E-commerce commodity sales volume analysis system

Abstract

The invention discloses an e-commerce commodity sales volume analysis system, and the system comprises a data analysis module which comprises a market analysis module, a flow analysis module and a price analysis module. The market analysis module comprises a flow word module used for monitoring e-commerce commodity flow word ranking changes and a competitive product module used for monitoring competitive product sales changes, the flow analysis module is used for analyzing flow changes of e-commerce commodities, and the price analysis module is used for analyzing price change trends of the e-commerce commodities and competitive products; a data analysis module analyzes the sales volume change of the e-commerce commodity based on a plurality of influence factors, wherein the influence factors comprise the flow word ranking data of the e-commerce commodity, the competitive product sales volume data and the price data of the e-commerce commodity and the competitive product; the influence factors further comprise a difference evaluation number in a home page of the e-commerce commodity, a difference evaluation number in a Review page and a Rating score. The system can give consideration to sales fluctuation reason analysis of advertisement commodities and non-advertisement commodities.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an e-commerce product sales analysis system, an e-commerce product sales analysis method and system. Background Art

[0002] With the rise of global e-commerce, international retail trade has developed rapidly. A large number of domestic small and medium-sized e-commerce users have expanded their retail business to foreign markets and sold many domestic products to foreign markets through overseas e-commerce platforms. With the development of cross-border business, e-commerce ERP systems developed based on ERP software have gradually developed. E-commerce ERP systems can be deeply connected with e-commerce platforms to help domestic small and medium-sized e-commerce users uniformly manage their overseas stores, solve the obstacles caused by language differences, and enable one operator to manage hundreds of e-commerce stores at the same time, greatly improving the efficiency of store operations.

[0003] The e-commerce ERP system accesses and controls the stores on the e-commerce platform through established rules and processes dynamic data from all aspects of store operations. The data management is complex and it must also meet the operational convenience needs of various types of users. Therefore, the various functional modules of the existing commercialized e-commerce ERP systems are still in the stage of gradual functional updating and improvement. The functional algorithms and rules formulated by various software companies when developing their own e-commerce ERP systems are basically different. As user needs change, new versions of each functional module will continue to be developed to be compatible with more usage scenarios.

[0004] In actual e-commerce activities, product sales may fluctuate beyond the normal range, which is usually called sales fluctuation of products. The current sales fluctuation analysis method on the market mainly analyzes the traffic and conversion rate of product advertisements to summarize the reasons for sales fluctuation of e-commerce products. However, this sales analysis method can usually only be applied to sales analysis of products with more advertisements, and is difficult to apply to products with less or even no advertisements.

[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 e-commerce product sales analysis method and system, which can analyze the reasons for sales fluctuations of various types of products, can be applied to products with and without advertisements, and improve the accuracy of the analysis results.

[0007] To achieve the above objectives, the present application proposes an e-commerce product sales analysis method and system, the method comprising: Step S1: Obtain sales data of a preset product on an e-commerce platform; Step S2: when it is detected that the sales fluctuation of the preset commodity exceeds a preset threshold, the preset marketing data, traffic data and price data on the e-commerce platform where the preset commodity is located are obtained, wherein the preset marketing data represents the market demand for the preset commodity on the e-commerce platform, and the preset marketing data includes commodity traffic word data and competitive product sales data, wherein the commodity traffic word is a word that represents the main attribute of the preset commodity and is used to search for the preset commodity on the e-commerce platform, and the traffic data represents the exposure degree of the preset commodity on the e-commerce platform; Step S3.1: Analyze the preset marketing data to obtain the change in demand for the commodity category and the impact of competing products corresponding to the preset commodity, and determine the impact of market demand changes based on the change in demand for the commodity category and the impact of competing products, where the competing products are commodities belonging to the commodity category corresponding to the preset commodity; Step S3.2: Analyze the traffic data to obtain the impact of exposure changes for the preset commodity; Step S3.3: Analyze the price data to obtain the impact of price changes for the preset commodity; Step S4: determining the target factor causing the sales volume fluctuation of the preset commodity based on the market demand change impact, the exposure change impact and the price change impact of the preset commodity.

[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 search ranking of commodity traffic words on the e-commerce platform can reflect the market demand level for commodity types. If the commodity traffic word search ranking data is collected and analyzed, it is possible to verify the sales trend of the same type of commodities from the side. For example, when the traffic word search ranking of a commodity drops sharply, the reason for the obvious change in its ranking is likely to be the overall decrease in market demand for such commodities. Therefore, a market analysis module is set on the data analysis module of the commodity sales analysis system. The market analysis module includes a traffic word module, which is used to monitor the ranking changes of the traffic words of e-commerce commodities.

