Commodity data processing method, equipment, device, storage medium and program product

Abstract

The present application provides a method, equipment, device, storage medium and program product for processing commodity data, which obtains a user portrait table, multiple commodity attributes and sales data corresponding to each commodity attribute, and for any user portrait value and any commodity attribute in the user portrait table, obtains, according to the sales data corresponding to each commodity attribute, the first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and the second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value, and the third sales amount of the commodity corresponding to the commodity attribute and the fourth sales amount of all commodities corresponding to the multiple commodity attributes; according to the third sales amount and the fourth sales amount, calculates the user's preference for the commodity corresponding to the commodity attribute; according to the first sales amount, the second sales amount, the third sales amount and the fourth sales amount, calculates the significance index, thereby improving the accuracy and efficiency of obtaining the user's preference.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, equipment, device, storage medium and program product for processing commodity data. Background Art

[0002] Customer-to-Manufacturer (C2M) reverse customization is a process whereby companies collect multiple preferences from users and reverse-design products based on the collected preferences, thereby embodying multiple preferences in one product. Accurate analysis of product data can correctly obtain user preferences, further ensure that the products produced can meet user needs, and thus promote transactions and increase corporate visibility. Therefore, how to process product data is key.

[0003] In the prior art, the processing of commodity data mainly involves obtaining two indicators, namely user portrait and commodity attribute, cross-analyzing the two indicators, calculating and obtaining the sales quantity after the cross-analysis, and then analyzing the sales quantity after the cross-analysis to obtain the user preference under the user portrait.

[0004] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art: in the prior art, after calculating the sales quantity through cross-analysis of the two indicators of user portrait and product attributes, it is still necessary to obtain the user's preferences through manual surveys, which makes the accuracy and efficiency of the obtained user preferences low. Summary of the invention

[0005] The present application provides a method, device, apparatus, storage medium and program product for processing commodity data to solve the problem in the prior art that user preferences can only be obtained through manual surveys, resulting in low accuracy and efficiency of the obtained user preferences.

[0006] In a first aspect, an embodiment of the present application provides a method for processing commodity data, including:

[0007] Obtain a user portrait table, multiple commodity attributes, and sales data corresponding to each commodity attribute, wherein the sales data includes information about all users who purchased commodities corresponding to the commodity attributes and sales amounts, and the multiple commodity attributes are different attribute characteristics of commodities of the same type;

[0008] For any user portrait value and any commodity attribute in the user portrait table, according to the sales data corresponding to each commodity attribute, obtain the first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and the second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value;

[0009] According to the sales data corresponding to each commodity attribute, obtaining a third sales amount of the commodity corresponding to the commodity attribute and a fourth sales amount of all commodities corresponding to the multiple commodity attributes;

[0010] Calculating and obtaining the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount;

[0011] A significance index is calculated based on the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount, and the significance index is used to indicate the significance of the user with the user portrait value in the sales of the product.

[0012] In a possible design of the first aspect, the method further includes:

[0013] Based on the obtained user preference degree for the product corresponding to each product attribute, and the significance index of the user corresponding to each user portrait value in the sales of the product, a user preference attribute degree analysis table is obtained; the user attribute preference degree analysis table includes the preference data of users for different product attributes under different user portrait values.

[0014] Optionally, the method further includes:

[0015] A product recommendation strategy is determined based on the data in the user preference attribute degree analysis table; the product recommendation strategy is used to indicate product attributes that are on an upward trend and / or a user portrait value of at least one major consumer group corresponding to each product attribute.

[0016] Optionally, the method further includes:

[0017] The user preference attribute degree analysis table is visualized, and the visualized user preference attribute degree analysis table is output through a graphical user interface.

[0018] In another possible design of the first aspect, calculating and obtaining the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount includes:

[0019] The ratio of the third sales amount to the fourth sales amount is calculated to obtain the user's preference for the commodity corresponding to the commodity attribute.

[0020] In another possible design of the first aspect, calculating and obtaining the significance index according to the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount includes:

[0021] Calculating a ratio of the first sales amount to the second sales amount to obtain a first ratio;

[0022] Calculating a ratio of the third sales amount to the fourth sales amount to obtain a second ratio;

[0023] The ratio between the first ratio and the second ratio is calculated to obtain the significance index.

