduling plan of the substitute sales employee; Obtaining the corresponding recent sales indicators of the substitute sales employee during the first recent impact period; Adjust the scheduling plan of the substitute sales employee during the second recent impact period according to the recent sales index.

[0017] By adopting the above technical solution, the scheduling plan of the substitute sales employee in the second recent impact period is adjusted according to the sales indicators of the substitute sales employee in the first recent impact period, thereby realizing feedback optimization of the scheduling plan and further ensuring the rationality of the scheduling plan.

[0018] Optionally, obtaining the recent work history of the target supplementary salesperson; Calculating a fatigue curve of the target supplementary salesperson based on the recent work history; A maximum fatigue level within the recent impact period according to the fatigue level curve; The salesperson whose maximum fatigue level is less than the fatigue threshold value among the target supplementary salesperson is selected, and the target supplementary salesperson is updated.

[0019] By adopting the above technical solution, the fatigue curve of the salesperson is calculated based on the recent work history, so as to realize the fatigue prediction of the salesperson, and ensure the normal rest time of the salesperson by updating the salesperson.

[0020] In a second aspect, the present application provides a sales staff scheduling system, which adopts the following technical solutions: A sales staff scheduling system, comprising: The acquisition module is used to obtain shift data, sales indicators, employee profiles, and forecast influencing factors; A memory for storing a program for scheduling the sales staff; The processor and the program in the memory can be loaded and executed by the processor to implement the sales staff scheduling method.

[0021] By adopting the above technical solutions and utilizing existing shift data, sales indicators, and employee portraits, a relationship between sales personnel and sales indicators is established, enabling the scheduling plan to achieve an accurate match between staffing and business needs, thereby improving the accuracy and feasibility of the scheduling plan and increasing sales conversion rates.

[0022] In a third aspect, the present application provides a smart terminal that adopts the following technical solution: An intelligent terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any one of the methods described above.

[0023] In a fourth aspect, the present application provides a computer storage medium capable of storing a corresponding program, which is convenient for improving the sales conversion rate of sales personnel and adopts the following technical solutions: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any of the above-mentioned sales staff scheduling methods.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. Leverage existing shift data, sales metrics, and employee profiles to establish relationships between sales personnel and sales metrics, enabling scheduling plans to accurately match staffing with business needs, improving the accuracy and feasibility of scheduling plans and boosting sales conversion rates. 2. Reassign the second target salesperson's schedule time slots, add the target time slots to the second target salesperson's schedule time slots, optimize the second target salesperson's schedule time slots, improve the salesperson's skill level, and optimize the overall balance of manpower supply and demand; 3. In the event of a sales vacancy, select a replacement salesperson based on the vacant salesperson’s schedule and employee profile to ensure the normal operation of the store and the sales conversion rate of the entire store. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flowchart of a sales staff scheduling method provided in an embodiment of the present application.

[0026] Figure 2 This is a flowchart of a method for adjusting a shift scheduling scheme provided in an embodiment of the present application.

[0027] Figure 3 This is a flow chart of a first method for adjusting a shift scheduling scheme provided in an embodiment of the present application.

[0028] Figure 4 This is a flow chart of a second method for adjusting a shift scheduling scheme provided in an embodiment of the present application.

[0029] Figure 5 This is a flow chart of a third method for adjusting a shift schedule provided in an embodiment of the present application.

[0030] Figure 6 This is a flow chart of a fourth method for adjusting a shift schedule provided in an embodiment of the present application.

[0031] Figure 7 This is a structural diagram of a sales staff scheduling system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1 To the attached Figure 7 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0033] The embodiment of the present application discloses a method for scheduling sales staff. Figure 1 , the method comprising: Step S101: Collect shift data and sales indicators within a historical period. The shift data includes sales personnel and their corresponding working hours. The sales indicators include sales data, sales activities, payment methods, and customer flow data.

[0034] The salespeople in the shift data are time-dependent. For example, the salespeople on Monday are Person A and Person B, while the salespeople on Tuesday are Person A and Person C.