[0009] In addition, the applicant also found that the sales of competing products may also have a significant impact on the sales of this product. For example, when the market demand for similar products has not changed much, if a certain product is more popular in the market and a large number of users buy the product, the sales of other similar products will be affected. In this way, by obtaining and analyzing the sales information of competing products, the reasons for the fluctuations in sales of this store's products can be verified to a certain extent, and the analysis results based on the traffic word search ranking can be supplemented to improve the overall accuracy of the product sales analysis. Therefore, a competing product module is further set up on the market analysis module to monitor the sales trend of competing products.

[0010] On this basis, since product exposure is an important factor affecting product sales, a traffic analysis module is further set up on the data analysis module. The traffic analysis module includes an advertising traffic module and a natural traffic module, which are used to monitor changes in product exposure.

[0011] In addition, the applicant also found that the price change of the commodity can also reflect the change trend of the commodity sales volume. If the commodity price information is processed and the noise is removed, the price change trend and commodity sales volume trend in the same period are analyzed, then the reasons for the fluctuation of commodity sales volume can be verified to a certain extent. Therefore, a price analysis module is further set on the data analysis module to monitor the price changes of commodities and competing products.

[0012] In summary, if the above external market factors, exposure change impact, and price change impact are comprehensively analyzed, the accuracy of product sales change analysis can be greatly improved. Moreover, due to the introduction of evaluation factors other than advertising traffic, such as external market factors and exposure change impact, the reasons for sales changes can also be accurately analyzed for products with less or no advertising.

[0013] The order of analysis of product sales in this application also reflects the importance of the above factors, that is, the importance of external market factors, exposure change impact, and price change impact decreases from high to low. In addition, the information required for sales analysis in this application is relatively easy to obtain, such as product traffic word search ranking data, traffic data, price data and other related data information can be directly obtained on the e-commerce platform, which reduces the difficulty of executing sales analysis and improves the efficiency of sales analysis.

[0014] In this way, the e-commerce product sales analysis method proposed in this application can conduct effective sales analysis on both advertised products and non-advertisement products at the same time, identify the factors affecting sales changes, improve the scope of application and analysis efficiency of the sales analysis method, and improve the accuracy of product sales analysis results, as well as reduce the difficulty of executing sales analysis, ultimately improving e-commerce operating efficiency and corporate profits.

[0015] Furthermore, the present application also provides an e-commerce commodity sales analysis system, which is used to analyze the reasons for sales fluctuations of both advertising commodities and non-advertising commodities. The commodity sales analysis system includes a data analysis module, which includes a market analysis module, a traffic analysis module, and a price analysis module; the market analysis module includes a traffic word module and a competitive product module, the traffic word module is used to monitor the ranking changes of traffic words of e-commerce commodities, and the competitive product module is used to monitor the sales change trend of competitive products, wherein the e-commerce commodity is the commodity to be analyzed, and the competitive product is the commodity belonging to the commodity category corresponding to the commodity to be analyzed; the traffic analysis module is used to analyze the traffic changes of the e-commerce commodity, and the price analysis module is used to analyze the price change trend of the e-commerce commodity and the competitive product; the data analysis module analyzes the sales changes of the e-commerce commodity based on multiple influencing factors, and the influencing factors include the traffic word ranking data of the e-commerce commodity, the sales data of the competitive product, and the price data of the e-commerce commodity and the competitive product; the influencing factors also include the number of negative reviews on the homepage of the e-commerce commodity, the number of negative reviews on the Review page, and the Rating score.

[0016] The present application also provides a system, which is an e-commerce ERP system or an e-commerce platform system, and the system can execute the operating instructions of each method step of the present application.

[0017] 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.

[0018] 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.

[0019] refer to Figure 1 The e-commerce ERP system of this application includes one or more of the 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 data analysis 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 users of the ERP system of this application can also be called store managers, sellers, operators, operation personnel, etc., and their identities are not strictly limited unless otherwise stated.

[0020] The meaning and description of the terms in the field of e-commerce in this application (the letters of English words in this application are not case sensitive).

[0021] (1) Listing: Product details page (2) PV: Page views (3) Review: Product review (4) Rating: Product ranking rating BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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.

[0023] Figure 1 This is a schematic diagram of the functional modules of an e-commerce ERP system in one embodiment of the present application.

[0024] Figure 2 A flowchart of an e-commerce product sales analysis method in one embodiment of the present application.

[0025] Figure 3 This is a schematic diagram of the structure of a data analysis module in one embodiment of the present application.

[0026] Figure 4 This is a schematic diagram of the application principle in one embodiment of the present application. DETAILED DESCRIPTION

[0027] 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.

[0028] Figure 2 A flow chart of an e-commerce product sales analysis method in one embodiment of the present application is shown, and the e-commerce product sales analysis method mainly includes the following steps S1 to S4.