[0024] In another possible design of the first aspect, the obtaining of the user portrait table, the multiple commodity attributes, and the sales data corresponding to each commodity attribute includes:

[0025] Obtaining total order data of a preset type of commodity, the total order data including the commodities sold in each order, commodity attributes, sales quantity, sales amount, and user information in the order;

[0026] Based on the pre-configured user portrait table and the total order data, data association processing is performed through user information and / or product attributes to obtain a temporary data table, which includes the user portrait table, the multiple product attributes and the sales data corresponding to each product attribute.

[0027] In a second aspect, an embodiment of the present application provides a device for processing commodity data, including:

[0028] An acquisition module is used to acquire a user portrait table, multiple commodity attributes, and sales data corresponding to each commodity attribute. The sales data includes information about all users who purchased commodities corresponding to the commodity attributes and sales amounts. The multiple commodity attributes are different attribute characteristics of commodities of the same type.

[0029] A processing module is used for obtaining, for any user portrait value and any commodity attribute in the user portrait table, a first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and a second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value according to the sales data corresponding to each commodity attribute;

[0030] The processing module is further configured to obtain, based on the sales data corresponding to each commodity attribute, a third sales amount of the commodity corresponding to the commodity attribute and a fourth sales amount of all commodities corresponding to the multiple commodity attributes;

[0031] The processing module is further used to calculate and obtain the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount;

[0032] The processing module is also used to calculate a significance index based on the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount, wherein the significance index is used to indicate the significance of the user with the user portrait value in the sales of the product.

[0033] In a possible design of the second aspect, the processing module is further used to:

[0034] Based on the obtained user preference degree for the product corresponding to each product attribute, and the significance index of the user corresponding to each user portrait value in the sales of the product, a user preference attribute degree analysis table is obtained; the user attribute preference degree analysis table includes the preference data of users for different product attributes under different user portrait values.

[0035] Optionally, the processing module is further used to:

[0036] A product recommendation strategy is determined based on the data in the user preference attribute degree analysis table; the product recommendation strategy is used to indicate product attributes that are on an upward trend and / or a user portrait value of at least one major consumer group corresponding to each product attribute.

[0037] Optionally, the processing module is further used to:

[0038] The user preference attribute degree analysis table is visualized, and the visualized user preference attribute degree analysis table is output through a graphical user interface.

[0039] In another possible design of the second aspect, the processing module is specifically configured to:

[0040] The ratio of the third sales amount to the fourth sales amount is calculated to obtain the user's preference for the commodity corresponding to the commodity attribute.

[0041] In yet another possible design of the second aspect, the processing module is specifically configured to:

[0042] Calculating a ratio of the first sales amount to the second sales amount to obtain a first ratio;

[0043] Calculating a ratio of the third sales amount to the fourth sales amount to obtain a second ratio;

[0044] The ratio between the first ratio and the second ratio is calculated to obtain the significance index.

[0045] In yet another possible design of the second aspect, the acquisition module is specifically configured to:

[0046] Obtaining total order data of a preset type of commodity, the total order data including the commodities sold in each order, commodity attributes, sales quantity, sales amount, and user information in the order;

[0047] Based on the pre-configured user portrait table and the total order data, data association processing is performed through user information and / or product attributes to obtain a temporary data table, which includes the user portrait table, the multiple product attributes and the sales data corresponding to each product attribute.

[0048] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and computer program instructions stored in the memory and executable on the processor, wherein the processor is used to implement the first aspect and the methods provided by various possible designs when executing the computer program instructions.

[0049] In a fourth aspect, an embodiment of the present application may provide a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect and the methods provided by various possible designs.

[0050] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the first aspect and the methods provided by various possible designs.