[0035] Working hours refer to the salesperson's daily working hours. The salesperson's working hours may vary over time. For example, salesperson A's working hours on Monday are from 8:00 to 17:00, while salesperson A's working hours on Tuesday are from 13:00 to 22:00.

[0036] The sales data includes at least one of sales revenue, order volume, average customer unit price, and product sales volume.

[0037] Sales activities include but are not limited to at least one of discount activities, purchase-and-get-one-free activities, lucky draw activities, and member welfare activities.

[0038] Payment method refers to the specific means or channels used by customers to complete fund settlement. Payment methods include electronic payment and cash payment.

[0039] Customer flow data refers to the number of customers entering the store within a unit of time.

[0040] For example, historical sales data for a specific period can be obtained through a POS (Point of Sale) system. After verification, the sales data is imported into an analysis database for subsequent use. Furthermore, after verification, a marker can be added to the sales data to record the corresponding product, payment method, and sales activity.

[0041] Step S102: Classify the sales indicators according to the working hours to obtain the segmented sales indicators corresponding to each working hour.

[0042] Segmented sales indicators are used to represent sales indicators within different working hours. For example, a sales timestamp corresponding to the sales indicator is determined. This sales timestamp indicates the time the sales indicator was generated or the time corresponding to the sales indicator. For example, if the sales indicator refers to sales data, the sales timestamp indicates the time the sales data was generated; if the sales indicator refers to customer flow data, the sales timestamp indicates the date corresponding to the customer flow data. Based on the correspondence between sales timestamps and working hours, sales indicators are classified to obtain segmented sales indicators.

[0043] For example, the segmented sales index refers to the sales index from 7:00 to 17:00 on January 1st.

[0044] Step S103: Generate an employee portrait of the salesperson.

[0045] Employee portrait is a digital talent model formed by combining the multi-dimensional characteristics of sales personnel.

[0046] For example, employee metrics for sales personnel are obtained, including their education, job level, skills, work performance, sales type, etc. The employee metrics are quantified into employee characteristics. An employee profile is generated based on the employee characteristics.

[0047] Step S104: Using working hours, employee portraits, and segmented sales indicators as training samples, a scheduling influencing factor model is trained. The scheduling influencing factor model is used to predict the impact of working hours and sales indicators on sales personnel.

[0048] The scheduling influencing factor model adopts regression analysis or decision tree model.

[0049] For example, a functional relationship between sales personnel, sales data, sales activities, payment methods, and customer flow data is generated based on segmented sales indicators. Combined with employee profiles, the sales personnel's contribution to the sales conversion rate of the sales data is evaluated.

[0050] Optionally, the scheduling influencing factor model includes a prediction module and an optimization module. The prediction module outputs sales data for different salesperson combinations within a specific working period based on working hours, segmented sales indicators, and employee profiles. The prediction module can use a linear regression model or a decision tree model. The optimization module uses the output of the prediction module to generate a scheduling plan that optimizes the overall objective. Overall optimization is a type of multi-objective optimization that optimizes sales data, labor costs, salesperson satisfaction, and customer satisfaction. The optimization module can use a linear programming model.

[0051] Furthermore, when training the scheduling influencing factor model, the prediction module and the optimization module need to be trained separately. For the prediction module, feature extraction is performed on indicators other than sales data for working hours and segmented sales indicators to form input features; a validation set is formed based on employee portraits and sales data. The input features are input into the prediction module to obtain a training output set. The prediction module is trained based on the loss value between the training output set and the validation set. For the optimization module, since the optimization module uses a linear programming module, it essentially uses constraints and functions for solution. Therefore, the training process of the optimization module is equivalent to the process of adjusting the parameters of the constraints and functions. The optimization module approaches the goal of optimizing the overall goal by adjusting the parameters in the constraints and functions.

[0052] Step S105: After the scheduling influencing factor model is trained, the predicted influencing factors for the future time period are obtained.