[0029] Step S1: Obtain sales data of a preset product on an e-commerce platform; Step S2: when it is detected that the sales fluctuation of the preset commodity exceeds a preset threshold, the preset marketing data, traffic data and price data on the e-commerce platform where the preset commodity is located are obtained, wherein the preset marketing data represents the market demand for the preset commodity on the e-commerce platform, and the preset marketing data includes commodity traffic word data and competitive product sales data, wherein the commodity traffic word is a word that represents the main attribute of the preset commodity and is used to search for the preset commodity on the e-commerce platform, and the traffic data represents the exposure degree of the preset commodity on the e-commerce platform; Step S3.1: Analyze the preset marketing data to obtain the change in demand for the commodity category and the impact of competing products corresponding to the preset commodity, and determine the impact of market demand changes based on the change in demand for the commodity category and the impact of competing products, where the competing products are commodities belonging to the commodity category corresponding to the preset commodity; Step S3.2: Analyze the traffic data to obtain the impact of exposure changes for the preset commodity; Step S3.3: Analyze the price data to obtain the impact of price changes for the preset commodity; Step S4: determining the target factor causing the sales volume fluctuation of the preset commodity based on the market demand change impact, the exposure change impact and the price change impact of the preset commodity.

[0030] The present application proposes a method for analyzing the sales volume of e-commerce products. When it is detected that the sales volume fluctuation of the preset product exceeds the preset threshold, it is determined that the preset product has a sales volume change, and the preset marketing data, traffic data and price data on the e-commerce platform where the preset product is located are obtained. Among them, the preset marketing data represents the market demand of the preset product on the e-commerce platform, specifically including the change in demand for the product category and the impact of competing products, which is used to analyze the external market factors that affect the change in product sales volume; the traffic data represents the exposure of the preset product on the e-commerce platform, which is used to analyze the advertising operation factors that affect the change in product sales volume. The above preset marketing data is analyzed to obtain the change in demand for the product category and the impact of competing products of the preset product, that is, whether the sales volume of the preset product is affected by the reduction in user demand for the product category corresponding to the preset product, or whether the competitive product has gained an advantage, so as to determine the impact of market demand changes; then, at the advertising operation level, the exposure change impact of the preset product is analyzed for the traffic data, and at the sales strategy level, the price change impact of the preset product is analyzed for the price data; finally, based on the above market demand change impact, exposure change impact and the price change impact, the target factor causing the fluctuation in sales volume of the preset product is determined, so that the user can improve the e-commerce operation strategy to improve the target factor.

[0031] In this way, the e-commerce product sales analysis method proposed in this application can conduct effective sales analysis on both advertised products and non-advertisement products at the same time, identify the factors affecting sales changes, improve the scope of application and analysis efficiency of the sales analysis method, and improve the accuracy of product sales analysis results, as well as reduce the difficulty of executing sales analysis, ultimately improving e-commerce operating efficiency and corporate profits.

[0032] Figure 3The schematic diagram of the structure of the data analysis module in one embodiment of the present application is shown, and the data analysis module includes a data acquisition module, a data detection module, a market analysis module, a traffic analysis module, and a price analysis module. Specifically, the data acquisition module is used to obtain the data information required for performing commodity sales analysis on the e-commerce platform; the data detection module is used to detect whether there is an abnormal fluctuation in commodity sales; the market analysis module includes a traffic word module for analyzing commodity category demand and a competitive product module for analyzing the impact of competitive products; the traffic analysis module includes an advertising traffic module for analyzing the impact of advertising traffic changes and a natural traffic module for analyzing natural traffic changes; the price analysis module is used to analyze the impact of price trend changes on commodities and competitive products.

[0033] The following is a detailed explanation of each method step in the e-commerce product sales analysis method.

[0034] Step S1: Obtain sales data of a preset product on an e-commerce platform.

[0035] Specifically, the e-commerce ERP system obtains sales data of goods on the e-commerce platform and monitors fluctuations in sales of goods.

[0036] Step S2: When it is detected that the sales fluctuation of the preset commodity exceeds a preset threshold, the preset marketing data and traffic data on the e-commerce platform where the preset commodity is located are obtained, and the preset marketing data characterizes the market demand for the preset commodity on the e-commerce platform. The preset marketing data includes commodity traffic word data and competitor sales data. The commodity traffic word is a word that characterizes the main attribute of the preset commodity and is used to search for the preset commodity on the e-commerce platform. The traffic data characterizes the exposure level of the preset commodity on the e-commerce platform.

[0037] Specifically, when it is detected that the sales fluctuation of the preset commodity exceeds the preset threshold, it is determined that the preset commodity has a sales change, and the preset marketing data and traffic data on the e-commerce platform where the preset commodity is located are obtained. Among them, the above preset threshold can be set according to actual needs and is not specifically limited here; the preset marketing data represents the market demand for the preset commodity on the e-commerce platform, and is used to analyze external market factors that affect changes in commodity sales. The preset marketing data may include: commodity traffic word search ranking data, commodity repurchase rate, and commodity coupon collection volume; the traffic data represents the exposure of the preset commodity on the e-commerce platform, and is used to analyze advertising operation factors that affect changes in commodity sales. The traffic data may include: commodity page click-through rate, commodity exposure rate, advertising conversion rate, etc.

