Marketing decision-making method and system for e-commerce platform

Abstract

The invention discloses a marketing decision-making method and system for an e-commerce platform. Order data of all customers in the e-commerce platform in a preset time period are obtained, and sales data of all commodities sold in the e-commerce platform in the target area are obtained through statistics; calculating respective corresponding sales characteristic parameters according to the sales data of each commodity in the target area; comparing the sales characteristic parameter corresponding to each commodity with a preset parameter threshold to obtain a comparison result; and according to the comparison result of each commodity, determining a marketing scheme corresponding to the commodity in the target area. According to the invention, the order data in the preset time period is counted, analyzed and converted into the characteristic parameters capable of accurately reflecting the importance of the commodity, then the characteristic parameters are compared with the preset threshold value, and the sales situation of the commodity is judged according to the comparison result to determine the marketing scheme. The scientificity of marketing schemes for different commodities is effectively improved, marketing resources are reasonably planned, and the commodity sales volume can be improved.

Description

Technical Field

[0001] The present invention relates to the field of commercial marketing, and in particular to a marketing decision-making method, system, computing device and computer storage medium for an e-commerce platform. Background Art

[0002] As e-commerce businesses expand, their sales reach becomes increasingly broad, and nearly every type of commodity in daily life can be purchased through online shopping platforms. At the same time, given the convenience and versatility of the internet, e-commerce platforms can conduct various types of product marketing.

[0003] However, since the needs of customers on e-commerce platforms are different, the products listed by merchants are usually diverse, and the range of products from types to prices varies greatly.

[0004] To address this, e-commerce platforms typically collect and analyze user visit data and other information to build user profiles for different product types, and then push items they may be interested in. However, this marketing approach focuses solely on the user end, pushing marketing based solely on user-related information, which can easily lead to information cocoons. Furthermore, sales performance varies significantly for different products, ignoring the merchant's demand for product sales. This can lead to a narrow marketing scope, waste, and increased overall costs for merchants. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a marketing decision-making method for an e-commerce platform and a corresponding marketing decision-making system, computing device and computer storage medium for the e-commerce platform.

[0006] According to one aspect of the present invention, a marketing decision-making method for an e-commerce platform is provided, the method comprising:

[0007] Obtain order data from all customers on the e-commerce platform within a preset time period, and statistically calculate sales data for each product sold on the e-commerce platform in the target area;

[0008] Calculate the corresponding sales characteristic parameters of each product based on the sales data of each product in the target area;

[0009] Compare the sales characteristic parameters corresponding to each product with the preset parameter threshold to obtain the comparison results;

[0010] Based on the comparison results of each product, determine the corresponding marketing plan for the product in the target area.

[0011] In the above solution, the step of obtaining order data of all customers on the e-commerce platform within a preset period and obtaining statistical sales data of each commodity sold on the e-commerce platform in the target area further includes:

[0012] Based on the order data, analyze the product name and quantity in each order data;

[0013] Based on the obtained product names and product quantities, combined with the sales unit prices corresponding to each product name, the sales data corresponding to each product within the preset period is obtained; wherein,

[0014] The sales data at least includes the sales volume and sales revenue of the product within a preset time period.

[0015] In the above solution, the step of obtaining order data of all customers on the e-commerce platform within a preset period and obtaining statistical sales data of each commodity sold on the e-commerce platform in the target area further includes:

[0016] Get the customer's purchase address from the order data;

[0017] According to the provincial administrative divisions and the purchase address in the order data, set the corresponding regional label for the order data;

[0018] Based on the regional labels of the order data, statistics are obtained for the order data of each product in each region within the preset time period;

[0019] According to the regional label corresponding to the target area, the corresponding order data is filtered and then the corresponding sales data is determined.

[0020] In the above solution, the step of calculating the sales characteristic parameters corresponding to each commodity based on the sales data of each commodity further includes:

[0021] The sales characteristic parameters include at least the sales volume ratio and sales revenue ratio of the commodity;

[0022] The sales volume share of a product is the ratio of the sales volume of the product to the total sales volume of all products;

[0023] The sales share of a product is the ratio of the sales of that product to the total sales of all products.

