Information generation method and system based on main commodity linkage

Abstract

The invention discloses an information generation method and system based on main commodity linkage, and the method comprises the steps: selecting a main commodity SKU in a commodity management page in response to a first association instruction, and enabling the main commodity SKU to be the minimum commodity unit of inventory management; in response to the second association instruction, an associated product SKU is determined in product SKUs corresponding to the main commodity SKU, the product SKU is the minimum product unit operated by the shop, and the associated product SKU and the main commodity SKU have an information synchronization relationship; in response to the information modification instruction, performing modification operation on the commodity information of the main commodity SKU to obtain target commodity information; and when it is detected that the commodity information of the main commodity SKU is changed, performing a modification operation on the product information of the associated product SKU based on the target commodity information to obtain target product information displayed on the e-commerce sales page. The online product information of each platform shop can be accurately and quickly modified in batches under the condition that the shop management authority is ensured to be clear and orderly, so that the shop operation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for determining inventory based on historical sales, a method for generating information based on main product linkage, a method for managing gift items in an e-commerce store, and corresponding systems. Background Art

[0002] In daily e-commerce activities, e-commerce companies usually set safety stock values ​​for goods to ensure that the inventory level of goods remains sufficient and avoid product delivery delays and order losses caused by production interruptions or out-of-stock. At present, e-commerce ERP systems on the market usually use fixed values ​​as safety stocks; however, due to the uncertainty of product sales, inaccuracy of market demand forecasts, and volatility of material delivery time, the use of a fixed safety stock value strategy is prone to various adverse consequences. For example, an excessively high safety stock value will significantly increase the company's inventory costs, occupy valuable inventory space, and even cause a large amount of product inventory to be stranded, increasing the risk of product expiration, damage or failure; for another example, an excessively low safety stock value may cause the company to fall into a shortage of stock, unable to fulfill its contract in a timely manner, and ultimately suffer severe penalties from the e-commerce platform.

[0003] In addition, in actual operations, when sellers operate a large number of stores and product SKUs, when they need to change operating strategies or maintain product sales pages, they often need to manually adjust the product display information and prices one by one, which is inefficient, cumbersome, and error-prone.

[0004] In addition, when sellers operate multiple stores on multiple platforms, they usually need to set gift rules for each store in turn, resulting in independent gift rules for each site, platform, and store, which cannot be managed in a unified manner, causing management confusion and low operational efficiency. In addition, sellers cannot set differentiated gift rules based on actual operating scenarios, such as holidays or platform promotions, and have low management flexibility for gift rules.

[0005] Other technical issues related to this application will be further described later. The above content is only used to assist in understanding the technical solution of this application, and does not mean that all of the above content is prior art. Summary of the invention

[0006] The main purpose of the present invention is to provide an inventory determination method and system based on historical sales, which can dynamically adjust the safety inventory value of the goods according to the current operating environment, ensure that the safety inventory value is accurate and reasonable, so as to adapt to the ever-changing operating environment, reduce the inventory cost of goods, and ensure that the inventory of goods meets market demand. In addition, the application also provides an information generation method and system based on the linkage of main goods, which can accurately and quickly batch modify the online product information of each platform store while ensuring that the store management authority is clear and orderly, thereby improving the store operation efficiency. In addition, the application also provides a gift management method and system for an e-commerce store, which can uniformly set and centrally manage the gift rules of multiple stores, and flexibly expand the dimensions of gift rules, greatly improving the processing efficiency and accuracy of order gifts.

[0007] To achieve the above objectives, the present application proposes a method and system for determining inventory based on historical sales, the method comprising: Step S1: Obtain historical sales data of the target product; Step S2: Based on the historical sales data, calculate the daily average sales corresponding to each preset time period, wherein each preset time period is a historical time period of different lengths from the current time; Step S3: determining a change trend of the historical sales data based on the daily average sales, wherein the change trend includes an upward trend and a downward trend; Step S4: determining a weight corresponding to the daily average sales volume based on the change trend, wherein the historical time period includes a first time period with the shortest duration from the current time, the rising trend corresponds to a first weight based on the first time period, and the falling trend corresponds to a second weight based on the first time period, and the first weight is greater than the second weight; Step S5: Calculate the predicted daily sales based on the weighted average daily sales, and determine the safety stock corresponding to the target product based on the predicted daily sales. The safety stock is used to determine whether the current stock of the target product needs to be replenished.

[0008] Other features and technical effects of this application are described in the following part of the specification. The technical problem solving ideas and related product design solutions of this application are: The applicant found that the historical sales data of the goods can reflect the current operating environment, that is, reflect multiple factors such as the uncertainty of commodity sales volume, the inaccuracy of market demand forecasts, and the volatility of supplier / material delivery time. Therefore, historical sales data can be used as a reference factor for dynamically adjusting the safety inventory value of the goods.

[0009] On this basis, the applicant proposed to calculate the average daily sales of different historical time periods based on the historical sales data of the goods, such as the average sales of the past 3 days, the average sales of the past 7 days, the average sales of the past 15 days, etc., so as to predict the daily sales of the goods in the future replenishment cycle based on the average sales of the above historical time periods.

[0010] Furthermore, according to the trend of historical sales data, corresponding weights are set for the daily average sales in each historical time period, and the predicted daily sales and safety stock determined can be more in line with the actual e-commerce business scenario. The reason is that although both too high safety stock and too low safety stock will have an adverse impact on e-commerce companies, the impact of inventory costs caused by too high safety stock (slightly increasing the financial burden) is less harmful than the impact of out-of-stock caused by too low safety stock (affecting the store's reputation, order loss or breach of contract, and penalties from the e-commerce platform). Therefore, the setting method of safety stock should be more inclined to ensure that the inventory of goods can meet market demand.

[0011] Specifically, when the trend of historical sales data is an upward trend, a higher weight is set for the average sales of the past 3 days, tending to increase the predicted daily sales and safety stock, thereby ensuring that the commodity inventory can meet the increase in market demand; when the trend of historical sales data is a downward trend, a lower weight is set for the average sales of the past 3 days, focusing more on the changes in average sales over long periods of time, tending to maintain or slightly reduce the existing predicted daily sales and safety stock, thereby avoiding excessive reduction of commodity inventory and the risk of failure to meet market demand.

[0012] In this way, the inventory determination method of historical sales provided by the present application calculates the average daily sales corresponding to multiple historical time periods based on the historical sales data of the target product, and sets corresponding weights for the average daily sales based on the changing trend of the historical sales data, thereby determining the predicted daily sales of the product; then, the safety stock is dynamically adjusted according to the predicted daily sales of the product to ensure that the safety stock is accurate and reasonable, thereby adapting to the ever-changing operating environment, reducing product inventory costs, and ensuring that product inventory meets market demand.

[0013] Furthermore, the present application also provides a method for generating information based on main product linkage, which is used in a product module of an e-commerce ERP system or an e-commerce platform system, and the method comprises: Step M1: In response to the first association instruction, a main product SKU is selected on the product management page, where the main product SKU is the smallest product unit for inventory management; Step M2: in response to the second association instruction, determining an associated product SKU in the product SKU corresponding to the main product SKU, wherein the product SKU is the smallest product unit operated by the store, and an information synchronization relationship exists between the associated product SKU and the main product SKU; Step M3: in response to the preset information modification instruction, perform a modification operation on the product information of the main product SKU to obtain target product information; Step M4: When it is detected that the product information of the main product SKU has changed, a modification operation is performed on the product information of the associated product SKU based on the target product information to obtain the target product information displayed on the e-commerce sales page.

[0014] Other features and technical effects of this application are described in the following part of the specification. The technical problem solving ideas and related product design solutions of this application are: The applicant found that since product SKU is the smallest unit of inventory management and is used to manage product inventory, while product SKU is the smallest product unit of store operation and is used to display product information to buyers on the e-commerce sales page, and one product SKU usually corresponds to multiple product SKUs (that is, products sold in multiple stores refer to the same product inventory), product SKU can be used as an information medium for batch editing of product SKUs of each store.