[0053] Factors influencing the forecast include future working hours, sales activities, payment methods, predicted customer flow data, etc.

[0054] It's important to note that the scheduling influencing factor model is continuously trained over time, and the scheduling data model continuously collects data to update its own model parameters to adapt to changes in the market and business environment. This process forms a closed-loop optimization loop, further improving the accuracy and feasibility of scheduling recommendations.

[0055] Step S106: calling the scheduling influencing factor model, performing data processing on the predicted influencing factors, and obtaining a scheduling plan for the sales staff.

[0056] Optionally, there can be multiple scheduling plans, and corresponding forecast performance indicators are provided. The forecast performance indicators include one or more of predicted sales data, labor costs, and customer wait times. The scheduling plans and forecast performance indicators are displayed on a visual interface to facilitate comparison, selection, and adjustment by management personnel.

[0057] By adopting the above technical solutions and utilizing existing shift data, sales indicators, and employee portraits, a relationship between sales personnel and sales indicators is established, enabling the scheduling plan to achieve an accurate match between staffing and business needs, thereby improving the accuracy and feasibility of the scheduling plan and increasing sales conversion rates.

[0058] In the following embodiment, the shift schedule of some sales personnel can be adjusted to enable these sales personnel to improve their sales skills in their sales work. Therefore, the embodiment of the present application discloses a method for adjusting the shift schedule. Figure 2 , the method comprising: Step S201: Classify the sales staff based on the employee portrait as the classification standard to obtain a first sales staff and a second sales staff. The skill level of the first sales staff is greater than a preset level threshold, and the skill level of the second sales staff is not greater than the preset level threshold.

[0059] Optionally, the employee profile includes the salesperson's skill level, which is used to measure the salesperson's mastery of skills. Salespeople are categorized based on their skill level, with salespeople whose skill level exceeds a preset threshold being classified as first-tier salespeople and salespeople whose skill level is below the preset threshold being classified as second-tier salespeople.

[0060] The preset level threshold is a preset value, and technicians can adjust the preset level threshold according to actual needs.

[0061] Step S202: Screen out a second target salesperson from the second salesperson.

[0062] In one implementation of this embodiment, the second target salesperson may be selected from the second salesperson in the following manner, specifically including: 1. Obtain the historical sales data of the second salesperson.

[0063] The historical sales data refers to the sales data of the second salesperson during the historical period. For example, after collecting and obtaining the sales data during the historical period, the goods sold by the second salesperson are determined; and the historical sales data is determined based on the aforementioned goods sold.

[0064] 2. Convert historical sales data into the sales performance of the second salesperson.

[0065] Optionally, the performance data corresponding to the historical sales data is retrieved from a preset sales-performance conversion table to obtain the sales performance of the second salesperson. The sales-performance conversion table is used to represent the corresponding relationship between sales data and sales performance.

[0066] 3. Take the target sales performance whose sales performance is greater than the first performance threshold and less than the second performance threshold, and determine the second candidate salesperson corresponding to the target sales performance.

[0067] The first performance threshold is less than the second performance threshold, and the first performance threshold and the second performance threshold can be set by relevant personnel. In a specific embodiment, the first performance threshold refers to the salesperson's performance pass line, and the second performance threshold refers to the salesperson's performance excellent line.

[0068] Exemplarily, a sales performance that satisfies a threshold greater than a first performance threshold and less than a second performance threshold is searched to obtain a target sales performance, and the salesperson corresponding to the target sales performance is determined as the second candidate salesperson.

[0069] 4. Count the working hours of the second candidate salesperson.

[0070] In this step, working hours refers to the total working hours of the second candidate in the historical period.

[0071] 5. Select the second candidate salesperson whose working hours are less than the working hours threshold as the second target salesperson.

[0072] The working hours threshold is a preset empirical value, and the working hours threshold may refer to the maximum working hours of a salesperson in a unit of time.