[0024] In the above solution, the sales characteristic parameters corresponding to each commodity are compared with the preset parameter threshold to obtain the comparison result, further comprising:

[0025] The preset parameter thresholds include at least a first sales volume proportion threshold, a second sales volume proportion threshold and a sales revenue proportion threshold;

[0026] Compare the sales share of each product with the sales share threshold; compare the sales volume share of each product with the first sales volume share threshold and the second sales volume share threshold;

[0027] Get the comparison results corresponding to each product.

[0028] In the above solution, determining the marketing plan corresponding to each product based on the comparison results of the products further includes:

[0029] According to the comparison results, if the sales volume of a product exceeds the sales volume threshold, the product will be set as a category of products in the target area;

[0030] If the sales revenue share of a product is not higher than the sales revenue share threshold, but the sales volume share is higher than the first sales volume share threshold, the product will be set as a Category II product in the target area.

[0031] If the sales revenue share of a product is not higher than the sales revenue share threshold, and the sales volume share is not higher than the second sales volume share threshold, the product will be set as a Category 3 product in the target area;

[0032] The remaining products that do not belong to the above three categories are set as the fourth category of products in the target area;

[0033] The marketing intensity decreases step by step in the order of Category 1 products > Category 2 products > Category 3 products > Category 4 products, and the corresponding marketing plan for each product is determined accordingly.

[0034] In the above solution, the method further includes:

[0035] Based on the marketing strength of the same product in different regions, select the region with the highest level for any product;

[0036] Obtain user data from the product order data in the region;

[0037] Use AI models to extract user personal characteristics based on user data and generate marketing plans based on user personal characteristics.

[0038] According to another aspect of the present invention, a marketing decision system for an e-commerce platform is provided, comprising: a statistics module, a feature calculation module, a comparison module, and a solution determination module; wherein,

[0039] The statistical module is used to obtain order data of all customers on the e-commerce platform within a preset period of time, and to obtain sales data of each commodity sold on the e-commerce platform in the target area;

[0040] The feature calculation module is used to calculate the corresponding sales feature parameters of each product based on the sales data of each product in the target area;

[0041] The comparison module is used to compare the sales characteristic parameters corresponding to each product with the preset parameter threshold to obtain a comparison result;

[0042] The scheme determination module is used to determine the marketing scheme corresponding to each product in the target area based on the comparison results of each product.

[0043] According to another aspect of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0044] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned marketing decision-making method for an e-commerce platform.

[0045] According to another aspect of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned marketing decision-making method for an e-commerce platform.

[0046] According to the technical solution provided by the present invention, the order data of all customers on the e-commerce platform within a preset time period is obtained, and the sales data of each commodity sold on the e-commerce platform in the target area is statistically obtained; the corresponding sales characteristic parameters of each commodity are calculated based on the sales data of each commodity in the target area; the sales characteristic parameters corresponding to each commodity are compared with the preset parameter threshold to obtain a comparison result; and based on the comparison result of each commodity, the marketing plan corresponding to the commodity in the target area is determined. Based on the technical solution provided by the present invention, by obtaining order data of a preset time period on an e-commerce platform, determining the product name and quantity in each order, and combining the corresponding selling price of the product, accurately counting the sales volume and sales revenue of each product in the time period, and further setting a regional label for the order data based on the purchase address in the order, the sales data of the target area is accurately obtained; by determining the sales volume share and sales revenue share of the product in the target area, and comparing them with the preset sales volume share threshold and sales revenue share threshold, the sales situation and importance of the product in the region are scientifically reflected based on the comparison results; based on the comparison results, the marketing intensity level corresponding to the product is determined, and high marketing intensity is set for products with a high share, while helping marketing is carried out for products with a low share. In this way, by ensuring that existing priority products are promoted, helping push products with poor sales performance, the scientific nature of product sales is improved, the exposure rate of some undiscovered good products is increased, waste on the merchant side is reduced, the overall cost of the merchant is reduced, and the experience of both merchants and consumers is greatly improved. In addition, by determining the area with the highest marketing intensity for the same product in each region, AI is used to extract user personal characteristics from the user data of the product orders in that area, and a marketing plan for the product is generated based on the personal characteristics. User characteristics are extracted from the area with the best sales of this type of product, and the target users corresponding to the product are accurately determined to complete precision marketing, which effectively improves the scientific nature of the marketing plan and helps to further increase product sales.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1A schematic diagram showing a process of a marketing decision-making method for an e-commerce platform according to an embodiment of the present invention is shown;