[0051] The method, equipment, device, storage medium and program product for processing commodity data provided by the embodiment of the present application include: obtaining a user portrait table, multiple commodity attributes and sales data corresponding to each commodity attribute, for any user portrait value and any commodity attribute in the user portrait table, according to the sales data corresponding to each commodity attribute, obtaining the first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and the second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value, according to the sales data corresponding to each commodity attribute, obtaining the third sales amount of the commodity corresponding to the commodity attribute and the fourth sales amount of all commodities corresponding to the multiple commodity attributes, according to the third sales amount and the fourth sales amount, calculating the user's preference for the commodity corresponding to the commodity attribute, and calculating the significance index according to the first sales amount, the second sales amount, the third sales amount and the fourth sales amount. In the technical scheme, by processing the user portrait table, multiple commodity attributes and the sales data corresponding to each commodity attribute, the preference degree and significance index that can directly measure the user's preference are obtained, and the commodity attributes preferred by the user can be screened, thereby improving the accuracy and efficiency of obtaining the user's preference. The method does not require manual research, reducing the workload, manpower cost and material cost of the processing process. Furthermore, the staff can provide more accurate marketing plans for users in the user portrait table based on the degree of preference and significance index, and realize personalized advertising. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 A schematic diagram of an application scenario of the method for processing commodity data provided in an embodiment of the present application;

[0054] Figure 2 A flowchart of a first embodiment of a method for processing commodity data provided in an embodiment of the present application;

[0055] Figure 3A A schematic diagram of the analysis results of the user preference attributes of a third-tier city for a user portrait value provided in an embodiment of the present application;

[0056] Figure 3B A schematic diagram of the analysis results of the degree of preference attributes of users aged 26 to 35 years old provided in the embodiment of the present application as user portrait values;

[0057] Figure 4A flowchart of a second embodiment of a method for processing commodity data provided in an embodiment of the present application;

[0058] Figure 5 A flowchart of a third embodiment of the method for processing commodity data provided in the embodiment of the present application;

[0059] Figure 6 A schematic diagram of the structure of a device for processing commodity data provided in an embodiment of the present application;

[0060] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0061] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0064] In addition, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0065] Before introducing the embodiments of the present application, the application background of the embodiments of the present application is first explained:

[0066] In actual production, designers in each company can design products based on currently popular elements and innovative elements, thereby designing a variety of products. After that, the company will put a variety of products into production based on the design results, and put the produced products on the market so that users can choose the products they need. However, in recent years, the market has gradually entered the stock market from the incremental market, and the problem of product homogeneity has begun to emerge. If the products produced cannot meet most of the needs of users and do not meet the preferences of users, it will not only affect the user experience, but also affect the sales of the products.

[0067] C2M reverse customization usually means that enterprises collect multiple preferences of users, and reverse-design products based on the collected preferences, so that multiple preferences are concentrated in one product, so that when users spend the same price to buy goods, the product can meet more preferences and needs of users. Accurate analysis of product data can correctly obtain user preferences, and can produce products more targeted, thereby ensuring that the products produced can maximize the satisfaction of users' personalized preferences, promote transactions, and increase corporate visibility. Therefore, how to process product data is the key.

[0068] In the prior art, there are mainly two ways to process commodity data:

[0069] 1) Obtain two indicators, user portrait and product attributes, calculate the sales quantity under different enumeration values ​​of the two indicators respectively, and obtain user preferences based on the sales data analysis. However, this method can only analyze the two indicators of user portrait and product attributes separately, which is relatively simple and cannot perform cross-analysis on the two indicators of user portrait and product attributes at the same time.

[0070] 2) Obtain two indicators, namely user portrait and product attributes, conduct cross-analysis on the two indicators, calculate the sales volume after the cross-analysis, and then analyze the sales volume after the cross-analysis to obtain the user preference under the user portrait. However, after calculating the sales volume after the cross-analysis of the two indicators, this method still needs to obtain the user's preference through manual research, which makes the accuracy and efficiency of the obtained user's preference low.

[0071] In response to the above problems, the invention of this application is as follows: In the prior art, after calculating the two indicators of user portrait and product attributes, due to the lack of suitable analysis technology to determine objective data indicators to measure user preferences, it is still necessary to obtain user preferences through manual research, resulting in low accuracy and efficiency of the obtained user preferences. Based on this, the inventor found that if the degree of preference and significance in the sale of goods can be obtained based on user portraits and product attributes, the degree of preference and significance can objectively measure the user's preferences, which can solve the problem of obtaining user preferences through manual research in the prior art, thereby improving the accuracy and efficiency of the obtained user preferences, and may further save labor costs.