[0015] On this basis, the applicant proposed that by editing the product information of the product SKU and synchronizing the edited product information to the corresponding product SKU, the purpose of batch editing the product information of the product SKU of each store can be achieved, avoiding the process of seller users having to manually edit and adjust multiple product SKUs.

[0016] It should be noted that in the practice of e-commerce operations, within the e-commerce sellers, inventory management personnel and store operations personnel are independent of each other in the management structure, and therefore have independent operating permissions. In this solution, batch editing of product information of product SKUs of each store belongs to the store operation module, but the editing process involves product SKUs, so directly adjusting or maintaining information on product SKUs may cause additional information redundancy for inventory management personnel. In addition, product SKU information is usually relatively fixed and not adjusted frequently. It is only used by inventory management personnel in warehouse operations (such as picking, packing, etc.), while product SKUs are used for store operations (such as batch adjustment of prices, batch editing of product information, etc.), and the frequency of changes is higher. If the management permissions of the two overlap, it will also affect the daily operations of inventory management personnel.

[0017] Therefore, the applicant further proposed that before executing the above-mentioned information synchronization operation for the product SKU, the seller user selects the main product SKU from multiple product SKUs, and the main product SKU has information synchronization requirements and is used to perform information synchronization operations. The store operator has the editing authority for the main product SKU, and only has the viewing authority for other product SKUs that have not been selected, thereby achieving the purpose of distinguishing management permissions.

[0018] Then, the seller user selects the associated product SKU that has an information synchronization relationship with the main product SKU from the multiple product SKUs corresponding to the main product SKU. It can be understood that the purpose of the selection process of the associated product SKU is to carry out refined management of the product SKUs for batch editing of product information. For example, when the seller user changes the operation strategy, by modifying the price or display information of the product SKU of some stores as a test, he does not want to adjust all the product SKUs corresponding to the main product SKU to avoid excessive testing costs; for example, when the multiple product SKUs corresponding to the main product SKU are managed by two or more independent store operators / teams, performing batch modification of product information operations on multiple associated product SKUs can avoid interference and conflict in management among independent store operators / teams, further improving the flexibility of store management.

[0019] In this way, the information generation method based on main product linkage provided by this application can accurately and quickly batch modify the online product information of stores on each platform while ensuring that the store management authority is clear and orderly, thereby improving store operation efficiency.

[0020] Furthermore, the present application also provides a gift management method for an e-commerce store, which is used in an order module of an e-commerce ERP system or an e-commerce platform system, and the method includes: Step N1: Displaying gift rule information based on a rule setting page, the rule setting page including a first interaction area and a second interaction area, the first interaction area and the second interaction area are independent of each other, and the first interaction area is larger than the second interaction area in area; the first interaction area is used to set the rule effectiveness conditions, the rule effectiveness conditions include platform stores, commodity SKUs and product SKUs, and the second interaction area is used to set the gift distribution method; Step N2: In response to a first trigger operation on the first interactive area, selecting a condition option corresponding to the rule effectiveness condition; and Step N3: In response to the second trigger operation on the second interactive area, determining a gift distribution method, wherein the gift distribution method includes a gift type and a gift quantity; Step N4: Generate gift rules based on the condition options and the gift distribution method; Step N5: Obtain order data, compare the order data with the gift rules, and execute the gift distribution operation according to the comparison result.

[0021] Other features and technical effects of this application are described in the following part of the specification. The technical problem solving ideas and related product design solutions of this application are: The applicant discovered that based on the characteristics of the e-commerce ERP system connecting to multiple platform stores, the gift rules of multiple platform stores can be uniformly set and managed, thereby improving the management efficiency of gift management.

[0022] On this basis, the applicant proposed to build a rule setting page based on the e-commerce ERP system, through which the gift rules applied to multi-platform stores are set. Specifically, the rule setting page includes a first interactive area and a second interactive area, the first interactive area and the second interactive area are independent of each other, and the first interactive area is larger than the second interactive area in area.

[0023] Among them, the first interactive area is used to set the effective conditions of the rules. Users perform interactive operations with the first interactive area and customize the effective conditions of the gift rules from multiple dimensions such as platform stores (a comprehensive setting item of platform dimensions and store dimensions, where users select e-commerce platforms and stores operating on e-commerce platforms as the effective conditions of gift rules), product SKU (i.e., the smallest commodity unit for inventory management) and product SKU (i.e., the smallest product unit for store operations). Since setting the effective conditions of gift rules is the main part of setting gift rules, the area of ​​the first interactive area is relatively large.

[0024] It should be noted that the above-mentioned commodity SKU and product SKU are independent rule effectiveness conditions in order to adapt to the actual e-commerce operation model. For example, some sellers only manage stores in the e-commerce ERP system without involving warehouses, so they can set gift rules based on the product SKU dimension; similarly, if sellers manage stores and warehouses at the same time based on the e-commerce ERP system, they can set gift rules based on the commodity SKU or product SKU dimension according to the inventory management situation.

[0025] Then, the second interactive area is used to set the gift distribution method, such as the gift type and gift quantity, etc. Since the setting space and flexibility of the gift distribution method are relatively low, the area of ​​the second interactive area is relatively small.

[0026] When the user interacts with the first interactive area and sets the conditions for the effectiveness of the gift rules, and interacts with the second interactive area and sets the gift distribution method, the e-commerce ERP system integrates the gift rule effectiveness conditions and the gift distribution method to generate a complete gift rule.

[0027] Finally, in actual operation, when the buyer places an order, the e-commerce ERP system obtains the order data on the e-commerce platform, and compares the order data with multiple pre-set gift rules in turn, then determines the gift based on the comparison results, and adds the gift to the order's shipping package to complete the gift distribution operation.

[0028] In this way, the gift management method for e-commerce stores provided by the present application can uniformly set and manage the gift rules applied to multi-platform stores based on the rule setting page, which can avoid the independence of the gift rules of each platform store, thereby improving the setting and management efficiency of the gift rules; and, based on the first interactive area and the second interactive area of ​​the rule setting page, respectively set the gift rule effectiveness conditions and the gift distribution method, wherein the rule effectiveness conditions contained in the first interactive area set the commodity SKU and product SKU condition options according to the e-commerce operation scenario, so that the gift rule setting process has extremely high flexibility and convenience, ensuring that the gift rules can meet the operational needs of various e-commerce scenarios.

[0029] The present application also provides a server, which includes a memory and a processor. The system in the present application is stored in the memory, and the processor can run the operating instructions of each method step of the present application.

[0030] The present application also provides a computer device, which includes a memory and a processor. The system in the present application is stored in the memory, and the processor can execute the operating instructions of each method step in the present application.

[0031] refer to Figure 1 The e-commerce ERP system of this application includes one or more functional modules such as commodity module, sales module, procurement module, logistics module, warehouse module, financial module, advertising module, customer service module, tool module, authority management module and order module. The functional modules can be integrated with each other or exist independently, and one functional module can be a submodule of another functional module; the functional modules of this application can also be set on other e-commerce management systems (such as e-commerce platforms) to manage the stores of this e-commerce management system or other e-commerce management systems. Users of the ERP system of this application can also be called store managers, sellers, operators, operators, etc., and their identities are not strictly limited unless otherwise stated. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide further understanding of the present application and do not constitute a limitation of the present application; the contents shown in the accompanying drawings may be real data of the embodiments and belong to the protection scope of the present application.

[0033] Figure 1 This is a schematic diagram of the functional modules of an e-commerce ERP system in one embodiment of the present application.

[0034] Figure 2 This is a schematic diagram of the process of determining safety stock by an e-commerce ERP system in one embodiment of the present application.

[0035] Figure 3 This is a flowchart of a method for determining inventory based on historical sales in one embodiment of the present application.

[0036] Figure 4 This is a schematic diagram of the process of batch modifying product information of product SKUs in an e-commerce ERP system in one embodiment of the present application.

[0037] Figure 5 This is a flowchart of an information generation method based on main product linkage in one embodiment of the present application.