[0051] Figure 2 A schematic diagram showing a flow chart of a sales data statistics method for products on an e-commerce platform according to an embodiment of the present invention is shown;

[0052] Figure 3 A schematic flow chart of a method for generating an impact plan for a specific commodity according to an embodiment of the present invention is shown;

[0053] Figure 4 A structural block diagram of a marketing decision system for an e-commerce platform according to an embodiment of the present invention is shown;

[0054] Figure 5 A schematic structural diagram of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0056] Figure 1 A flowchart of a marketing decision-making method for an e-commerce platform according to an embodiment of the present invention is shown. The method includes the following steps:

[0057] Step S101: Obtain order data of all customers on the e-commerce platform within a preset period of time, and obtain sales data of each commodity sold on the e-commerce platform in a target area.

[0058] Step S102: Calculate the corresponding sales characteristic parameters of each commodity according to the sales data of each commodity in the target area.

[0059] Specifically, the sales characteristic parameters include at least the sales volume ratio and sales revenue ratio of the product;

[0060] The sales volume share of a product is the ratio of the sales volume of the product to the total sales volume of all products;

[0061] The sales share of a product is the ratio of the sales of that product to the total sales of all products.

[0062] Step S103 : comparing the sales characteristic parameters corresponding to each commodity with the preset parameter threshold to obtain a comparison result.

[0063] Preferably, the preset parameter thresholds include at least a first sales volume proportion threshold, a second sales volume proportion threshold and a sales revenue proportion threshold;

[0064] Compare the sales share of each product with the sales share threshold; compare the sales volume share of each product with the first sales volume share threshold and the second sales volume share threshold;

[0065] Get the comparison results corresponding to each product.

[0066] Step S104: Determine the marketing plan corresponding to each product in the target area based on the comparison results of each product.

[0067] Preferably, according to the comparison result, if the sales volume ratio of a product is higher than the sales volume ratio threshold, the product is set as a category of products in the target area;

[0068] If the sales revenue share of a product is not higher than the sales revenue share threshold, but the sales volume share is higher than the first sales volume share threshold, the product will be set as a Category II product in the target area.

[0069] If the sales revenue share of a product is not higher than the sales revenue share threshold, and the sales volume share is not higher than the second sales volume share threshold, the product will be set as a Category 3 product in the target area;

[0070] The remaining products that do not belong to the above three categories are set as the fourth category of products in the target area;

[0071] The marketing intensity decreases step by step in the order of Category 1 products > Category 2 products > Category 3 products > Category 4 products, and the corresponding marketing plan for each product is determined accordingly.

[0072] Preferably, the sales volume proportion threshold may be 10%, the first sales volume proportion threshold may be 20%, and the second sales volume proportion threshold may be 1%.

[0073] Among them, after completing the comparison of sales data and commodity classification based on the above preset thresholds, the first category of commodities is the most important sales-oriented commodities in the area, so its marketing intensity is set to the highest first level; the second category of commodities is the secondary main commodities with high sales volume but low total sales volume (usually relatively cheap but high-selling products), so its marketing intensity is set to the second highest second level; the third category of commodities is commodities with low sales volume and sales volume, and it is difficult to obtain effective marketing using traditional intelligent marketing methods, forming a vicious circle, so its marketing intensity is set to the third level; the fourth category of commodities is commodities with average sales volume and sales volume, and its influence intensity is set to the fourth level to supplement the marketing methods of the other three categories.