[0072] For example, the method for processing commodity data provided in the embodiment of the present application can be applied to Figure 1 In a schematic diagram of an application scenario shown. Figure 1 The following is a schematic diagram of an application scenario of the method for processing commodity data provided in the embodiment of the present application, which is used to solve the above technical problems. Figure 1 As shown, the application scenario may include: a server 11 and a data storage device 12 connected to the server.

[0073] For example, in Figure 1 In the application scenario shown, the data storage device 12 can store a user portrait table, multiple product attributes, and sales data corresponding to each product attribute. The server 11 can execute the program code of the product data processing method based on the user portrait table, multiple product attributes, and sales data corresponding to each product attribute in the data storage device 12, and determine the significance index for indicating the significance of the user of the user portrait value in the sales of the product, as well as the user's preference for the product corresponding to the product attribute.

[0074] Optionally, the server 11 may also store the obtained processing results in the data storage device 12 so that subsequent staff can formulate a more accurate marketing plan based on the processing results.

[0075] It should be noted that the attached Figure 1 This is only a schematic diagram of an application scenario provided by the embodiment of the present application. Figure 1 does not limit the equipment included in Figure 1 The positional relationship between the devices in the Figure 1 In the embodiment, the data storage device 12 can be an external memory relative to the server 11. In other cases, the data storage device 12 can also be placed in the server 11.

[0076] In practical applications, since the terminal device is also a processing device with data processing capabilities, the above Figure 1The server in the application scenario shown can also be implemented by a terminal device. In the embodiment of the present application, the server and the terminal device for data processing can be collectively referred to as an electronic device.

[0077] The technical solution of the present application is described in detail below through specific embodiments.

[0078] It should be noted that the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0079] Figure 2 The flowchart of the first embodiment of the method for processing commodity data provided in the present application is as follows. Figure 2 As shown, the method for processing the commodity data may include the following steps:

[0080] S101: Obtain a user portrait table, multiple product attributes, and sales data corresponding to each product attribute.

[0081] In this step, the electronic device needs to obtain a user portrait table, multiple product attributes, and sales data corresponding to each product attribute, so as to facilitate subsequent analysis, so that the staff can formulate a more accurate marketing plan based on the analysis results.

[0082] The sales data includes all user information and sales amounts of the products corresponding to the purchased product attributes. Multiple product attributes are different attribute characteristics of the same type of products.

[0083] In a possible implementation, the electronic device can obtain the total order data of a preset type of commodity, and based on the pre-configured user portrait table and the total order data, perform data association processing through user information and / or commodity attributes to obtain a temporary data table. After the electronic device obtains the total order data, it obtains the multiple commodities corresponding to the user in the user portrait table and the sales amount corresponding to each commodity, and obtains the commodity attributes corresponding to the commodity, and stores the user portrait table, the multiple commodity attributes, and the sales data corresponding to each commodity attribute in the temporary data table.

[0084] Exemplarily, the preset type can be a first-level category type, a second-level category type, a third-level category type, or a fourth-level category type. It can be selected according to actual conditions, and the embodiments of the present application do not impose specific restrictions on this.

[0085] Among them, the total order data includes the goods sold in each order, product attributes, sales quantity, sales amount, and user information in the order; the temporary data table includes the user portrait table, multiple product attributes and the sales data corresponding to each product attribute.

[0086] Exemplarily, the preset type is milk as an example for explanation. The electronic device obtains the total order data of all milk, and then obtains the multiple milk products corresponding to the user information in the total order data and the sales amount corresponding to each milk product based on the user information in the pre-configured user portrait table. Furthermore, the electronic device can obtain the stock keeping unit (SKU) corresponding to each milk product, obtain the product attributes of each milk product through the association between the SKU and the product attributes in the attribute importance ranking table, and store the multiple milk products and the sales amount and product attributes corresponding to each milk product in the temporary data table.

[0087] In this example, the product attributes of each pure milk product can be skim, whole fat, sugar-free, etc., which can be set according to actual conditions, and the embodiments of the present application do not specifically limit this.

[0088] S102: For any user portrait value and any product attribute in the user portrait table, based on the sales data corresponding to each product attribute, obtain the first sales amount of the product corresponding to the product attribute of the user under the user portrait value and the second sales amount of all products corresponding to multiple product attributes under the user portrait value.