[0038] Step S3.1: Analyze the preset marketing data to obtain the changes in demand for the product category and the impact of competing products corresponding to the preset product, and determine the impact of market demand changes based on the changes in demand for the product category and the impact of competing products, where the competing products are products belonging to the product category corresponding to the preset product.

[0039] Specifically, among the external market factors, changes in demand for product categories and the impact of competing products can have an impact on the sales of preset products. Among them, changes in demand for product categories refer to the reduction in market demand for the product categories corresponding to the preset products, so the sales of the preset products are also affected; the impact of competing products refers to whether other products with the same attributes as the preset products in the product categories corresponding to the preset products have better competitive advantages, resulting in a reduction in user demand for the preset products due to the fact that competing products are more popular with users when the demand for the product categories remains basically unchanged.

[0040] Step S3.2: Analyze the traffic data to obtain the exposure change impact of the preset product.

[0041] Specifically, from the perspective of internal product operations, among advertising operation factors, analyzing the traffic data that characterizes product exposure can also determine the reasons for fluctuations in product sales. Specifically, advertising and natural traffic distribution can be selected as entry points for the dimensions. Among them, advertising strategies mainly refer to the impact of different advertising strategies and specific aspects of advertising strategies on product sales, while natural traffic is related to the product's page display elements, such as the number of negative reviews on the review homepage, the number of negative reviews on the page, ranking scores, product reviews, etc. It may also be related to changes in competing products, including changes in competing product titles, changes in main images, and adjustments to five-point descriptions. In this way, analyzing traffic data can determine the changes in exposure traffic and display effects of preset products on e-commerce platforms, so as to determine the target factors that cause sales fluctuations of preset products.

[0042] Step S3.3: Analyze the price data to obtain the impact of the price change of the preset product.

[0043] Specifically, price is also an important factor that can affect product sales in e-commerce activities. By analyzing the price data of preset products, it is possible to determine whether the sales of preset products fluctuate because the price of competing products is lower or the competing products have adopted special discount rules to gain a competitive advantage.

[0044] Step S4: determining the target factor causing the sales volume fluctuation of the preset commodity based on the market demand change impact, the exposure change impact and the price change impact of the preset commodity.

[0045] Specifically, the preset marketing data, traffic data and price data are analyzed from the three dimensions of external market factors, advertising operation factors and sales strategies. Then, based on the relationship between the impact of changes in market demand, exposure and price changes corresponding to the preset products and sales fluctuations, the target factors that lead to abnormal sales of the preset products are determined for users to refer to and adjust e-commerce operation strategies.

[0046] Furthermore, if Figure 4 As shown, in one embodiment, the preset marketing data includes commodity traffic words, which are words that characterize the main attributes of the preset commodity and are used to search for the preset commodity on the e-commerce platform. The above step S3.1 analyzes the preset marketing data to obtain the commodity category demand changes corresponding to the preset commodity, including the following steps S3.11 and S3.12.

[0047] Step S3.11: Determine the search ranking data of the commodity traffic word on the e-commerce platform; Step S3.12: Analyze the search ranking data of the commodity traffic word to obtain the commodity category demand change corresponding to the preset commodity.

[0048] Specifically, on an e-commerce platform, users can filter out products that meet specific attributes by searching for some product traffic words, and e-commerce platforms, such as Amazon, will make the search ranking data of product traffic words public. In this embodiment, by analyzing the search ranking data of product traffic words, it is possible to determine changes in market demand. For example, if the product traffic words of a preset product drop significantly in the search ranking, it means that the demand for this type of product by users in the market has dropped, that is, the demand for the product category to which the preset product belongs has dropped.

[0049] Furthermore, in one embodiment, the preset marketing data also includes category sales data of the preset category in which the preset commodity is located, and sales data of competing products corresponding to the preset category; step S3.1 analyzes the preset marketing data to obtain the competitive product impact corresponding to the preset commodity, including the following steps S3.13 and S3.14.

[0050] Step S3.13: comparing the sales data of the category and the sales data of the competing product with the sales data of the preset product in terms of the degree of change and the trend of change, to obtain a first comparison result; Step S3.14: Determine the impact of competing products corresponding to the preset product according to the first comparison result.

[0051] Specifically, the sales data of the category to which the preset product belongs, the sales data of competing products, and the sales data of the preset product are compared. If the sales trend of the product category is basically stable, but the sales trends of competing products and the preset product have changed significantly, it can be determined that the competing products have caused the sales fluctuations of the preset product.