[0074] According to a marketing decision-making method for an e-commerce platform provided in this embodiment, order data of all customers on the e-commerce platform within a preset time period is obtained, and sales data of each commodity sold on the e-commerce platform in a target area is statistically obtained; corresponding sales characteristic parameters of each commodity are calculated based on the sales data of each commodity in the target area; the sales characteristic parameters corresponding to each commodity are compared with preset parameter thresholds to obtain comparison results; and based on the comparison results of each commodity, a marketing plan corresponding to the commodity in the target area is determined. Through the marketing decision-making method for e-commerce platforms provided by this embodiment, by obtaining order data of a preset time period in the e-commerce platform, the sales data of each commodity in the target area during the time period is accurately counted; by determining the sales volume share and sales revenue share of the commodity in the target area, and comparing them with the preset sales volume share threshold and sales revenue share threshold, the sales situation and importance of the commodity in the region are scientifically reflected according to the comparison results; according to the comparison results, the marketing intensity level corresponding to the commodity is determined, and a high marketing intensity is set for commodities with a high share, while helpful marketing is carried out for commodities with a low share. In this way, in addition to ensuring the existing priority commodities, helpful push is carried out for commodities with poor sales, thereby improving the scientific nature of commodity sales, increasing the exposure rate of some undiscovered good products, reducing waste on the merchant side, reducing the overall cost of merchants, and greatly improving the experience of both merchants and consumers.

[0075] Figure 2 A schematic diagram showing a flow chart of a sales data statistics method for products on an e-commerce platform according to an embodiment of the present invention is shown;

[0076] like Figure 2 As shown, the method includes the following steps:

[0077] Step S201: Analyze the product names and product quantities in each order data according to the order data.

[0078] Step S202 : Based on the obtained product names and product quantities, combined with the sales unit prices corresponding to the product names, sales data corresponding to the products within a preset time period are obtained.

[0079] Preferably, the sales data at least includes the sales volume and sales amount of the product within a preset time period.

[0080] Preferably, the customer's purchase address is obtained from the order data; according to the provincial administrative divisions, corresponding regional tags are set for the order data based on the purchase address in the order data; based on the regional tags of the order data, the order data of each product in each region within a preset time period is statistically obtained; based on the regional tags corresponding to the target area, the corresponding order data is filtered to obtain the corresponding sales data.

[0081] For example, after determining the order data in various regions according to provincial administrative divisions, the target area is determined to be the Jiangsu, Zhejiang and Shanghai regions. The corresponding order data is filtered out based on the regional labels, and the sales data corresponding to each product is further analyzed and counted. Finally, the sales data of a specific product is extracted based on actual needs.

[0082] The above method obtains order data from an e-commerce platform for a preset time period, identifies the product name and quantity in each order, and, based on the product's corresponding selling price, accurately calculates the sales volume and sales revenue for each product within that time period. Furthermore, regional tags are assigned to the order data based on the purchase address in the order, accurately deriving sales data for each product in the target region. This helps identify better marketing strategies and improves the scientific nature of the marketing strategy determination process.

[0083] Figure 3 A schematic flow chart of a method for generating an impact plan for a specific commodity according to an embodiment of the present invention is shown;

[0084] like Figure 3 As shown, the method includes the following steps:

[0085] Step S301 : Based on the marketing strength levels of the same product in different regions, the region with the highest level is selected for any product.

[0086] Step S302: Obtain user data from the product order data in the area.

[0087] Step S303: Use the AI model to extract user personal characteristics based on user data, and generate a marketing plan based on the user personal characteristics.

[0088] Preferably, user data may include at least consumption data and browsing data; user personal characteristics may include age, gender, monthly consumption amount, proportion of purchased commodity types, etc.

[0089] Preferably, a sample analysis of user data is performed using a K-means clustering model to determine the user's consumption characteristics, and a marketing plan is generated based on this according to the AI model.