[0089] In this step, after obtaining the user portrait table, multiple product attributes and the sales data corresponding to each product attribute, the electronic device can process them to obtain the first sales amount and the second sales amount, so as to facilitate the subsequent calculation of the significance index indicating the significance of the user with the user portrait value in the sales of the product based on the first sales amount and the second sales amount.

[0090] For example, the user portrait value of the user may be female, may live in a third-tier city, or may be between 18 and 22 years old. It can be set according to actual conditions, and the embodiments of the present application do not impose specific restrictions on this.

[0091] Optionally, the first sales amount may be the sum of sales amounts of all SKUs corresponding to the commodity attributes of the user under the user portrait value, and the second sales amount may be the sum of sales amounts of all SKUs of the user under the user portrait value, wherein all SKUs belong to the same preset type.

[0092] For example, the first sales amount and the second sales amount can be calculated and obtained by using the SUM()OVER() window function. Some fields involved in the calculation process, as well as the corresponding field types and field explanations can be shown in Table 1:

[0093] Table 1

[0094]

[0095]

[0096] It should be understood that Table 1 is only an example of some fields. In the actual process of calculating the first sales amount and the second sales amount through the SUM()OVER() window function, other fields can also be used to participate in the calculation and can be used according to actual conditions. The embodiments of the present application do not impose specific restrictions on this.

[0097] S103: According to the sales data corresponding to each commodity attribute, a third sales amount of the commodity corresponding to the commodity attribute and a fourth sales amount of all commodities corresponding to multiple commodity attributes are obtained.

[0098] In this step, the electronic device may further calculate the third sales amount and the fourth sales amount based on the sales data corresponding to each commodity attribute, so as to calculate the preference degree and the significance index.

[0099] Optionally, the third sales amount may be the sum of sales amounts of all SKUs corresponding to the commodity attribute, and the fourth sales amount may be the sum of sales amounts of all SKUs, wherein all SKUs belong to the same preset type.

[0100] Exemplarily, the third sales amount and the fourth sales amount can be calculated and obtained by using the SUM()OVER() window function.

[0101] S104: Calculate and obtain the user's preference for the commodity corresponding to the commodity attribute based on the third sales amount and the fourth sales amount.

[0102] In this step, after the electronic device obtains the third sales amount and the fourth sales amount, it can further process them to obtain the degree of preference for the product corresponding to the product attribute.

[0103] In one achievable manner, the electronic device may calculate the ratio of the third sales amount to the fourth sales amount to obtain the user's preference for the product corresponding to the product attribute. That is, after the electronic device calculates the ratio, the ratio is determined as the preference for the product corresponding to the product attribute.

[0104] Exemplarily, the degree of preference may be represented by the formula: degree of preference = third sales amount / fourth sales amount.

[0105] The value range of preference is from 0 to 1.

[0106] In actual application, after obtaining the preference levels of multiple commodity attributes corresponding to commodities, the electronic device can calculate the average of the multiple preference levels and determine the average preference level. Furthermore, the electronic device can compare the preference levels of multiple commodity attributes corresponding to commodities with the average preference level to determine the commodity attributes that the user prefers more.

[0107] S105: Calculate and obtain a significance index based on the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount.

[0108] In the prior art, significance is used to indicate the differences in the concerns of user groups with different characteristics. In the actual calculation and application process, significance can be calculated by the following formula: significance = the proportion of groups with a certain characteristic in the target group / the proportion of groups with the same characteristic in the whole. The larger the significance, the stronger the degree of fit of the target group.

[0109] In this step, the electronic device can further calculate the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount to obtain a significance index, so that subsequent staff can analyze the significance index and further formulate marketing strategies.

[0110] In one possible implementation, the electronic device may calculate the ratio of the first sales amount to the second sales amount to obtain a first ratio; then calculate the ratio of the third sales amount to the fourth sales amount to obtain a second ratio; and finally calculate the ratio between the first ratio and the second ratio to obtain a significance index.

[0111] Exemplarily, the significance index may be obtained by the following formula: significance index=first ratio / second ratio.

[0112] Among them, the significance index is used to indicate the significance of the user with the user portrait value in the sales of the product.