[0073] Therefore, the above-mentioned method for determining the second target sales department personnel can screen the second target sales personnel based on sales performance and working hours. On the premise of reasonably meeting the sales personnel's rest time, it provides sales personnel with opportunities to learn and improve, which helps to improve the sales personnel's work enthusiasm.

[0074] Step S203: Determine a first target salesperson associated with the second target salesperson from among the first salespersons.

[0075] In some embodiments, the first target salesperson is a salesperson who is on the same team as the second target salesperson.

[0076] In other embodiments, the first target salesperson refers to a salesperson whose rank is higher than that of the second target salesperson.

[0077] Step S204: In the scheduling plan, determine a first scheduling period for the first target salesperson and a second scheduling period for the second target salesperson.

[0078] The first scheduling period refers to the working period of the first target salesperson in the future period.

[0079] The second scheduling period refers to the working period of the second target salesperson in the future period.

[0080] Step S205: Take the target time period in the first scheduling time period, the passenger flow data prediction value corresponding to the target time period is greater than the passenger flow data threshold, and the target time period does not overlap with the second scheduling time period.

[0081] The passenger flow data threshold is a preset empirical value, and relevant personnel can adjust the specific value of the passenger flow data threshold according to actual needs.

[0082] Optionally, the duration of the target period is greater than a preset duration threshold.

[0083] Optionally, a difference period is obtained based on the difference between the first and second shift scheduling periods. Passenger flow data prediction values ​​are predicted for each moment in the difference period. The target period is selected as the period where the passenger flow data prediction value is greater than a passenger flow data threshold and has a duration greater than a preset duration threshold.

[0084] Step S206: Select an exchange period from the second scheduled period according to the duration of the target period.

[0085] Optionally, the exchange period and the target period do not overlap, and the duration of the exchange period is the same as the duration of the target period.

[0086] Step S207: adding the target period to the second shift schedule period, and removing the exchange period from the second shift schedule period.

[0087] This step will adjust the working hours of the second target salesperson so that the second target salesperson works during the target period. The customer flow during the target period is large, and the sales performance of the second target salesperson reaches a certain level and the working hours are appropriate. The first target salesperson and the second target salesperson will work together during the target period, so that the second target salesperson can train his sales skills during the target period without affecting the second target salesperson's rest.

[0088] By adopting the above technical solution, the scheduling time periods of the second target salesperson are reallocated, the target time periods are added to the scheduling time periods of the second target salesperson, the scheduling time periods of the second target salesperson are optimized, the skill level of the salesperson is improved, and the overall balance of manpower supply and demand is optimized.

[0089] In actual scenarios, sales staff may be unable to work for a certain period of time, resulting in vacancies in the store's shift schedule, which may affect the normal operation of the store. Therefore, the embodiment of this application discloses a method for adjusting the shift schedule. Figure 3 , the method comprising: Step S301: when there is a vacant salesperson, determine a first employee portrait of the vacant salesperson.

[0090] The vacant sales staff may be a sales staff who has resigned, a sales staff whose vacation date has reached a preset number of days, or a sales staff who has been on a business trip for a preset number of days.

[0091] The first employee profile refers to the employee profile of the vacant salesperson.

[0092] Step S302: Based on the first employee portrait, determine candidate supplementary sales personnel from the remaining sales personnel, where the similarity between the second employee portrait of the candidate supplementary sales personnel and the first employee portrait is greater than a similarity threshold.

[0093] Exemplarily, a feature extraction operation is performed on the employee profiles of all sales personnel to obtain employee profile features. The employee profile features are mapped into a vector space. A first employee profile feature corresponding to the first employee profile is determined in the vector space. A candidate employee profile feature whose Euclidean distance from the first employee profile feature is less than a preset Euclidean distance is determined in the vector space. The sales personnel corresponding to the candidate employee profile feature are selected as candidate supplementary sales personnel. The preset Euclidean distance is converted to obtain a similarity threshold.

[0094] Step S303: Determine the impact time period corresponding to the vacant sales staff from the scheduling plan.