[0090] Among them, the objective function of the K-means clustering model is

[0091]

[0092] Where N is the number of data points; K is the number of clusters; r ik is the indicator variable; x i is the eigenvector of the i-th data point; μ k is the cluster center of the kth cluster.

[0093] According to the above method, by determining the area with the highest marketing strength for the same product in each region, AI can be used to extract user personal characteristics from the user data of the product orders in the area, and a marketing plan for the product based on the personal characteristics can be generated. User characteristics can be extracted from the area with the best sales of this type of product, and the target users corresponding to the product can be accurately determined to complete precision marketing, effectively improving the scientific nature of the marketing plan and helping to further increase product sales.

[0094] Figure 4 A structural block diagram of a marketing decision system for an e-commerce platform according to an embodiment of the present invention is shown;

[0095] like Figure 4 As shown, the system includes: a statistics module 401, a feature calculation module 402, a comparison module 403 and a solution determination module 404; wherein,

[0096] The statistical module 401 is used to obtain order data of all customers in the e-commerce platform within a preset period of time, and to obtain sales data of each commodity sold in the e-commerce platform in the target area.

[0097] Specifically, the statistical module 401 is further used to:

[0098] Based on the order data, analyze the product name and quantity in each order data;

[0099] Based on the obtained product names and product quantities, combined with the sales unit prices corresponding to each product name, the sales data corresponding to each product within the preset period is obtained; wherein,

[0100] The sales data at least includes the sales volume and sales revenue of the product within a preset time period.

[0101] Specifically, the statistics module 401 is further used to:

[0102] Get the customer's purchase address from the order data;

[0103] According to the provincial administrative divisions and the purchase address in the order data, set the corresponding regional label for the order data;

[0104] Based on the regional labels of the order data, statistics are obtained for the order data of each product in each region within the preset time period;

[0105] According to the regional label corresponding to the target area, the corresponding order data is filtered and then the corresponding sales data is determined.

[0106] The feature calculation module 402 is used to calculate the corresponding sales feature parameters of each commodity based on the sales data of each commodity in the target area.

[0107] Specifically, the feature calculation module 402 is further configured to:

[0108] The sales characteristic parameters include at least the sales volume ratio and sales revenue ratio of the commodity;

[0109] The sales volume share of a product is the ratio of the sales volume of the product to the total sales volume of all products;

[0110] The sales share of a product is the ratio of the sales of that product to the total sales of all products.

[0111] The comparison 403 is used to compare the sales characteristic parameters corresponding to each commodity with the preset parameter threshold to obtain a comparison result.

[0112] Specifically, the comparison module 403 is further configured to:

[0113] The preset parameter thresholds include at least a first sales volume proportion threshold, a second sales volume proportion threshold and a sales revenue proportion threshold;

[0114] Compare the sales share of each product with the sales share threshold; compare the sales volume share of each product with the first sales volume share threshold and the second sales volume share threshold;

[0115] Get the comparison results corresponding to each product.

[0116] Preferably, the comparison module 403 is further configured to:

[0117] According to the comparison results, if the sales volume of a product exceeds the sales volume threshold, the product will be set as a category of products in the target area;

[0118] If the sales revenue share of a product is not higher than the sales revenue share threshold, but the sales volume share is higher than the first sales volume share threshold, the product will be set as a Category II product in the target area.

[0119] If the sales revenue share of a product is not higher than the sales revenue share threshold, and the sales volume share is not higher than the second sales volume share threshold, the product will be set as a Category 3 product in the target area;

[0120] The remaining products that do not belong to the above three categories are set as the fourth category of products in the target area;

[0121] The marketing intensity decreases step by step in the order of Category 1 products > Category 2 products > Category 3 products > Category 4 products, and the corresponding marketing plan for each product is determined accordingly.

[0122] The scheme determination 404 is used to determine the marketing scheme corresponding to the product in the target area based on the comparison results of each product.