[0052] For example, assuming that the sales volume trend of a product category is stable, the sales volume of competing products increases while the sales volume of preset products decreases, it means that the competing products have gained a competitive advantage and have had a major impact on the sales volume fluctuations of the preset products; similarly, if the sales volume of both competing products and preset products decreases, it means that other products in this product category have gained a competitive advantage and have had a major impact on the sales volume fluctuations of the preset products.

[0053] Furthermore, in one embodiment, the traffic data includes product advertisement sales, and step S3.2 includes the following steps S3.21 to S3.24.

[0054] Step S3.21: obtaining, on the e-commerce platform, the product advertisement sales corresponding to each advertisement delivery strategy applied to the preset product; Step S3.22: Determine the contribution ratio of the product advertisement sales volume to the preset product sales volume fluctuation, and determine the advertising delivery strategy whose contribution ratio reaches the preset ratio as the target delivery strategy; Step S3.23: determining the advertisement delivery factors that cause the advertisement flow rate change based on the target delivery strategy; Step S3.24: Determine the impact of the exposure change based on the advertising delivery factors.

[0055] Specifically, advertising delivery strategies include traffic word delivery, automatic delivery, product delivery and other strategy types; among them, traffic word delivery means setting specific traffic words for preset products. When users search for the specific traffic words on the e-commerce platform, the search results will display the above preset products; automatic delivery means that the e-commerce platform or e-commerce ERP system automatically sets traffic words for preset products based on big data analysis results; product delivery means establishing a binding relationship between the preset product and a target product. When users search for the target product on the e-commerce platform, the search results will display the preset product that has a binding relationship with the target product.

[0056] It is understandable that in actual e-commerce activities, there may be multiple advertising delivery strategies applied to a certain product. After all, each advertising delivery strategy has different resource investment and different impacts on product sales. In this embodiment, the product advertising sales corresponding to each advertising delivery strategy applied to the preset product on the e-commerce platform are obtained, and then the contribution ratio of the product advertising sales to the preset product sales fluctuation is determined. If there is a large contribution ratio of product advertising sales and the contribution rate of the advertising delivery strategy to the product sales is negative, then the advertising delivery strategy is determined to be the target delivery strategy that has the main negative impact on the product sales fluctuation, so as to further analyze the target delivery strategy and subsequently generate e-commerce operation suggestions for adjusting the target delivery strategy.

[0057] Further, based on the above embodiment, the above step S3.23 includes the following step S3.231 and step S3.232.

[0058] Step S3.231: Obtain the advertising sales contribution rates corresponding to the target delivery strategy in the exposure phase, click phase, and conversion phase respectively; Step S3.232: Determine the advertising placement factors that lead to the change in exposure of the preset product based on the advertising sales contribution rate.

[0059] Specifically, the advertising sales contribution rate corresponding to the exposure stage, click stage and conversion stage of the target delivery strategy is obtained on the e-commerce platform. The advertising sales contribution rate represents the direction and degree of influence of the target delivery strategy on the product sales in the exposure stage, click stage and conversion stage. For example, if the advertising sales contribution rates of the exposure stage, click stage and conversion stage are 3.22%, 5.46% and -4.35% respectively, it means that the target delivery strategy has a big problem in the conversion stage, and the user should optimize and adjust the conversion stage of the target delivery strategy.

[0060] Among them, for the calculation of the contribution rate of each link in the advertising delivery strategy, there is the following formula: 1. Additive disassembly type: Known,

[0061] Target Fluctuation:

[0062] Index Y month-on-month volatility factor contribution rate C i Calculation formula:

[0063] It can be understood that the above-mentioned addition decomposition type formula is applicable to the calculation of contribution rate when parallel indicators are added.

[0064] For example: total traffic = advertising traffic + natural traffic, where Y is the total traffic, X1 is advertising traffic, X2 is natural traffic, and Y is the total traffic. 1 is the total flow of this period, Y 0 is the total traffic of the previous period, and ΔY% is the month-on-month value of the total traffic. If used to calculate the contribution of advertising traffic and natural traffic to the change of total traffic, then the month-on-month fluctuation of total traffic = advertising traffic contribution + natural traffic contribution, where C xi ; is the sum of contribution rates, X i 1 is the current period data of advertising traffic or natural traffic; X i 0 It is the previous period data of advertising traffic or natural traffic. The subscript i represents advertising traffic or natural traffic.

[0065] It is understandable that the dimensional decomposition of absolute value indicators is additive. The year-on-year / month-on-month change of absolute quantity indicators is the weighted sum of the changes of each sub-indicator, including the sum of the page views (PV) of the product details page (listing) on ​​the Amazon platform, which is equal to the sum of the page views (PV) of each channel. Then the contribution rate of the change in the total page view PV is equal to the change of each channel divided by the total page view PV of the previous month.

[0066] The data shows that the overall PV increased, but the increase was not large, mainly due to the impact of advertising. Among them, the advertising PV decreased by -16.67% month-on-month, and the contribution rate was -10%, which reduced the overall traffic growth.