[0095] The impact period refers to the working period of the vacant salesperson in the scheduling plan.

[0096] Step S304: determining a recent impact period in the impact period according to the current time.

[0097] The time difference between the recent impact period and the current time is less than the preset impact duration, and the recent impact period is a continuous period, for example, the impact duration is 20 hours.

[0098] Step S305: Acquire recent customer flow data and recent sales activities corresponding to the recent impact period.

[0099] Recent customer flow data refers to customer flow data within the recent impact period. Recent sales activity refers to sales activity within the recent impact period.

[0100] Step S306: Screen candidate supplementary sales personnel based on recent customer flow data and recent sales activities to obtain target supplementary sales personnel.

[0101] For example, recent customer flow data and recent sales activities are quantified to obtain quantified recent customer flow data and quantified recent sales activities. Based on the employee profiles of candidate supplementary sales personnel, sales personnel corresponding to the quantified recent customer flow data and quantified recent sales activities are screened to obtain target supplementary sales personnel.

[0102] Step S307: Add the recent impact period to the scheduling plan for the target supplementary sales staff.

[0103] Optionally, add a recent impact period to the scheduling plan of the target supplementary sales staff, and remove the corresponding period in the scheduling plan of the target supplementary sales staff based on the duration of the recent impact period to ensure the rest time of the target supplementary sales staff.

[0104] By adopting the above technical solution, when there is a vacancy in sales staff, a replacement sales staff is selected based on the vacant sales staff's schedule and employee portrait to ensure the normal operation of the store and the sales conversion rate of the entire store.

[0105] In the following embodiment, sales staff can also be transferred from other stores to fill the vacant sales staff. Therefore, the embodiment of the present application discloses a second method for adjusting the shift plan. Figure 4 , the method comprising: Step S401: If no candidate supplementary salesperson has been determined, determine nearby stores of the current store.

[0106] Nearby stores refer to stores whose physical distance from the current store is less than a preset physical distance threshold.

[0107] Step S402: Acquire salesperson information of nearby stores to obtain nearby salespersons.

[0108] Nearby sales staff refers to sales staff at nearby stores. Sales staff information includes at least one of the sales staff's name, employee profile, and rank.

[0109] Step S403: Obtain the nearby shift scheduling plans of nearby stores during the recent impact period.

[0110] The nearby shift plan includes the shift plans of nearby sales staff in nearby stores during the recent impact period.

[0111] Step S404: Select target supplementary sales personnel from nearby sales personnel according to the nearby scheduling plan.

[0112] Exemplarily, employee portraits of nearby sales personnel are obtained to obtain nearby employee portraits. Feature extraction operations are performed on the nearby employee portraits to obtain nearby employee portrait features. The nearby employee portrait features are mapped into a vector space. A first employee portrait feature corresponding to the first employee portrait is determined in the vector space. A nearby employee portrait feature whose Euclidean distance from the first employee portrait feature is less than a preset Euclidean distance is determined in the vector space. The sales personnel corresponding to the nearby employee portrait feature are selected as target supplementary sales personnel. The preset Euclidean distance is converted to obtain a similarity threshold.

[0113] By adopting the above technical solutions and analyzing the sales data of different stores, human resources can be shared among multiple stores, optimizing the overall balance of human supply and demand, and avoiding human resource shortages in some stores.

[0114] This application embodiment discloses a third method for adjusting a shift schedule. Figure 5 , the method comprising: Step S501: extracting advantageous skill features of the first user portrait.

[0115] Optionally, skill features are extracted from the first user profile. A skill scoring model is invoked to score the skill features to obtain a skill score. Skill scores greater than a skill score threshold are selected, and skill features corresponding to the skill scores are determined to obtain a dominant skill feature.

[0116] Step S502: Sort the second user portraits in descending order according to their advantageous skill features to obtain a user portrait ranking.