[0123] Specifically, the scheme determines 404, further for,

[0124] Based on the marketing strength of the same product in different regions, select the region with the highest level for any product;

[0125] Obtain user data from the product order data in the region;

[0126] Utilize AI models to extract user characteristics based on user data and generate marketing plans based on these characteristics.

[0127] The marketing decision system for an e-commerce platform provided in accordance with this embodiment includes: a statistical module, a feature calculation module, a comparison module and a solution determination module; wherein the statistical module is used to obtain the order data of all customers on the e-commerce platform within a preset time period, and statistically obtain the sales data of each commodity sold on the e-commerce platform in a target area; the feature calculation module is used to calculate the corresponding sales feature parameters of each commodity based on the sales data of each commodity in the target area; the comparison module is used to compare the sales feature parameters corresponding to each commodity with the preset parameter threshold to obtain a comparison result; the solution determination module is used to determine the marketing solution corresponding to the commodity in the target area based on the comparison result of each commodity. The marketing decision system for an e-commerce platform provided in this embodiment obtains order data from a preset time period on the e-commerce platform, determines the product name and quantity in each order, and accurately calculates the sales volume and sales revenue of each product in the time period based on the corresponding selling price of the product. The order data is further labeled with a region based on the purchase address in the order, thereby accurately obtaining sales data for the target region. The sales volume share and sales revenue share of the product in the target region are determined, and compared with preset sales volume share and sales revenue share thresholds. The comparison results scientifically reflect the sales situation and importance of the product in the region. Based on the comparison results, the marketing intensity level corresponding to the product is determined, and high marketing intensity is set for products with a high share. At the same time, supportive marketing is performed for products with a low share. In addition to ensuring the promotion of existing priority products, supportive promotion is performed for products with poor sales, thereby improving the scientific nature of product sales, increasing the exposure rate of some undiscovered good products, reducing waste on the merchant side, lowering the merchant's overall cost, and significantly improving the experience of both merchants and consumers. In addition, by determining the area with the highest marketing intensity for the same product in each region, AI is used to extract user personal characteristics from the user data of the product orders in that area, and a marketing plan for the product is generated based on the personal characteristics. User characteristics are extracted from the area with the best sales of this type of product, and the target users corresponding to the product are accurately determined to complete precision marketing, which effectively improves the scientific nature of the marketing plan and helps to further increase product sales.

[0128] The present invention also provides a non-volatile computer storage medium, which stores at least one executable instruction. The executable instruction can execute a marketing decision-making method for an e-commerce platform in any of the above method embodiments.

[0129] Figure 5 A schematic structural diagram of a computing device according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0130] like Figure 5 As shown, the computing device may include: a processor (processor) 502 , a communications interface (Communications Interface) 504 , a memory (memory) 506 , and a communication bus 508 .

[0131] in:

[0132] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0133] The communication interface 504 is used to communicate with other devices such as clients or other servers.

[0134] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the above-mentioned embodiment of the marketing decision-making method for the e-commerce platform.

[0135] Specifically, the program 510 may include program codes, which include computer operating instructions.

[0136] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in a computing device may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0137] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0138] Program 510 can be specifically used to cause processor 502 to execute a marketing decision-making method for an e-commerce platform in any of the above-mentioned method embodiments. The specific implementation of each step in program 510 can refer to the corresponding descriptions of the corresponding steps and units in the above-mentioned marketing decision-making method embodiment for an e-commerce platform, and will not be repeated here. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working process of the above-mentioned devices and modules can refer to the corresponding process description in the above-mentioned method embodiment, and will not be repeated here.

[0139] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages can be utilized to realize the content of the present invention described herein, and the above description of specific languages is for the purpose of disclosing the best mode of the present invention.

[0140] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0141] Similarly, it should be understood that in order to streamline the present disclosure and aid understanding of one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0142] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0143] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0144] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in accordance with the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing a portion or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0145] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A marketing decision-making method for an e-commerce platform, comprising: Obtain order data from all customers on the e-commerce platform within a preset time period, and statistically calculate sales data for each product sold on the e-commerce platform in the target area; Calculate the corresponding sales characteristic parameters of each product based on the sales data of each product in the target area; Compare the sales characteristic parameters corresponding to each product with the preset parameter threshold to obtain the comparison results; Based on the comparison results of each product, determine the corresponding marketing plan for the product in the target area.