[0038] Figure 6 This is a schematic diagram of the effect of the rule setting page in an embodiment of the present application.

[0039] Figure 7 This is a flow chart of a gift management method for an e-commerce store in one embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of this application more clear, the following is a further detailed description of the embodiments of this application through specific implementation methods combined with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0041] Figure 2 A schematic diagram of the process of determining safety stock by an e-commerce ERP system in one embodiment of the present application is shown. Figure 2 As shown, the warehouse module of the e-commerce ERP system obtains the historical sales data of the target product from the background database, and calculates the daily average sales corresponding to each preset time period based on the historical sales data, such as the average sales of the product corresponding to the past three days (daily average sales 1), the average sales of the product corresponding to the past seven days (daily average sales 2), and the average sales of the product corresponding to the past fifteen days (daily average sales 3). Then, the e-commerce ERP system determines the sales change trend of the target product based on the daily average sales corresponding to each preset time period; wherein, when the sales change trend is an upward trend, the e-commerce ERP system tends to determine the safety stock based on the recent sales data, at which time the average sales of the product corresponding to the past three days (daily average sales 1) corresponds to the first weight; when the sales change trend is a downward trend, the e-commerce ERP system tends to determine the safety stock based on the long-term sales data, at which time the average sales of the product corresponding to the past three days (daily average sales 1) corresponds to the second weight, and the first weight is greater than the second weight. Finally, the e-commerce ERP system calculates the predicted daily sales of the target product in the future replenishment cycle based on the weighted daily average sales, and then determines the safety stock of the target product.

[0042] Figure 3 A method for determining inventory based on historical sales in one embodiment of the present application is shown, and the method mainly includes the following steps S1 to S5.

[0043] The following is a detailed description of each step in the inventory determination method based on historical sales.

[0044] Step S1: Obtain historical sales data of the target product.

[0045] Specifically, the warehouse module of the e-commerce ERP system obtains the historical sales data of the target product based on the backend database.

[0046] Step S2: Based on the historical sales data, the daily average sales corresponding to each preset time period is calculated, and each preset time period is a historical time period of different lengths from the current time.

[0047] For example, each preset time period includes the past 3 days, the past 7 days, the past 15 days, and the past 30 days, and the average daily sales include the average sales of the target product in the past 3 days, the past 7 days, the past 15 days, and the past 30 days.

[0048] Step S3: Determine the change trend of the historical sales data based on the daily average sales, where the change trend includes an upward trend and a downward trend.

[0049] For example, based on the target product's average sales volume over the past three days, the past seven days, the past 15 days, and the past 30 days, determine whether the sales volume trend of the target product is rising or falling, and then determine whether to set a higher weight for the target product's recent sales volume data to determine the safety stock.

[0050] Step S4: Determine the weight corresponding to the daily average sales volume based on the changing trend, wherein the historical time period includes a first time period with the shortest duration. When the changing trend is the rising trend, the daily average sales volume of the first time period corresponds to a first weight. When the changing trend is the falling trend, the daily average sales volume of the first time period corresponds to a second weight. The first weight is greater than the second weight.

[0051] Specifically, when the trend of historical sales data is an upward trend, a higher weight is set for the recent sales data of the target product, such as the average sales of the past 3 days, which tends to increase the predicted daily sales and safety stock, thereby ensuring that the product inventory can meet the increase in market demand.

[0052] When the trend of historical sales data is downward, a lower weight is set for the recent sales data of the target product, such as the average sales of the past three days. At this time, more emphasis is placed on long-term sales data, and there is a tendency to maintain or slightly reduce the existing forecast daily sales and safety stock, so as to avoid excessive reduction of product inventory and the risk of failure to meet market demand.

[0053] The first time period may be determined based on the shortest replenishment cycle or the shortest sales cycle.

[0054] Step S5: Calculate the predicted daily sales based on the weighted average daily sales, and determine the safety stock corresponding to the target product based on the predicted daily sales. The safety stock is used to determine whether the current stock of the target product needs to be replenished.

[0055] Specifically, the e-commerce ERP system calculates the predicted daily sales based on the weighted daily average sales, such as the average sales of the past 3 days with a weight of 30%, the average sales of the past 7 days with a weight of 20%, the average sales of the past 15 days with a weight of 30%, and the average sales of the past 30 days with a weight of 20%, and calculates the predicted daily sales of the target product in the future, such as the next replenishment cycle. Then, according to the equation: safety stock = procurement arrival time * predicted daily sales, the safety stock of the target product is calculated. Among them, procurement arrival time refers to the time when the target product reaches the warehouse after being purchased from the supplier and reaches the saleable state through logistics.

[0056] Furthermore, in one embodiment, the weight corresponding to the daily average sales volume is negatively correlated with the length of the preset time period. Specifically, this embodiment achieves the purpose of smoothing data by assigning different weights to the average sales volume in each preset time period, with a higher weight set for recent data and a gradually decreasing weight set for long-term data. For example, the weight of the average sales volume in the past 3 days is 40%, the weight of the average sales volume in the past 7 days is 30%, the weight of the average sales volume in the past 15 days is 15%, and the weight of the average sales volume in the past 30 days is 15%.

[0057] Furthermore, in one embodiment, before step S2, the method further includes the following steps S6.1 to S6.3.

[0058] Step S6.1: determining a target sales period and a non-target sales period in the sales time period corresponding to the historical sales data, wherein the target sales period has a higher average sales volume than the non-target sales period; Step S6.2: Set a first de-weighting factor for the sales data corresponding to the target sales period, and set a second de-weighting factor for the sales data corresponding to the non-target sales period, wherein the first de-weighting factor and the second de-weighting factor are both constants less than 1, and the first de-weighting factor is less than the second de-weighting factor.

[0059] Step S6.3: Correcting the historical sales data based on the first de-weighting factor and the second de-weighting factor to calculate the daily average sales volume based on the corrected historical sales data.

[0060] Specifically, the target sales period and the non-target sales period are mainly used in situations where there is a significant difference in the sales volume of certain commodities (such as seasonal commodities) in a specific time period, so as to achieve the effect of smoothing data, so as to more accurately judge the changing trend of commodity sales volume. For example, the sales volume of seasonal commodities is higher in the target sales period (i.e., the commodities are in the peak season), and lower in the non-target sales period (i.e., the commodities are in the off-season); by setting corresponding de-weighting factors for the sales volume data of the target sales period and the non-target sales period, the sales volume data of seasonal commodities tends to be smoothed. That is, by simply applying the data attenuation method, it is ensured that the changing trend of commodity sales volume is judged based on the overall sales volume of seasonal commodities, and then the weight corresponding to the daily average sales volume is determined according to the sales volume changing trend.

[0061] For example, assuming that the seasonal product is a Christmas tree, the period from November to January is the target sales period, and February to October is the non-target sales period. The reduction factor of the Christmas tree sales x during the period from November to January is set to 0.2, and the reduction factor of the Christmas tree sales y during the period from February to October is set to 0.8.

[0062] Further, based on the above embodiment, step S6.1 includes the following steps S6.11 and S6.12.

[0063] Step S6.11: Compare the target product with the preset product type; Step S6.12: If the target product meets the preset product type, then in the sales time period corresponding to the historical sales data, a target sales period and a non-target sales period are determined, and the target sales period has a higher average sales volume than the non-target sales period.

[0064] Specifically, seller users pre-set and store product types with phased sales characteristics (such as seasonal products, holiday products, or certain products that are marketed and promoted during a specific time period) in the backend database of the e-commerce ERP system.

[0065] When the target product needs to be automatically replenished or have safety stock set, the e-commerce ERP system will compare the target product with the above-mentioned product types. If the target product meets the product type, the time period when the sales volume of the target product is significantly higher will be set as the target sales period, and other time periods except the target sales period will be set as non-target sales periods.

[0066] As a feasible implementation method, seller users can pre-set target sales periods and non-target sales periods corresponding to product types; when the target product meets the product type, the e-commerce ERP system automatically determines the corresponding target sales period and non-target sales period without going through the sales data analysis stage.