[0113] In actual application, after the electronic device obtains the significance index of the product corresponding to the product attribute, it can be compared with the average significance index, where the average significance index is 1. If the significance index is higher than the average significance index, it means that the user under the user portrait table pays more attention to the product attribute corresponding to the significance index than the overall level.

[0114] The method for processing commodity data provided by the embodiment of the present application is to obtain a user portrait table, multiple commodity attributes and sales data corresponding to each commodity attribute, and for any user portrait value and any commodity attribute in the user portrait table, obtain the first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and the second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value according to the sales data corresponding to each commodity attribute, obtain the third sales amount of the commodity corresponding to the commodity attribute and the fourth sales amount of all commodities corresponding to the multiple commodity attributes according to the sales data corresponding to each commodity attribute, calculate the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount, and calculate the significance index according to the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount. By processing the user portrait table, multiple commodity attributes and the sales data corresponding to each commodity attribute, the preference degree and significance index that can directly measure the user's preference are obtained, and the commodity attributes preferred by the user can be screened, thereby improving the accuracy and efficiency of obtaining the user's preference. This method does not require manual research, reducing the workload, manpower cost and material cost of the processing process. Furthermore, the staff can provide more accurate marketing plans for users in the user portrait table based on the degree of preference and significance index, and realize personalized advertising.

[0115] In addition, the method provided in the embodiment of the present application can also use the user portrait table, multiple product attributes and the sales data corresponding to each product attribute to establish a user preference attribute analysis model. The user preference attribute analysis model can deeply analyze the attribute preferences of users under different user portrait tables, so that staff can formulate more accurate marketing plans based on the attribute preferences, reducing the investment of manpower and material resources.

[0116] Optionally, in some embodiments, the method for processing commodity data may further include the following steps:

[0117] According to the obtained user preference degree for each commodity attribute corresponding to the commodity, and the significance index of each user portrait value corresponding to the user in the sales of the commodity, a user preference attribute degree analysis table is obtained.

[0118] The user attribute preference analysis table includes preference data of users for different commodity attributes under different user portrait values, and the preference data includes significance index and preference degree.

[0119] Optionally, the electronic device may also calculate the year-on-year growth rate of the preference level of the commodity attribute based on the commodity attribute and the sales data corresponding to each commodity attribute. The year-on-year growth rate may be calculated by the formula: year-on-year growth rate = (this year's preference level corresponding to the commodity attribute - last year's preference level corresponding to the commodity attribute) / last year's preference level corresponding to the commodity attribute.

[0120] For example, taking the preset type as milk and the user portrait value as a third-tier city as an example, the user attribute preference analysis table can be shown in Table 2.

[0121] Table 2

[0122] Product attributes Preference Significance Year-on-year growth rate Degreasing 5％ 1.20 5 Low fat 18％ 1.12 2 Full fat 82％ 1.09 1 Contains sugar 90％ 1.10 1

[0123] For example, taking the preset type as milk and the user portrait value as the age range of 26 to 35 years old as an example, the user attribute preference analysis table can be shown in Table 3.

[0124] Table 3

[0125] Product attributes Preference Significance Year-on-year growth rate Degreasing 4％ 1.16 5 Low fat 17％ 1.10 2 Full fat 83％ 1.08 1 Contains sugar 92％ 1.11 1

[0126] It is understandable that only preference data, significance index and year-on-year growth rate corresponding to some user portrait values ​​and product attributes are shown in Tables 2 and 3. User portrait values ​​and product attributes may also have other contents and forms, which can be determined according to actual needs and will not be described here.

[0127] The method provided in the embodiment of the present application can more clearly show the degree of change in the attribute preference of the user portrait value under the user portrait table by calculating the year-on-year growth rate of the product attribute, thereby further improving the accuracy and efficiency of the obtained user preferences.

[0128] Optionally, in some embodiments, the method for processing commodity data may further include the following steps:

[0129] Determine the product recommendation strategy based on the data in the user preference attribute degree analysis table.

[0130] The product recommendation strategy is used to indicate product attributes that are on an upward trend and / or the user portrait value of at least one major consumer group corresponding to each product attribute.

[0131] For example, according to Tables 2 and 3, it can be seen that the market share of full-fat milk is higher, but the significance of skimmed milk is higher. Therefore, the electronic device can determine the product recommendation strategy so that the staff can formulate a marketing strategy based on the product recommendation strategy. The marketing strategy can be to recommend skimmed milk to users whose user portrait value under the user portrait table is a third-tier city and / or whose age is 26 to 35 years old.