[0067] 2. Ratio Decomposition Type Known,

[0068] Contribution of indicator changes by item:

[0069] Contribution of structural changes in sub-items:

[0070] Target Fluctuation:

[0071] Index Y month-on-month volatility factor contribution rate C xi Calculation formula: , where X1 is the current period data, X0 is the previous period data, For the average logarithmic weight, there is a formula:

[0072] It can be understood that the above ratio decomposition type formula is applicable to the contribution rate calculation when indicators are compared, that is, the attribution of ratio indicators such as click-through rate and conversion rate.

[0073] For example: Product click-through rate = clicks / exposures, where Y is the product click-through rate, S is the clicks, and P is the exposure. xi ; represents click volume, click volume = ad click volume + natural click volume, S xi ; The subscript i represents the number of ad clicks or natural clicks. xi ; represents exposure, exposure = advertising exposure + natural exposure, P xi The subscript i represents the advertising exposure or natural exposure, P i Represents the proportion of advertising exposure to total exposure; Y i Represents the ad click-through rate or natural click-through rate, Y 0 Represents the total click rate of the previous period, Y 1 Represents the total click rate of this issue. i 1 represents the click-through rate of this advertisement, Y i 0 represents the click rate of the previous advertisement, P i 1 represents the click rate of the previous advertisement, P i 0 Represents the click-through rate of this advertisement.

[0074] 3. Multiplication decomposition type Known

[0075] Target Fluctuation:

[0076] The calculation formula of the month-on-month volatility factor contribution rate Ci of indicator Y is: , where X 1 is the data of this period, X 0 For the previous period data, For the average logarithmic weight, there is a formula:

[0077] It can be understood that the above multiplication decomposition type formula is applicable to the calculation of the contribution of each sub-item to the whole when the absolute value indicators are multiplied.

[0078] For example,

[0079] Sales volume = traffic volume * conversion rate, Y is sales volume, X1 is traffic volume, X2 is conversion rate, Y is0 represents the sales volume of the previous period, Y 1 Represents the sales volume for this period.

[0080] Further, based on the above embodiment, the above step S3.24 includes the following steps S3.241 to S3.243.

[0081] Step S3.241: Compare the page display elements of the preset product with the preset display standard to obtain a second comparison result; Step S3.242: Determine the impact of natural flow change according to the second comparison result; Step S3.243: Determine the impact of the exposure change based on the advertising placement factors and the impact of the natural traffic change.

[0082] Specifically, the exposure traffic allocation of the e-commerce platform for the product includes the exposure traffic brought by advertising and the natural traffic automatically allocated by the system. In this embodiment, the natural traffic is related to the page display elements of the preset product. The page display elements of the preset product are the elements that affect the display effect of the product display page, including the comment changes on the product display page, the number of negative reviews on the comment homepage, the number of negative reviews on the Review page, the Rating product ranking score, etc. The above elements can be analyzed to determine the impact of the natural traffic changes caused by the page display elements of the preset product.

[0083] Further, based on the above embodiment, the above step S3.242 includes the following step S3.2421 and step S3.2422.

[0084] Step S3.2421: Compare the page display elements of the competitor product with the preset display standard to obtain a third comparison result; Step S3.2422: Determine the impact of natural flow rate changes based on the second comparison result and the third comparison result.

[0085] Specifically, changes in the display elements of the competitor's product display page may cause the competitor to obtain more exposure traffic, thereby reducing the exposure traffic allocated by the system to the preset product. In this embodiment, the page display elements of the competitor that will affect the natural traffic distribution include changes in the competitor's title, changes in the main image, and adjustments to the five-point description. This embodiment analyzes whether the page display effect of the competitor is better due to changes in the page display elements of the competitor, thereby reducing the natural traffic distribution obtained by the preset product, and ultimately causing changes in the sales of the preset product.

[0086] Further, in one embodiment, step S3.3 includes the following steps S3.31 and S3.32.

[0087] Step S3.31: Compare the price trends of the preset product and the competing product to obtain a fourth comparison result; Step S3.32: Determine the impact of price changes based on the comparison results.

[0088] Specifically, by comparing the price trends of the preset product with those of the competing product, it can be determined whether the competing product has gained a competitive advantage due to its lower price, thereby causing the sales volume of the preset product to change. The comparison process may include the initial pricing of the preset product and the competing product and the respective applicable preferential activity rules.

[0089] Furthermore, in one embodiment, after the above step S4, the method further includes the following steps S5.1 and S5.2.

[0090] Step S5.1: sorting and displaying the target factors according to the priorities corresponding to the impact of the market demand change, the impact of the exposure change, and the impact of the price change; Step S5.2: Generate corresponding operation suggestions based on the ranking display results of the target factors.