[0117] For example, a second skill score of the advantageous skill feature of the second user profile is obtained, and the second user profile is sorted in descending order using the second skill score to obtain a user profile ranking.

[0118] Step S503: Obtain substitute sales staff based on the top n ranked user portraits.

[0119] Where n is a preset positive integer. For example, n is 5. The first n user profiles in the ranking are taken to obtain the first n second user profiles. The sales staff corresponding to the first n second user profiles are determined as replacement sales staff.

[0120] Step S504: extracting a first recent impact period and a second recent impact period from the recent impact period in chronological order.

[0121] The first recent impact period is closer to the current period than the second recent impact period.

[0122] Optionally, the recent impact time periods are redistributed according to a preset ratio to obtain a first recent impact time period and a second recent impact time period.

[0123] Optionally, the recent impact period can be split into several sub-recent impact periods based on the continuity of the time periods within the recent impact period. The time period closest to the current time in the sub-recent impact period is taken as the first recent impact period, and the sub-recent impact periods other than the first recent impact period are taken as the second recent impact period.

[0124] Step S505: Add the first recent impact period to the scheduling plan for the replacement sales employee.

[0125] Optionally, add the first recent impact period to the scheduling plan for the substitute sales staff, and remove the corresponding period in the scheduling plan for the substitute sales staff based on the length of the first recent impact period to ensure the rest time of the substitute sales staff.

[0126] Step S506: Obtain the recent sales index corresponding to the substitute sales employee in the first recent impact period.

[0127] Recent sales metrics include sales data, sales activity, payment methods, and customer traffic data for the replacement sales employee during the first recent impact period.

[0128] Step S507: Adjust the scheduling plan of the substitute sales employee in the second recent impact period according to the recent sales indicators.

[0129] For example, if the difference between the recent sales index and the ideal sales index is greater than a preset difference, the substitute sales employee's scheduled hours during the second recent impact period are increased. If the difference between the recent sales index and the ideal sales index is less than the preset difference, the substitute sales employee's scheduled hours during the second recent impact period are decreased.

[0130] By adopting the above technical solution, the scheduling plan of the substitute sales employee in the second recent impact period is adjusted according to the sales indicators of the substitute sales employee in the first recent impact period, thereby realizing feedback optimization of the scheduling plan and further ensuring the rationality of the scheduling plan.

[0131] In the following embodiment, when selecting target supplementary sales staff, it is also necessary to ensure the normal rest time of the sales staff to avoid the sales staff from working continuously in a short period of time. Therefore, the embodiment of the present application discloses a fourth method for adjusting the shift plan. Figure 6 , the method comprising: Step S601: Obtain the recent work history of the target supplementary salesperson.

[0132] Recent work history refers to the data set of the target supplementary salesperson's work hours within a preset time period. For example, recent work history refers to the target supplementary salesperson's total work hours and daily work hours over the past 7 days.

[0133] Step S602: Calculate the fatigue curve of the target supplementary salesperson based on recent work history.

[0134] The fatigue curve is used to reflect the accumulated fatigue state of the salesperson. For example, the fatigue curve is ,in, is the preset coefficient, represents the working hours on day i, Represents the attenuation weight, which depends on the value of i. The larger i is, The bigger it is, the longer the salesperson works. The bigger it is, the more fatigued the salesperson will be.

[0135] Step S603: Calculate the maximum fatigue level within the recent impact period based on the fatigue level curve.

[0136] After the fatigue curve is obtained in step S602, the fatigue degree of the fatigue curve within the recent impact period is calculated using the recent impact period as input to obtain a fatigue degree set. The maximum value in the fatigue degree set is taken as the maximum fatigue degree.

[0137] Step S604: Select the salesperson whose maximum fatigue level is less than the fatigue level threshold among the target supplementary salespersons, and update the target supplementary salespersons.

[0138] The fatigue threshold is a preset empirical value. When the salesperson's maximum fatigue is greater than the fatigue threshold, it is not recommended that the salesperson continue to work. Therefore, it is not recommended for the salesperson to work during the recent impact period.