2. The method according to claim 1, characterized in that The step of obtaining order data of all customers on the e-commerce platform within a preset period and obtaining statistical sales data of each commodity sold on the e-commerce platform in the target area further includes: Based on the order data, analyze the product name and quantity in each order data; Based on the obtained product names and product quantities, combined with the sales unit prices corresponding to each product name, the sales data corresponding to each product within the preset period is obtained; wherein, The sales data at least includes the sales volume and sales revenue of the product within a preset time period.

3. The method according to claim 1, characterized in that The step of obtaining order data of all customers on the e-commerce platform within a preset period and obtaining statistical sales data of each commodity sold on the e-commerce platform in the target area further includes: Get the customer's purchase address from the order data; According to the provincial administrative divisions and the purchase address in the order data, set the corresponding regional label for the order data; Based on the regional labels of the order data, statistics are obtained for the order data of each product in each region within the preset time period; According to the regional label corresponding to the target area, the corresponding order data is filtered and then the corresponding sales data is determined.

4. The method according to claim 1, wherein The calculating of sales characteristic parameters corresponding to each commodity based on the sales data of each commodity further includes: The sales characteristic parameters include at least the sales volume ratio and sales revenue ratio of the commodity; The sales volume share of a product is the ratio of the sales volume of the product to the total sales volume of all products; The sales share of a product is the ratio of the sales of that product to the total sales of all products.

5. The method according to claim 1, wherein The comparison of the sales characteristic parameters corresponding to each commodity with the preset parameter threshold to obtain the comparison result further includes: The preset parameter thresholds include at least a first sales volume proportion threshold, a second sales volume proportion threshold and a sales revenue proportion threshold; Compare the sales share of each product with the sales share threshold; compare the sales volume share of each product with the first sales volume share threshold and the second sales volume share threshold; Get the comparison results corresponding to each product.

6. The method according to claim 1, characterized in that Determining a marketing plan corresponding to each product based on the comparison results of the products further includes: According to the comparison results, if the sales volume of a product exceeds the sales volume threshold, the product will be set as a category of products in the target area; If the sales revenue share of a product is not higher than the sales revenue share threshold, but the sales volume share is higher than the first sales volume share threshold, the product will be set as a Category II product in the target area. If the sales revenue share of a product is not higher than the sales revenue share threshold, and the sales volume share is not higher than the second sales volume share threshold, the product will be set as a Category 3 product in the target area; The remaining products that do not belong to the above three categories are set as the fourth category of products in the target area; The marketing intensity decreases step by step in the order of Category 1 products > Category 2 products > Category 3 products > Category 4 products, and the corresponding marketing plan for each product is determined accordingly.

7. The method according to claim 1, characterized in that The method further comprises: Based on the marketing strength of the same product in different regions, select the region with the highest level for any product; Obtain user data from the product order data in the region; Use AI models to extract user personal characteristics based on user data and generate marketing plans based on user personal characteristics.

8. A marketing decision-making system for an e-commerce platform, comprising: Statistics module, feature calculation module, comparison module and solution determination module; among them, The statistical module is used to obtain order data of all customers on the e-commerce platform within a preset period of time, and to obtain sales data of each commodity sold on the e-commerce platform in the target area; The feature calculation module is used to calculate the corresponding sales feature parameters of each product based on the sales data of each product in the target area; The comparison module is used to compare the sales characteristic parameters corresponding to each product with the preset parameter threshold to obtain a comparison result; The scheme determination module is used to determine the marketing scheme corresponding to each product in the target area based on the comparison results of each product.

9. A computing device comprising: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to a marketing decision-making method for an e-commerce platform as described in any one of claims 1-7.

10. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform operations corresponding to the marketing decision-making method for an e-commerce platform as described in any one of claims 1-7.

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