[0091] Specifically, there are many ways to determine the priority: for example, the priority can be determined according to the respective ranges of change in the impact of market demand changes, the impact of exposure changes, and the impact of price changes. That is, assuming that the market demand changes are large and the price changes are small, the impact of market demand changes is set to the highest priority, and the impact of price changes is set to the lowest priority. For another example, the priority can be set according to user needs. If the user believes that the impact of exposure changes is a factor that needs to be considered and adjusted, the impact of exposure changes can be proactively set to the highest priority.

[0092] After determining the priority, the display methods of the target factors corresponding to the impact of market demand changes, exposure changes, and price changes are adjusted according to the priority. For example, assuming that the impact of market demand changes has the highest priority, the target factors that cause the impact of market demand changes, such as a decrease in user demand for this type of product in the market, are displayed in bright colors and a larger display area. It can be understood that the specific display method is not limited here. In this embodiment, adjusting the display method of the target factors according to the priority can make the display method of the target factors more consistent with user needs and improve the display effect of the target factors.

[0093] On this basis, after determining the display method of the target factor according to the priority, the corresponding operational suggestions are generated for user reference. For example, assuming that the impact of changes in market demand has the highest priority, and the target factor is the decline in demand for this type of goods by users in the market, the operational suggestions generated are: adjust the investment ratio of the current goods, or even recommend replacing other goods for promotion and sales, and add other auxiliary or secondary operational suggestions on this basis, such as adjusting specific aspects of the target delivery strategy, etc., that is, the main part of the operational suggestions corresponds to the target factors with high priority. In this embodiment, the operational suggestions are generated according to the priority. Since the operational suggestions correspond to the target factors with high priority, they are more feasible and effective for user reference.

[0094] In one embodiment, when it is determined through analysis of preset marketing data that the change in demand for a commodity category has reached a preset range, for example, the search ranking of a commodity traffic word has dropped to a preset range, the analysis steps for the impact of exposure changes and price changes will no longer be performed, and the corresponding operational suggestions will be directly generated as follows: replace the current commodity for sales promotion. Because, compared with other factors, the impact of changes in market demand has the greatest impact on fluctuations in commodity sales, and to a large extent determines the main operational strategy adopted by sellers for the commodity. If the analysis step for the impact of changes in market demand shows that the overall demand for the commodity category in the market has dropped significantly, that is, consumers have very low demand for this type of commodity, then it is difficult for sellers to restore commodity sales to normal by adjusting operational strategies. Therefore, there is no need to analyze other factors that may cause fluctuations in commodity sales, thereby saving system computing costs and improving the efficiency of commodity sales analysis.

[0095] Furthermore, when the analysis of preset marketing data determines that the impact of competitors is the main factor causing the sales change of preset products, that is, the sales trend of competitors is better than the sales trend of preset products to a preset degree, the analysis of page display elements and price data of competitors is directly performed, and the factor with a greater influence between page display elements and price data is taken as the target factor, including: when the page display elements of competitors are adjusted significantly, the page display factor is determined as the target factor; or when the price trends between competitors and preset products are significantly different, the price factor is determined as the target factor, and corresponding operational recommendations are generated based on the target factors, and other analysis steps for the impact of exposure changes are no longer performed.

[0096] It can be understood that in actual e-commerce activities, seller users usually need to manage multiple stores and a large number of goods. If the sales volume analysis of each product in each store is required to be comprehensively analyzed through the above-mentioned external market factors, exposure change impact, and price change impact, it will cause the e-commerce ERP system to have too high operating costs and low sales volume analysis efficiency, thereby affecting the efficiency of e-commerce operation and management. For this reason, the present embodiment presets the analysis of the impact of market demand changes to have the highest priority, and determines whether it is necessary to perform analysis steps for the impact of exposure changes and price changes based on the analysis results of the preset market demand changes. It can omit the subsequent analysis steps for the impact of exposure changes and price changes for products that are greatly affected by market demand changes, thereby avoiding the e-commerce ERP system from having too high operating costs due to numerous analysis steps, and improving the analysis efficiency, thereby ultimately improving the management and operation efficiency of the e-commerce ERP system.

[0097] 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 other related technical fields are included in the patent protection scope of the present application.

Claims

1. An e-commerce product sales analysis system, characterized in that: The commodity sales volume analysis system includes a data analysis module, and the data analysis module includes a market analysis module, a traffic analysis module and a price analysis module; The market analysis module includes a traffic word module and a competitive product module, wherein the traffic word module is used to monitor the ranking changes of the traffic words of e-commerce products, and the competitive product module is used to monitor the sales volume change trend of competitive products, wherein the e-commerce products are products to be analyzed, and the competitive products are products belonging to the product category corresponding to the products to be analyzed; the traffic analysis module is used to analyze the traffic changes of the e-commerce products, and the price analysis module is used to analyze the price change trends of the e-commerce products and competitive products; The data analysis module analyzes the sales volume changes of the e-commerce products based on multiple influencing factors, wherein the influencing factors include the traffic word ranking data of the e-commerce products, the sales volume data of competing products, and the price data of the e-commerce products and competing products; The influencing factors also include the number of negative reviews on the homepage of the e-commerce product, the number of negative reviews on the Review page, and the Rating score.