[0132] Optionally, in some embodiments, the method for processing commodity data may further include the following steps:

[0133] The user preference attribute degree analysis table is visualized and the visualized user preference attribute degree analysis table is output through a graphical user interface.

[0134] For example, Figure 3A A schematic diagram of the analysis results of the user preference attributes of a third-tier city user profile value provided in the embodiment of the present application, Figure 3B The user portrait value provided in the embodiment of the present application is a schematic diagram of the analysis result of the preference attribute degree of users aged 26 to 35. Figure 3A , Figure 3B As shown, the electronic device can output a user preference attribute degree analysis table after visualization through a graphical user interface in the form of a user preference attribute degree analysis result schematic diagram, with the horizontal axis being the preference degree, which can be used to represent the user's purchase ratio, and the vertical axis being the significance index. The size of the bubble is used to represent the year-on-year growth rate of the commodity attribute, and the larger the bubble, the greater the year-on-year growth rate. Figure 3A , Figure 3B It can be seen that in the current market, full-fat pure milk has a high market share, but with the upgrading of consumption and the refinement of scenarios, the upward trend of skim sugar-free low-calorie fitness milk is irreversible, and electronic devices can be based on Figure 3A , Figure 3B Determine product recommendation strategy.

[0135] Figure 4 The flowchart of the second embodiment of the method for processing commodity data provided in the embodiment of the present application is as follows. Figure 4 As shown, the method for processing the commodity data may include the following steps:

[0136] Step 1: Get the total order data, user profile table, and attribute importance ranking table of the preset type of goods.

[0137] In this step, the attribute importance ranking table includes product attributes and the SKU corresponding to each product attribute.

[0138] Optionally, the attribute importance ranking table may also include the importance of each product attribute.

[0139] Step 2: Get the temporary data table.

[0140] In this step, the electronic device performs data association processing based on the pre-configured user portrait table and total order data through user information and / or product attributes to obtain a temporary data table.

[0141] Step 3: Obtain the user preference attribute degree analysis table.

[0142] In this step, the electronic device further processes the data in the temporary data table to obtain the user's preference for the product corresponding to the product attribute and the significance of the product attribute under different user portrait values.

[0143] Figure 5The flowchart of the third embodiment of the method for processing commodity data provided in the embodiment of the present application is as follows. Figure 5 As shown, the method for processing the commodity data may include the following steps:

[0144] Step 1: Get the total order data of the preset type of goods.

[0145] Step 2: Process the total order data and obtain the user order summary information table.

[0146] In this step, the electronic device obtains the total order data, deletes the product data corresponding to the gifts in the total order data, processes the data of the products whose sales quantity of the SKU is not 0, and generates a user order summary information table.

[0147] Step 3: Use the attribute importance ranking table to associate each SKU in the user order summary information table with the product attributes.

[0148] Step 4: According to the user portrait table, associate the user information with each SKU and corresponding product attributes in the user order summary information table to obtain a temporary data table.

[0149] Step 5: Process the temporary data table to obtain the user's preference for the product attributes corresponding to the product and the significance index of the product attributes corresponding to the user's user portrait value.

[0150] Step 6: Obtain a user preference attribute degree analysis table based on the user's preference degree for each product attribute and the significance index of each user portrait value in the sales of the product.

[0151] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0152] Figure 6 This is a schematic diagram of the structure of the commodity data processing device provided in the embodiment of the present application. Figure 6 As shown, the commodity data processing device includes:

[0153] Acquisition module 51, used to acquire user portrait table, multiple commodity attributes and sales data corresponding to each commodity attribute, the sales data includes all user information and sales amount of the commodities corresponding to the commodity attributes, and the multiple commodity attributes are different attribute characteristics of the same type of commodities;

[0154] Processing module 52, for obtaining, for any user portrait value and any commodity attribute in the user portrait table, a first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and a second sales amount of all commodities corresponding to multiple commodity attributes under the user portrait value according to the sales data corresponding to each commodity attribute;

[0155] The processing module 52 is further used to obtain, according to the sales data corresponding to each commodity attribute, a third sales amount of the commodity corresponding to the commodity attribute and a fourth sales amount of all commodities corresponding to multiple commodity attributes;

[0156] The processing module 52 is further used to calculate and obtain the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount;

[0157] The processing module 52 is also used to calculate and obtain a significance index based on the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount. The significance index is used to indicate the significance of the user with the user portrait value in the sales of the product.