[0067] Further, based on the above embodiment, after step S6.11, the method further includes the following steps S6.13 and S6.14.

[0068] Step S6.13: Based on the preset commodity type, determine a target time period corresponding to the target commodity, where the target time period is a preset time period that characterizes a sales volume change trend of the target commodity; Step S6.14: Analyze the target time period, and generate a sales warning corresponding to the target product according to the analysis result.

[0069] Specifically, different types of goods should determine which time period should be focused on based on their life cycle, sales seasonality, market strategy and other factors. For example: Fast-moving consumer goods: focus on recent time periods such as 3 days and 7 days to adjust inventory and procurement plans in a timely manner. Seasonal goods: such as holiday gifts, you may need to focus on the time period before a specific holiday, such as 30 days, 60 days, etc., to predict and prepare for seasonal peaks. Long-term sales goods: such as daily consumer goods, you may need to focus on sales in longer time periods such as 30 days, 60 days, 90 days, etc., to conduct quarterly or semi-annual sales analysis and forecasts.

[0070] Therefore, the e-commerce ERP system determines the target time period corresponding to the target product based on the product type corresponding to the target product, and generates a corresponding sales warning when the sales volume reaches a preset range within the target time period.

[0071] For example, when the average sales volume of fast-moving consumer goods in the past three days (target time period) increases or decreases by 30% (preset range), a corresponding sales warning will be generated to prompt sellers to adjust their inventory and purchasing plans in a timely manner.

[0072] Further, based on the above embodiment, before step S6.3, the method further includes: Step S6.41: Compare the current inventory of the current product with the preset inventory; Step S6.42: adjusting the first weight reduction factor and the second weight reduction factor according to the comparison result.

[0073] In this embodiment, the e-commerce ERP system adjusts the first and second de-weighting factors according to the current inventory level of the target product, and then adjusts the judgment of the sales volume change trend and the weight of the daily average sales volume. Specifically, if the current inventory of the product is higher than the preset inventory, that is, the product inventory is large and the product backlog is serious, then the first de-weighting factor is reduced and the second de-weighting factor is increased, thereby reducing the safety stock; if the current inventory of the product is lower than the preset inventory, that is, the product inventory is small and the product is out of stock, then the first de-weighting factor is increased and the second de-weighting factor is reduced, thereby increasing the safety stock.

[0074] Further, based on the above embodiments, before step S6.3, the method further includes: Step S6.51: Analyze the sales volume data corresponding to multiple target sales periods respectively to obtain the increase rate of the sales volume data corresponding to the target sales period; Step S6.52: Adjust the first weight reduction factor and the second weight reduction factor according to the increase rate of the sales volume data.

[0075] In this embodiment, the e-commerce ERP system analyzes the sales volume increase of the target product during the target sales period, and then adjusts the first weight reduction factor and the second weight reduction factor according to the sales volume increase. For example, assume that the target product is a Christmas tree, and the period from November to January is the target sales period; the e-commerce ERP system analyzes the sales volume data of the Christmas trees in the store during the period from November to January in the past five years, and obtains the sales volume increase of the Christmas trees during the period from November to January. When the sales volume increase reaches a preset range, the e-commerce ERP system increases the first weight reduction factor and decreases the second weight reduction factor, so as to adjust the safety inventory of the target product, the Christmas tree, in a timely manner according to the market changes over the years.

[0076] Further, in one embodiment, step S3 includes steps S3.1 to S3.3 as follows.

[0077] Step S3.1: Classify each preset time period into a recent time period and a long-term time period according to a preset duration parameter; Step S3.2: When the daily average sales volume of the recent time period is greater than that of the long-term time period, determine that the change trend is an upward trend; and Step S3.3: When the daily average sales volume of the recent time period is less than that of the long-term time period, determine that the change trend is a downward trend.

[0078] This embodiment provides a specific implementation manner for judging the change trend of the sales volume of the target product. Specifically, in this embodiment, each preset time period is classified based on a preset duration parameter: for example, assume that the preset duration parameter is 10 days, and each preset time period includes: the past 3 days, the past 7 days, the past 15 days, and the past 30 days; then the past 3 days and the past 7 days are classified as the recent time period, and the past 15 days and the past 30 days are classified as the long-term time period. Then, the recent sales volume data (the sum of the average sales volume Avg3 in the past 3 days and the average sales volume Avg7 in the past 7 days) is compared with the long-term sales volume data (the sum of the average sales volume Avg15 in the past 15 days and the average sales volume Avg30 in the past 30 days). When Avg3 + Avg7 > Avg15 + Avg30, the sales volume change trend is an upward trend; when Avg3 + Avg7 < Avg15 + Avg30, the sales volume change trend is a downward trend. In this way, the accuracy of judging the sales volume change trend can be improved.

[0079] Further, in one embodiment, step S5 includes the following steps S5.1 to S5.4.

[0080] Step S5.1: Obtain unstructured data contained in a preset data source set, wherein the unstructured data includes social hot topics, search engine trends, and third-party market research reports; Step S5.2: Calculate the predicted daily sales volume corresponding to each sales region based on the unstructured data and the weighted daily average sales volume; Step S5.3: Obtaining supply chain data based on a preset blockchain network, and analyzing the supply chain data; Step S5.4: Determine the safety stock corresponding to the target product based on the analysis result and the predicted daily sales volume, wherein the safety stock is used to determine whether the current stock of the target product needs to be replenished.

[0081] The applicant found that determining the safety stock of the target product based on historical sales data still cannot adjust the safety stock of the target product in a timely manner, resulting in occasional out-of-stock situations of the target product.

[0082] The reason is that the above-mentioned method of setting the safety stock only refers to the historical sales data of the target product, that is, the e-commerce operating environment needs to be reflected in the sales data only after the transaction behavior of the target product occurs; since the reference data for setting the safety stock is single, it fails to fully combine the current operating environment and future events that may affect the sales trend of the product, resulting in a considerable lag in the inventory setting of the target product.

[0083] On this basis, the applicant proposed that the setting and adjustment of safety stock can be more accurate and timely by combining diversified unstructured data based on the unstructured data contained in the preset data source set. Among them, unstructured data includes social hot topics, search engine trends, and third-party market research reports.

[0084] Among them, social hot topics can reflect the topics or things that society is currently paying attention to, and then involve related products. For example, if a movie is hotly discussed on a social platform, the peripheral products of the protagonist of the movie may have a large market demand in the future. For another example, search engine trends can also reflect the topics or things that society is currently paying attention to. These topics or things may bring traffic to related products, thereby increasing the market demand for related products. In addition, for third-party market research reports provided by certain institutions, if the institution has a high degree of credibility and the market research report has a certain degree of professionalism and accuracy, the market research report can also be used as reference data for e-commerce companies to purchase, replenish, and set safety stocks.

[0085] It should be noted that the above-mentioned safety stock setting method is affected by many factors. Therefore, in actual application, there may be deviations in the predicted daily sales, errors in the safety stock setting, and low inventory of goods in some warehouses. At this time, these warehouses need to temporarily transfer goods from other warehouses for replenishment, which will affect the inventory of other warehouses. In addition, the inventory of goods in each warehouse is also affected by suppliers and logistics methods.

[0086] Therefore, the applicant further proposed to build a distributed ledger network based on blockchain technology, and e-commerce enterprises, e-commerce ERP systems, suppliers, logistics companies, regulatory agencies and other related parties joined the network as nodes. Select a suitable blockchain platform (such as Ethereum, Hyperledger Fabric, etc.), customize blockchain smart contracts according to actual business needs, and specify the rules for data storage, query, verification and update.

[0087] In this distributed ledger network, e-commerce companies can upload product operation plans, e-commerce ERP systems can upload operation suggestions and alarm information, suppliers can upload supply product inventory, logistics companies can upload product logistics status, and the product inventory status of each warehouse can also be shared in real time, achieving the effect of multi-party collaborative operation and transparent management, thereby avoiding deviations in the setting of safety stock due to untimely logistics, and warehouses requisitioning goods for replenishment while the warehouse's safety stock cannot be adjusted in time, effectively improving the operational efficiency of e-commerce companies.