2. The sales analysis system according to claim 1, characterized in that: The influencing factors also include competitor's page display element change data, and the display element change data includes competitor's title change data, main image change data and five-point description adjustment data.

3. The sales analysis system according to claim 1, characterized in that: The influencing factors also include the commodity category demand change data of the e-commerce commodity; And / or, the influencing factors also include traffic exposure data of the e-commerce products; And / or, the traffic analysis module includes an advertising traffic module for analyzing advertising traffic changes and a natural traffic module for analyzing natural traffic changes.

4. The sales analysis system according to claim 1, characterized in that: When analyzing the multiple influencing factors, the data analysis module reduces the importance weights of external market factors, exposure change impact, and price change impact from high to low.

5. The sales analysis system according to claim 1, characterized in that: When analyzing the sales volume changes of the e-commerce products, the steps of analyzing the sales volume changes include: Step S1: Obtaining sales data of the e-commerce product on the e-commerce platform; Step S2: Obtaining preset marketing data, traffic data and price data on the e-commerce platform where the e-commerce product is located; Step S3.1: Analyzing the preset marketing data to obtain the commodity category demand change and the impact of competing products corresponding to the e-commerce commodity, and determining the impact of market demand change based on the commodity category demand change and the impact of competing products; Step S3.2: Analyzing the traffic data to obtain the exposure change impact of the e-commerce product; Step S3.3: Analyzing the price data to obtain the impact of price changes on the e-commerce products; Step S4: determining the target factors causing the fluctuation in sales volume of the e-commerce product based on the impact of the market demand change, the exposure change and the price change of the e-commerce product.

6. The sales analysis system according to claim 5, characterized in that: The step S3.1 comprises: step S3.11: obtaining the search ranking data of the commodity traffic word on the e-commerce platform; step S3.12: obtaining the commodity category demand change corresponding to the e-commerce commodity based on the search ranking data of the commodity traffic word; And / or, step S3.2 comprises: Step S3.21: obtaining, on the e-commerce platform, the product advertisement sales corresponding to each advertising delivery strategy applied to the e-commerce product; Step S3.22: Determine the contribution ratio of the product advertisement sales volume to the fluctuation of the e-commerce product sales volume, and determine the advertising delivery strategy whose contribution ratio reaches a preset ratio as the target delivery strategy; Step S3.23: determining the advertisement delivery factors that cause the advertisement flow rate change based on the target delivery strategy; Step S3.24: Determine the impact of the exposure change based on the advertising delivery factors.

7. The sales analysis system according to claim 6, characterized in that: The preset marketing data also includes category sales data of the preset category in which the e-commerce product belongs, and sales data of competing products corresponding to the preset category; (1) When the data analysis module includes step S3.11, step S3.1 also includes: step S3.13: comparing the category sales data and the competitor sales data with the sales of the e-commerce product in terms of the degree of change and the trend of change to obtain a first comparison result; step S3.14: determining the impact of the competitor corresponding to the e-commerce product based on the first comparison result; (2) When the data analysis module includes step S3.23, step S3.23 includes: Step S3.231: Obtain the advertising sales contribution rates corresponding to the target delivery strategy in the exposure phase, click phase, and conversion phase respectively; Step S3.232: Determine the advertising placement factors that lead to the change in exposure of the e-commerce product according to the advertising sales contribution rate; (3) When the data analysis module includes step S3.24, step S3.24 includes: Step S3.241: Compare the page display elements of the e-commerce product with the preset display standard to obtain a second comparison result; Step S3.242: Determine the impact of natural flow change according to the second comparison result; Step S3.243: Determine the impact of the exposure change based on the advertising placement factors and the impact of the natural traffic change.

8. The sales analysis system according to claim 7, characterized in that: The step S3.242 comprises: Step S3.2421: Compare the page display elements of the competitor product with the preset display standard to obtain a third comparison result; Step S3.2422: Determine the impact of natural flow rate changes based on the second comparison result and the third comparison result.

9. The sales analysis system according to claim 5, characterized in that: The step S3.3 comprises: Step S3.31: Compare the price trends of the e-commerce product and the competing product to obtain a fourth comparison result; Step S3.32: Determine the impact of price changes based on the comparison results.

10. The sales analysis system according to claim 5, characterized in that: The steps of analyzing sales volume changes also include: Step S5.1: sorting and displaying the target factors according to the priorities corresponding to the impact of the market demand change, the impact of the exposure change, and the impact of the price change; Step S5.2: Generate corresponding operation suggestions based on the ranking display results of the target factors.

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