[0139] By adopting the above technical solution, the fatigue curve of the salesperson is calculated based on the recent work history, so as to realize the fatigue prediction of the salesperson, and ensure the normal rest time of the salesperson by updating the salesperson.

[0140] Optionally, the present application also provides a physical architecture of a sales staff scheduling system, which includes a POS front-end layer, a data synchronization and cache layer, a sales statistics and analysis layer, a scheduling impact analysis layer, a shift handover and authority management module, and a user interface layer.

[0141] POS front-end layer: Supports multiple operating systems, including Windows and Android. Cashiers log into the POS system to create a shift schedule and record order details, payment information, and other data in a local MySQL database. After completing cashier checkout and shift handover, the POS system uploads the collected shift data and sales metrics to the cloud.

[0142] Data synchronization and caching layer: Each POS terminal synchronizes shift data and sales metrics to a cloud-based MySQL database via the network. The cloud system uses Redis to cache hot data (such as real-time sales statistics and current shift schedules) to improve data query and statistical efficiency.

[0143] Sales Statistics and Analysis Layer: This backend processes uploaded shift-order-payment data to construct multidimensional datasets for business days and shifts. This layer is responsible for summarizing sales metrics (including by time period, product category, payment method, and other dimensions) and generating various reports and metrics for subsequent analysis.

[0144] Scheduling Impact Analysis: Based on sales statistics, a model of factors influencing scheduling is established. This layer uses statistical methods to assess the impact of sales indicators on labor demand, such as predicting the number of employees required for each time period based on order volume and sales data. By linking scheduling plans and sales indicators in a closed-loop manner, the analysis results accurately reflect actual business conditions.

[0145] Shift Handover and Permission Management Module: This module implements data verification and permission control for shift handover operations. During the handover process, the system verifies the current shift's income and expenditure data and generates handover documents (including paper or electronic payment slips). This module supports printing of current shift details and daily business reports, ensuring the accuracy and reliability of data for subsequent analysis.

[0146] User Interface: This layer provides a visual management interface and export functionality. Users can view sales statistics, schedule recommendations, and analysis reports for each schedule, and export reports for decision-making. The interface supports flexible filtering and comparison, assisting managers in scheduling evaluations.

[0147] Based on the same inventive concept, an embodiment of the present application provides a salesperson scheduling system, the system comprising: Acquisition module 701, for acquiring shift data, sales indicators, employee profiles and prediction influencing factors; Memory 702, used to store the program for the sales staff scheduling method; Processor 703, the program in the memory can be loaded and executed by the processor to implement the above-mentioned sales staff scheduling method.

[0148] By adopting the above technical solutions and utilizing existing shift data, sales indicators, and employee portraits, a relationship between sales personnel and sales indicators is established, enabling the scheduling plan to achieve an accurate match between staffing and business needs, thereby improving the accuracy and feasibility of the scheduling plan and increasing sales conversion rates.

[0149] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0150] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor for a method for scheduling sales personnel.

[0151] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0152] Based on the same inventive concept, an embodiment of the present application provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed by the sales staff scheduling method.

[0153] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A sales staff scheduling method, characterized in that: The method comprises: Collecting shift data and sales indicators within a historical period, wherein the shift data includes sales personnel and their corresponding working hours, and the sales indicators include sales data, sales activities, payment methods, and customer flow data; Classify the sales indicators according to the working periods to obtain segmented sales indicators corresponding to each working period; generating an employee profile of the salesperson; Using the working hours, the employee profiles, and the segmented sales indicators as training samples, a scheduling influencing factor model is trained, wherein the scheduling influencing factor model is used to predict the impact of the working hours and the sales indicators on the salesperson; After the scheduling influencing factor model is trained, the predicted influencing factors for future time periods are obtained; The scheduling influencing factor model is called, and data processing is performed on the predicted influencing factors to obtain a scheduling plan for the salesperson.