[0088] In this way, this embodiment combines diversified unstructured data to make the setting and adjustment of safety stock more accurate and timely; and by building a distributed ledger network based on blockchain technology, it achieves the effect of collaborative operation and transparent management among e-commerce companies, e-commerce ERP systems, suppliers, logistics companies, regulatory agencies, etc., thereby combining the influencing factors of e-commerce operations to the greatest extent, adjusting the safety stock of goods in a timely manner, and effectively improving the operational efficiency of e-commerce companies.

[0089] In one embodiment, based on daily e-commerce activities, sellers often need to manually adjust the display information and prices of products one by one, which is inefficient, cumbersome, and error-prone. The present application also provides an information generation method based on main product linkage.

[0090] Figure 4 The following is a schematic diagram showing the process of batch modifying product SKU product information in an e-commerce ERP system in one embodiment of the present application. Figure 4As shown, the seller user interacts with the product management page of the e-commerce ERP system to trigger the first association instruction. The product module responds to the first association instruction and selects the main product SKU from multiple product SKUs, and then determines multiple product SKUs corresponding to the main product SKU. Then, the seller user triggers the second association instruction to determine the associated product SKU from multiple product SKUs, and the associated product SKU is used to synchronize with the main product SKU information. When the seller user triggers the information modification instruction and modifies the main product SKU to generate the target product information, the e-commerce ERP system batch modifies the product information of the associated product SKU according to the target product information, and generates the target product information displayed on the product sales page.

[0091] Figure 5 An information generation method based on main product linkage in one embodiment of the present application is shown, and the method mainly includes the following steps M1 to M4.

[0092] Step M1: In response to the first association instruction, a main product SKU is selected on the product management page, where the main product SKU is the smallest product unit for inventory management.

[0093] Specifically, product SKU is the smallest unit of inventory management and is used to manage product inventory. One product SKU usually corresponds to multiple product SKUs (i.e., products sold in multiple stores refer to the same product inventory). The seller user triggers the first association instruction based on the product management page and selects the product SKU that needs to perform batch modification of information from multiple product SKUs as the main product SKU.

[0094] Step M2: In response to the second association instruction, determine the associated product SKU in the product SKU corresponding to the main product SKU, the product SKU is the smallest product unit of the store operation, and the associated product SKU has an information synchronization relationship with the main product SKU.

[0095] Specifically, product SKU is the smallest product unit of store operation, which is used to display product information to buyers on the e-commerce sales page. The seller user triggers the second association instruction based on the product management page, and selects the product SKU that needs to perform batch modification of information as the associated product SKU from the multiple product SKUs corresponding to the main product SKU.

[0096] Step M3: In response to the information modification instruction, a modification operation is performed on the product information of the main product SKU to obtain target product information.

[0097] Specifically, the seller user triggers an information modification instruction based on the product management page, and performs a modification operation on the product information of the main product SKU, such as modifying the product price, product title, or product description information of the main product SKU, to obtain the modified product information, that is, the target product information.

[0098] Step M4: When it is detected that the product information of the main product SKU has changed, a modification operation is performed on the product information of the associated product SKU based on the target product information to obtain the target product information displayed on the e-commerce sales page.

[0099] Specifically, the e-commerce ERP system detects that the product information of the main product SKU has changed. According to the modified product information, that is, the target product information, the product information of the associated product SKUs of each e-commerce platform and each store is modified to obtain the target product information, thereby avoiding the seller user's manual operation process of performing information modification on multiple product SKUs.

[0100] Further, in one embodiment, step M4 includes the following step M4.1 and step M4.2.

[0101] Step M4.1: When it is detected that the product information of the main product SKU has changed, a platform adjustment strategy is determined based on the e-commerce platform where the associated product SKU is located; Step M4.2: The target product information is adjusted based on the platform adjustment strategy, and the product information of the associated product SKU is modified according to the adjustment result to obtain the target product information displayed on the e-commerce sales page.

[0102] Specifically, the e-commerce ERP system can directly synchronize the target product information of the main product SKU, such as product price and product description information, to the associated product SKU, so that the target product information is completely consistent with the target product information.

[0103] However, since the operation modes of various e-commerce platforms are different, for example, e-commerce platform A focuses on product brand and quality, while product platform B focuses on product price performance, sellers can adjust the product information of store product SKU according to the operation mode of the e-commerce platform, that is, display different target product information on e-commerce platform A and e-commerce platform B. In this embodiment, the seller pre-sets the platform adjustment strategy corresponding to the e-commerce platform. When the e-commerce ERP system detects that the product information of the main product SKU has changed, it will automatically process the target product information according to the platform adjustment strategy corresponding to the e-commerce platform, and display the processing result based on the store-related product SKU of the e-commerce platform.

[0104] Further, based on the above embodiment, the platform adjustment strategy includes a price strategy, the target commodity information includes first price information, the target product information includes second price information, and the step M4.2 includes the following steps M4.21 and M4.22.

[0105] Step M4.21: adjusting the first price information based on the price ratio included in the price adjustment strategy; Step M4.22: Perform a modification operation on the product information of the associated product SKU according to the adjustment result to obtain the second price information displayed on the e-commerce sales page.

[0106] Specifically, the seller sets the price ratio between the associated product SKU and the main product SKU of each e-commerce platform, so that the e-commerce ERP system can automatically generate prices corresponding to each associated product SKU in batches.

[0107] For example, after the seller sets the price of the main product SKU, the e-commerce ERP system automatically converts the price of the product SKUs of different sites (countries) according to the preset fixed exchange rate. In addition, the seller can also set the price ratio rules between different platforms (such as A e-commerce platform: B e-commerce platform = 1:1.3, when the product price of A e-commerce platform is 100, the price of the same product on B e-commerce platform is set to 130), thereby improving the flexibility of product price management.

[0108] Further, based on the above embodiment, the platform adjustment strategy includes a display strategy, the target commodity information includes first display information, the target product information includes second display information, and step M4 includes the following steps M4.23 and M4.24.

[0109] Step M4.23: Optimizing the expression of the first display information based on the display strategy; Step M4.24: Perform a modification operation on the product information of the associated product SKU according to the optimization result to obtain second display information displayed on the e-commerce sales page.

[0110] Specifically, the seller sets up the dialogue between the associated product SKUs and the main product SKUs of each e-commerce platform, so that the e-commerce ERP system can automatically batch generate product description information corresponding to each associated product SKU.

[0111] For example, after the seller sets the information describing the product attributes in the main product SKU (first display information), the e-commerce ERP system automatically modifies the first display information according to the pre-set marketing phrases corresponding to each e-commerce platform, thereby generating second display information suitable for different e-commerce platforms, that is, the second display information contains the marketing phrases corresponding to each e-commerce platform (such as the second display information of e-commerce platform A contains marketing phrases that reflect the product's cost-effectiveness, and the second display information of e-commerce platform B contains marketing phrases that reflect the high-end nature of the product), or reflects the language style corresponding to each e-commerce platform (such as the second display information of e-commerce platform A is presented in a lively and vivid language style, and the second display information of e-commerce platform B is presented in a serious and accurate language style), thereby improving the flexibility of editing product display information.

[0112] Further, based on the above embodiment, step M4.24 includes the following step M4.241 and step M4.242.

[0113] Step M4.241: modify the product information of the associated product SKU according to the optimization result; Step M4.242: Perform a language translation operation on the modification result based on the site where the store is located to obtain second display information displayed on the e-commerce sales page.

[0114] Specifically, the e-commerce ERP system performs automatic intelligent translation based on the site (country) language habits of the store where each related product SKU is located, ensuring that the translated content is more in line with the expression habits of the local language and improving the readability of product display information.

[0115] Furthermore, in one embodiment, after step M4, the method further includes the following step M5.1.

[0116] Step M5.1: Based on the commodity management page, the information modification result of the associated product SKU is displayed, and the information modification result includes the associated product SKU for which the modification fails and the reason for the modification failure.

[0117] Specifically, when the user modifies the product information of the main product SKU on the product management page, the e-commerce ERP system automatically synchronizes the modified target product information to each related product SKU, generates the corresponding target product information, and displays the information modification results on the product management page.

[0118] The information modification results include the associated product SKUs whose product information has been successfully modified and the current status of the associated product SKUs, and the associated product SKUs whose product information has failed to be modified and the reasons for the modification failure. There are many types of reasons for modification failure, including the associated product SKU has been deleted / offline, store authorization has expired, modification method is not compliant, etc. In addition, the product management page will display a one-click update button corresponding to the reason for the modification failure, so that users can choose to let the e-commerce ERP system further optimize and update the associated product SKUs whose information modification failed.

[0119] In one embodiment, after step M1, the method further includes the following step M2.1.

[0120] Step M2.1: Detect the candidate product SKU corresponding to the product name of the main product SKU, and determine the candidate product SKU as the associated product SKU.

[0121] Specifically, this embodiment provides a method for an e-commerce ERP system to automatically determine associated product SKUs. That is, after the seller chooses to determine the main product SKU, the e-commerce ERP system automatically detects the candidate product SKUs corresponding to the product name of the main product SKU, and determines all the candidate product SKUs as associated product SKUs for performing information synchronization operations, thereby omitting the operation process of the seller selecting the associated product SKU and improving operational efficiency.

[0122] Further, in one embodiment, step M3 includes the following step M3.1 and step M3.2.

[0123] Step M3.1: In response to the information modification instruction, a modification operation is performed on the product information of the main product SKU; Step M3.2: Based on one or more parameters of product category, store positioning, site cultural background, and consumer preference, call the AI ​​model to perform optimization operations on the modification results to obtain target product information.

[0124] Specifically, this embodiment calls the AI ​​model to generate product information of the main product SKU, so as to achieve better marketing effect of the product information of the main product SKU. For example, on the product management page, the seller selects options such as product category, store location, site cultural background, and consumer preferences, or selects relevant AI model prompt words, so that the AI ​​model optimizes the product information pre-set by the seller, or the AI ​​model directly generates product information.

[0125] Furthermore, in one embodiment, after step M1, the method further includes the following steps M6.1 to M6.5.

[0126] Step M6.1: Detecting the preset conversion parameters of the product SKU corresponding to the main product SKU, and determining the product SKU whose preset conversion parameter is lower than the preset conversion threshold as the associated product SKU; Step M6.2: Obtain unstructured data contained in a preset data source set, wherein the unstructured data includes social hot topics, search engine trends, and third-party market research reports; Step M6.3: Generate a test adjustment strategy according to the unstructured data, and perform a modification operation on the product information of the main product SKU based on the test adjustment strategy to obtain test product information; Step M6.4: performing a modification operation on the product information of the associated product SKU based on the test product information to obtain the test product information displayed on the e-commerce sales page; Step M6.5: When it is detected that the preset conversion parameter of the associated product SKU reaches the preset conversion threshold, the information synchronization relationship between the associated product SKU and the main product SKU is eliminated.

[0127] The applicant has found that, in addition to achieving the goal of optimizing the product information of the product SKU by synchronizing the target product information of the main product SKU, the product information of the product SKU corresponding to the main product SKU can also be optimized through testing.

[0128] The reason is that in actual operations, the way product SKU information is presented is affected by many factors, such as product category, store positioning, site cultural background, and consumer preferences. It is difficult for sellers to determine the product information suitable for each product SKU in a short period of time and achieve better marketing results.

[0129] On this basis, the applicant proposed that the e-commerce ERP system detects the preset conversion parameters (such as click-through rate or conversion rate, etc.) of each product SKU corresponding to the main product SKU to determine whether the product SKU has a good presentation effect and is suitable for the current marketing scenario; if the preset conversion parameters of the product SKU are lower than the preset conversion threshold (such as the conversion rate is lower than 10%), it is determined that the presentation effect of the product information of the product SKU is poor and needs further optimization. At this time, the product SKU is automatically determined as an associated product SKU, that is, the product SKU is set to have an information synchronization relationship with the main product SKU.

[0130] Then, the e-commerce ERP system obtains a variety of unstructured data based on a preset data source set (such as a pre-set background database or cloud server), such as social hot topics, search engine trends, and third-party market research reports. This unstructured data is used to characterize the current marketing link, thereby serving as reference data / basis for optimizing the product information of related product SKUs.

[0131] Next, the e-commerce ERP system generates a test adjustment strategy based on the unstructured data, which includes generating corresponding marketing phrases, adjusting language style, or adjusting prices based on the unstructured data. The e-commerce ERP system modifies the product information of the main product SKU based on the test adjustment strategy to obtain the test product information, and synchronizes the test product information to each related product SKU to obtain the test product information displayed on the e-commerce sales page. At this time, each related product SKU enters the test phase.

[0132] Finally, after a period of testing (such as 3 days, 7 days or 30 days, etc.), if the e-commerce ERP system detects that the preset conversion parameters of some related product SKUs have reached the preset conversion threshold (such as the conversion rate of some related product SKUs increased from 5% to 10% after displaying the test product information), it means that the test product information is suitable for these related product SKUs and has a good presentation effect. Therefore, these related product SKUs should continue to use the current test product information, and the e-commerce ERP system cancels the information synchronization relationship between these related product SKUs and the main product SKU. For other related product SKUs whose preset conversion thresholds are still at a low level and have not reached the preset conversion threshold, the e-commerce ERP system continues to generate other test adjustment strategies based on unstructured data, and repeats the test process until the preset conversion parameters of all related product SKUs reach the preset conversion threshold.

[0133] Thus, in this embodiment, for the e-commerce scenario where seller users cannot independently and accurately determine the product information applicable to each product SKU, the e-commerce ERP system detects the product SKUs that currently have poor marketing effects and need to perform product information optimization; based on different test adjustment strategies, the product information of the product SKU is tested and optimized through the information synchronization relationship with the main product SKU until the product information of the product SKU can achieve better presentation and marketing effects, thereby making the product information optimization process of the product SKU highly intelligent, and eliminating the need for sellers to manually adjust the product information of the product SKU, thereby greatly improving the e-commerce operation efficiency.

[0134] Figure 6 FIG. 1 shows a schematic diagram of the effect of the rule setting page in one embodiment of the present application. Figure 6As shown, the seller user interacts with the first interactive area, triggers the first trigger operation, sets the effective conditions in the condition options such as platform store, commodity SKU and product SKU, and the setting results are displayed in the "Set Conditions" area on the right side of the first interactive area. Then, the seller user interacts with the second interactive area, triggers the second trigger operation, sets the number of gifts and gift SKU (i.e., gift type). Finally, the seller user interacts with the third interactive area, sets the association between gifts and inventory, and sets other specific judgment logic. The e-commerce ERP system generates gift rules based on the setting results of the rule setting page, then compares the gift rules with the data of each order, and executes the gift distribution operation based on the comparison results.

[0135] Figure 7 A gift management method for an e-commerce store in one embodiment of the present application is shown, and the method mainly includes the following steps N1 to N5.

[0136] Step N1: Display gift rule information based on the rule setting page, the rule setting page includes a first interaction area and a second interaction area, the first interaction area and the second interaction area are independent of each other, and the first interaction area is larger in area than the second interaction area; the first interaction area is used to set the rule effectiveness conditions, the rule effectiveness conditions include platform stores, commodity SKUs and product SKUs, and the second interaction area is used to set the gift distribution method.

[0137] Specifically, the e-commerce ERP system displays the contents of the gift rules based on the rule setting page, and the user interacts on the rule setting page to set the gift rules. Among them, the rule setting page includes a first interactive area and a second interactive area. The first interactive area is used to set the conditions for the effectiveness of the rules. The user performs interactive operations with the first interactive area, and customizes the effectiveness conditions of the gift rules from multiple dimensions such as platform stores (a comprehensive setting item of the platform dimension and the store dimension. The user selects the e-commerce platform and the store operating on the e-commerce platform as the effectiveness conditions of the gift rules), product SKU (that is, the smallest commodity unit for inventory management) and product SKU (that is, the smallest product unit for store operation). Since setting the effectiveness conditions of the gift rules is the main part of setting the gift rules, the area of ​​the first interactive area is relatively large.