2. The sales staff scheduling method according to claim 1, characterized in that: The method further comprises: Classifying the salespersons based on the employee portraits to obtain a first salesperson and a second salesperson, wherein the skill level of the first salesperson is greater than a preset level threshold, and the skill level of the second salesperson is not greater than the preset level threshold; Screening out a second target salesperson from the second salespersons; determining a first target salesperson associated with the second target salesperson from among the first salespersons; In the scheduling plan, determining a first scheduling period for the first target salesperson and a second scheduling period for the second target salesperson; A target time period in the first scheduling time period is selected, the passenger flow data prediction value corresponding to the target time period is greater than the passenger flow data threshold, and the target time period does not overlap with the second scheduling time period; Selecting a swap period from the second scheduled period based on the duration of the target period; The target period is added to the second shift period, and the exchange period is removed from the second shift period.

3. The sales staff scheduling method according to claim 2, characterized in that: The step of selecting a second target salesperson from the second salespersons includes: Obtaining historical sales data of the second salesperson; converting the historical sales data into sales performance of the second salesperson; Taking the target sales performance whose sales performance is greater than the first performance threshold and less than the second performance threshold, and determining a second candidate salesperson corresponding to the target sales performance; Counting the working hours of the second candidate salesperson; The second candidate salesperson whose working hours are less than the working hours threshold is selected as the second target salesperson.

4. The sales staff scheduling method according to claim 1, characterized in that: The method further comprises: In the event of a sales vacancy, determining a first employee profile of the sales vacancy; Determine, based on the first employee portrait, a candidate supplementary salesperson from the remaining salespersons, wherein the similarity between the second employee portrait of the candidate supplementary salesperson and the first employee portrait is greater than a similarity threshold; Determine the impact time period corresponding to the vacant salesperson from the scheduling plan; Determine a recent impact period in the impact period according to the current moment; Obtaining recent customer flow data and recent sales activities corresponding to the recent impact period; Screening the candidate supplementary sales personnel based on the recent customer flow data and the recent sales activities to obtain target supplementary sales personnel; The recent impact period is added to the scheduling plan for the target supplementary sales staff.

5. The sales staff scheduling method according to claim 4, characterized in that: The method further comprises: If the candidate supplementary salesperson is not determined, determine nearby stores of the current store; Obtaining salesperson information of the nearby stores to obtain nearby salespersons; Obtaining the nearby shift scheduling plans for the nearby stores during the recent impact period; The target supplementary salesperson is selected from the nearby salespersons according to the nearby scheduling plan.

6. The sales staff scheduling method according to claim 5, characterized in that: The method further comprises: Extracting advantageous skill features of the first user profile; Sort the second user portraits in descending order according to the advantageous skill features to obtain a user portrait ranking; Obtain substitute sales employees based on the top n ranked user portraits; Extracting a first recent impact period and a second recent impact period from the recent impact period in chronological order; Adding the first recent impact period to the scheduling plan of the substitute sales employee; Obtaining the corresponding recent sales indicators of the substitute sales employee during the first recent impact period; Adjust the scheduling plan of the substitute sales employee during the second recent impact period according to the recent sales index.

7. The sales staff scheduling method according to claim 4, characterized in that: The method further comprises: Obtaining the recent work history of the target supplementary salesperson; Calculating a fatigue curve of the target supplementary salesperson based on the recent work history; A maximum fatigue level within the recent impact period according to the fatigue level curve; The salesperson whose maximum fatigue level is less than the fatigue threshold value among the target supplementary salesperson is selected, and the target supplementary salesperson is updated.

8. A sales staff scheduling system, characterized in that: The system is used to execute the sales staff scheduling method according to any one of claims 1 to 7, and the system comprises: The acquisition module is used to obtain shift data, sales indicators, employee profiles, and forecast influencing factors; A memory for storing a program for scheduling the sales staff; The processor and the program in the memory can be loaded and executed by the processor to implement the sales staff scheduling method.

9. An intelligent terminal, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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