[0138] The second interactive area is used to set the gift distribution method, such as the gift type and quantity of the gift distributed. For example, the gift distribution method includes giving away y a gift SKUs. Since the setting space and flexibility of the gift distribution method are relatively low, the area of ​​the second interactive area is relatively small.

[0139] Step N2: In response to a first trigger operation on the first interactive area, a condition option corresponding to the rule effectiveness condition is selected.

[0140] Specifically, for the interaction in the first interactive area, the seller user sets the effective conditions of the gift rule in multiple dimensions such as platform store, product SKU and product SKU. For example, the seller user can set the purchase of product SKU A in store N1 on the M1 platform as the effective condition 1 of the gift rule; and the purchase of product SKU B in store N2 on the M2 platform as the effective condition 2 of the gift rule.

[0141] Step N3: In response to the second trigger operation on the second interactive area, determine a gift distribution method, where the gift distribution method includes a gift type and a gift quantity.

[0142] Specifically, the seller user interacts with the second interactive area, sets the gift type by setting the gift SKU, and sets the number of gifts to be distributed when the order data meets the effective conditions of the gift rule. For example, the seller user can set the purchase of x A product SKUs and y a gift SKUs.

[0143] Step N4: Generate gift rules based on the condition options and the gift distribution method.

[0144] Specifically, after the seller user completes the gift effectiveness conditions and gift distribution methods on the rule setting page, the e-commerce ERP system integrates the data on the rule setting page to generate corresponding gift rules.

[0145] Step N5: Obtain order data, compare the order data with the gift rules, and execute the gift distribution operation according to the comparison result.

[0146] Specifically, after the buyer places an order, the e-commerce ERP system obtains order data based on the e-commerce platform, and then compares the order data with each gift rule in turn to determine the gift rule whose order data meets the effectiveness conditions and has the highest priority, and determines the gift type and quantity based on the gift rule, and finally executes the gift distribution operation.

[0147] Further, in one embodiment, step N5 includes the following steps N5.1 to N5.3.

[0148] Step N5.1: Obtain order data and the priority of the gift rules; Step N5.2: According to the priority, the order data is compared with each of the gift rules in sequence; Step N5.3: Determine the target gift rules that match the order data, and perform the gift distribution operation according to the target gift rules.

[0149] Specifically, sellers interact with users on the rule setting page and set serial numbers for multiple gift rules, which are used to determine the priority of the gift rules. When the e-commerce ERP system obtains the order data, it compares the order data with each gift rule in order from small to large according to the serial number; if it is determined that the order data meets a gift rule, the comparison process is terminated and the gift distribution operation is executed.

[0150] For example, there are 10 gift rules with serial numbers 1-10. The e-commerce ERP system will compare the order data with the 10 gift rules in sequence according to the serial numbers. If it is determined that the order data hits the gift rule with serial number 2, the gift rules with serial numbers 3-10 will no longer be compared, but the gift distribution operation will be performed according to the gift rule with serial number 2.

[0151] In this way, this embodiment further improves the accuracy of gift rule management by setting priorities for gift rules and limiting the comparison process of gift rules and order data based on priorities, and avoids human errors that cause order data to hit multiple gift rules and thus excessive distribution of gifts.

[0152] Furthermore, in one embodiment, the method further includes the following step N6.1 and step N6.2.

[0153] Step N6.1: Displaying a plurality of gift rules based on a rule management page, wherein the rule management page includes an edit interaction button and a log interaction button, wherein the edit interaction button is used to switch between the rule management page and the rule setting page; Step N6.2: In response to the third trigger operation on the log interaction button, the rule management page is switched to the log change page, and the log change page is used to display the editing record of the gift rule.

[0154] Specifically, the rule management page displays multiple complete gift rules, each of which has a corresponding edit interaction button and log interaction button. When the seller user clicks the edit interaction button, the rule management page switches to the rule setting page for editing the gift rule; when the seller user clicks the edit interaction button, the rule management page switches to the log change page, which is used to display the operation record of the gift rule.

[0155] Furthermore, based on the above embodiment, the method also includes the following step N6.3 and step N6.4.

[0156] Step N6.3: Analyze the order data to determine a target time period in which the order quantity reaches a first quantity threshold; Step N6.4: Generate a first adjustment policy corresponding to the target time period, and display the first adjustment policy on the log change page.

[0157] Specifically, for certain time periods with a large number of orders (i.e., target time periods), the e-commerce ERP system generates a corresponding adjustment strategy and displays the adjustment strategy on the log change page to prompt seller users to increase the number of gifts or increase the value of gifts, thereby stimulating potential customers' desire to buy and improving marketing effectiveness.

[0158] It is understandable that for certain time periods with a smaller number of orders, corresponding adjustment strategies can also be generated to prompt sellers to increase the number of gifts, increase the value of gifts, or change the type of gifts, so as to improve sales in that time period.

[0159] Further, based on the above embodiment, after analyzing the order data in step N6.3, the method also includes the following steps N6.5 and N6.6.

[0160] Step N6.5: Determine the number of orders that match each of the gift rules, and determine the target gift rule whose order quantity is lower than a preset quantity threshold; Step N6.6: Generate a second adjustment strategy corresponding to the target gift rule, and display the second adjustment strategy on the log change page.

[0161] Specifically, the e-commerce ERP system analyzes the number of orders that hit each gift rule. If some gift rules are hit less often, it means that the gift rule (target gift rule) is not effective, for example, the effective conditions do not meet the actual situation, or the gift is too unattractive. At this time, the e-commerce ERP system generates an adjustment strategy corresponding to the target gift rule and displays the adjustment strategy on the log change page, prompting the seller user to adjust the gift type or quantity.

[0162] Furthermore, based on the above embodiment, the method also includes the following steps N7.1 to N7.3.

[0163] Step N7.1: Obtaining the display time of the gift option on the e-commerce platform, where the display time is the duration of the e-commerce platform displaying a preset virtual scene corresponding to the gift option of the target product, where the preset virtual scene is used to show the matching effect of the gift option and the target product; Step N7.2: Determine the target gift option whose display time is higher than the second time threshold; Step N7.3: Generate a preview adjustment strategy corresponding to the target gift option, and display the preview adjustment strategy based on the log change page, wherein the preview adjustment strategy is used to optimize the preset virtual scene of the target gift option.

[0164] In actual e-commerce scenarios, gifts and products on e-commerce platforms are often strongly correlated, especially when products and gifts are paired together, such as Bluetooth headsets and mobile phones, styluses and tablets, pillow gifts and sofas, beauty eggs and foundation liquid, etc. The matching effect between gifts and products will directly affect buyers' purchase inclination to give gifts.

[0165] The applicant found that conventional two-dimensional preview images often cannot fully display the matching effect between gifts and products. Buyers are unable to decide between multiple gifts, which greatly prolongs the decision-making time, thus seriously affecting the product purchase conversion rate.

[0166] Therefore, the applicant proposed that when users select products on the product sales page, the pre-created 3D models and virtual scenes can better demonstrate the matching effect between gifts and products, thereby shortening the decision-making time of buyers when purchasing products and improving conversion rates.

[0167] Then, the applicant further discovered that when the buyer user stayed on the 3D model or virtual scene of the target gift option for a long time to watch, there was a high probability that the display effect of the 3D model or virtual scene was poor, resulting in the buyer user being unable to quickly understand the matching effect between the target gift and the product, and thus unable to make a purchase choice in a short time. On this basis, the applicant proposed that when the e-commerce ERP system detects that the 3D model or virtual scene of the target gift option has a long display time, the e-commerce ERP system determines that the display effect of the 3D model or virtual scene is poor, and generates a corresponding preview adjustment strategy to prompt the seller user to adjust the 3D model or virtual scene.

[0168] In this way, this embodiment displays the matching effect between gifts and commodities through 3D models and virtual scenes, and can promptly remind seller users to adjust 3D models and virtual scenes with poor display effects, thereby shortening the decision time of buyers to purchase commodities and improving the purchase conversion rate.

[0169] Furthermore, in one embodiment, before step N3, the method further includes the following steps N3.1 and N3.2.

[0170] Step N3.1: Analyze the condition options to determine the operation model corresponding to the e-commerce platform and the buyer user profile corresponding to the store; Step N3.2: Determine a recommended gift SKU based on the operation model and the buyer user portrait, and display the recommended gift SKU in the second interactive area.

[0171] Specifically, in actual e-commerce scenarios, each e-commerce platform has its own uniqueness, and there are differences in aspects such as user group characteristics and operating models. Setting corresponding gift types according to the differences in e-commerce platforms can make the gifts more in line with the needs of the e-commerce platforms, thereby enhancing the competitiveness of stores and products on the e-commerce platforms.

[0172] For example, on e-commerce platforms that are dominated by young users, fashionable small items can be set as gifts; on e-commerce platforms that focus on cost-effectiveness, practical household items can be set as gifts, etc. In addition, different gifts can be set according to the style of each store. For example, high-end boutiques can give away exquisite gift packaging or high-end brand trial packs, while mass discount stores can give away small gifts at affordable prices.

[0173] In this embodiment, when the seller user interacts in the first interactive area of ​​the rule setting page and sets the corresponding effective conditions based on the platform store dimension, the e-commerce ERP system analyzes the operating mode contained in the effective conditions and the buyer user portrait corresponding to the store, and then determines the corresponding recommended gift type. Then, when the seller user interacts with the second interactive area and sets the gift type and gift quantity, the e-commerce ERP system displays the recommended gift type in the second interactive area for the seller user to choose.

[0174] Thus, in this embodiment, the e-commerce ERP system generates and displays corresponding recommended gift types based on the e-commerce platforms and stores included in the effective conditions set by the user, thereby improving the accuracy and convenience of seller users in selecting gifts, and is conducive to improving the marketing effect of store products.

[0175] Furthermore, in one embodiment, the rule effectiveness conditions also include the order amount and the order time. Specifically, the order amount dimension: different order amount ranges are set as the effectiveness conditions, so as to achieve precision marketing for customers at different consumption levels. For example, for low-spending customers, gifts can be used to motivate them to increase their spending to obtain more valuable gifts; for high-spending customers, high-quality gifts that match their consumption level are provided to meet their expectations.

[0176] Order time dimension: Considering the influence of time factors such as seasons, festivals, and promotions, sellers can flexibly set different gift conditions within the order time range. This setting method can enhance the timeliness and attractiveness of promotional activities. For example, during specific festivals, gifts related to holiday themes can be launched to stimulate consumers to place orders.

[0177] In addition, seller users can flexibly combine the above-mentioned dimensions such as order amount, order time, store platform, commodity SKU, product SKU, etc. according to their needs and set multiple effective conditions.

[0178] Furthermore, in one embodiment, the rule setting page also includes a third interaction area, and the third interaction area is used to set the association method between gifts and inventory. Specifically, the area of ​​the third interaction area is smaller than the first interaction area and the second interaction area, and is used to supplement the setting of some specific judgment logic, including the association method between gifts and inventory. For example, judgment logic 1 is that gift SKU is not automatically added when the available inventory of gift SKU is insufficient. When the seller user does not check judgment logic 1, if the order hits the gift rule and the gift inventory is insufficient, the e-commerce ERP system will automatically set the gift replenishment plan, and automatically add the gift SKU during the packaging process, and execute the gift distribution operation; if the seller user checks judgment logic 1, the e-commerce ERP system will not execute the gift distribution operation when the gift inventory is insufficient.

[0179] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. All equivalent transformations made by using the contents of the present application specification and drawings under the inventive concept of the present application, or direct / indirect application in the relevant technical field are included in the patent protection scope of the present application.

Claims

1. A method for generating information based on main product linkage, characterized in that: A commodity module for an e-commerce ERP system or an e-commerce platform system, the method comprising: Step M1: In response to the first association instruction, a main product SKU is selected on the product management page, where the main product SKU is the smallest product unit for inventory management; Step M2: in response to the second association instruction, determining an associated product SKU in the product SKU corresponding to the main product SKU, wherein the product SKU is the smallest product unit operated by the store, and an information synchronization relationship exists between the associated product SKU and the main product SKU; Step M3: in response to the information modification instruction, performing a modification operation on the product information of the main product SKU to obtain target product information; Step M4: When it is detected that the product information of the main product SKU has changed, a modification operation is performed on the product information of the associated product SKU based on the target product information to obtain the target product information displayed on the e-commerce sales page.

2. The method according to claim 1, characterized in that Step M4 includes: Step M4.1: When it is detected that the product information of the main product SKU has changed, a platform adjustment strategy is determined based on the e-commerce platform where the associated product SKU is located; Step M4.2: The target product information is adjusted based on the platform adjustment strategy, and the product information of the associated product SKU is modified according to the adjustment result to obtain the target product information displayed on the e-commerce sales page.

3. The method according to claim 2, characterized in that The platform adjustment strategy includes a price strategy, the target commodity information includes first price information, the target product information includes second price information, and the step M4.2 includes: Step M4.21: adjusting the first price information based on the price ratio included in the price adjustment strategy; Step M4.22: Perform a modification operation on the product information of the associated product SKU according to the adjustment result to obtain the second price information displayed on the e-commerce sales page.

4. The method according to claim 2, characterized in that The platform adjustment strategy includes a display strategy, the target commodity information includes first display information, and the target product information includes second display information. Step M4 includes: Step M4.23: Optimizing the expression of the first display information based on the display strategy; Step M4.24: Perform a modification operation on the product information of the associated product SKU according to the optimization result to obtain second display information displayed on the e-commerce sales page.

5. The method according to claim 4, characterized in that Step M4.24 includes: Step M4.241: modify the product information of the associated product SKU according to the optimization result; Step M4.242: Perform a language translation operation on the modification result based on the site where the store is located to obtain second display information displayed on the e-commerce sales page.

6. The method according to claim 1, characterized in that After step M4, the method further comprises: Step M5.1: Based on the commodity management page, the information modification result of the associated product SKU is displayed, and the information modification result includes the associated product SKU for which the modification fails and the reason for the modification failure.

7. The method according to claim 1, characterized in that After step M1, the method further comprises: Step M2.1: Detect the candidate product SKU corresponding to the product name of the main product SKU, and determine the candidate product SKU as the associated product SKU.

8. The method according to claim 1, characterized in that Step M3 includes: Step M3.1: In response to the information modification instruction, a modification operation is performed on the product information of the main product SKU; Step M3.2: Based on one or more parameters of product category, store positioning, site cultural background, and consumer preference, call the AI ​​model to perform optimization operations on the modification results to obtain target product information.

9. The method according to claim 1, characterized in that After step M1, the method further comprises: Step M6.1: Detecting the preset conversion parameters of the product SKU corresponding to the main product SKU, and determining the product SKU whose preset conversion parameter is lower than the preset conversion threshold as the associated product SKU; Step M6.2: Obtain unstructured data contained in a preset data source set, wherein the unstructured data includes social hot topics, search engine trends, and third-party market research reports; Step M6.3: Generate a test adjustment strategy according to the unstructured data, and perform a modification operation on the product information of the main product SKU based on the test adjustment strategy to obtain test product information; Step M6.4: performing a modification operation on the product information of the associated product SKU based on the test product information to obtain the test product information displayed on the e-commerce sales page; Step M6.5: When it is detected that the preset conversion parameter of the associated product SKU reaches the preset conversion threshold, the information synchronization relationship between the associated product SKU and the main product SKU is eliminated.

10. An information generation system based on main product linkage, characterized in that: The system is an e-commerce ERP system or an e-commerce platform system, and the system includes a commodity module, and the commodity module is used to execute the operation instructions included in the inventory determination method based on historical sales according to any one of claims 1 to 9.

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