[0158] In a possible design of the embodiment of the present application, the processing module 52 is further configured to:

[0159] Based on the obtained user preference degree for each product attribute corresponding to the product, and the significance index of each user portrait value corresponding to the user in the sales of the product, a user preference attribute degree analysis table is obtained; the user attribute preference degree analysis table includes the preference data of users for different product attributes under different user portrait values.

[0160] Optionally, the processing module 52 is further configured to:

[0161] Determine a product recommendation strategy based on data in the user preference attribute degree analysis table; the product recommendation strategy is used to indicate product attributes that are on an upward trend and / or user portrait values ​​of at least one major consumer group corresponding to each product attribute.

[0162] Optionally, the processing module 52 is further configured to:

[0163] The user preference attribute degree analysis table is visualized and the visualized user preference attribute degree analysis table is output through a graphical user interface.

[0164] In another possible design of the embodiment of the present application, the processing module 52 is specifically configured to:

[0165] The ratio of the third sales amount to the fourth sales amount is calculated to obtain the user's preference for the product corresponding to the product attribute.

[0166] In another possible design of the embodiment of the present application, the processing module 52 is specifically configured to:

[0167] Calculating the ratio of the first sales amount to the second sales amount to obtain a first ratio;

[0168] Calculate the ratio of the third sales amount to the fourth sales amount to obtain a second ratio;

[0169] The ratio between the first ratio and the second ratio is calculated to obtain a significance index.

[0170] In another possible design of the embodiment of the present application, the acquisition module is specifically used to:

[0171] Get the total order data of the preset type of goods, which includes the goods sold in each order, the attributes of the goods, the sales quantity, the sales amount, and the user information in the order;

[0172] Based on the pre-configured user portrait table and total order data, data association processing is performed through user information and / or product attributes to obtain a temporary data table, which includes the user portrait table, multiple product attributes and sales data corresponding to each product attribute.

[0173] The commodity data processing device provided in the embodiment of the present application can be used to execute the commodity data processing device in any of the above embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0174] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0175] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device may include: a processor 61, a memory 62, and computer program instructions stored in the memory 62 and executable on the processor 61, and the processor 61 implements a processing device for commodity data provided by any of the aforementioned embodiments when executing the computer program instructions.

[0176] Optionally, the electronic device may further include an interface for interacting with other devices.

[0177] Optionally, the above-mentioned components of the electronic device may be connected via a system bus.

[0178] The memory 62 may be a separate storage unit or a storage unit integrated in the processor. The number of processors may be one or more.

[0179] It should be understood that the processor 61 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0180] The system bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0181] All or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disc and any combination thereof.

[0182] The electronic device provided in the embodiment of the present application can be used to execute the commodity data processing device provided in any of the above method embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0183] An embodiment of the present application provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on a computer, the computer executes the above-mentioned commodity data processing device.

[0184] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0185] Optionally, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0186] An embodiment of the present application also provides a computer program product, which includes a computer program. The computer program is stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, the above-mentioned commodity data processing device can be implemented.

[0187] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for processing commodity data, characterized in that: include: Obtain a user portrait table, multiple commodity attributes, and sales data corresponding to each commodity attribute, wherein the sales data includes information about all users who purchased commodities corresponding to the commodity attributes and sales amounts, and the multiple commodity attributes are different attribute characteristics of commodities of the same type; For any user portrait value and any commodity attribute in the user portrait table, according to the sales data corresponding to each commodity attribute, obtain the first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and the second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value; According to the sales data corresponding to each commodity attribute, obtaining a third sales amount of the commodity corresponding to the commodity attribute and a fourth sales amount of all commodities corresponding to the multiple commodity attributes; Calculating and obtaining the user's preference for the commodity corresponding to the commodity attribute based on the third sales amount and the fourth sales amount; Calculate and obtain a significance index based on the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount, wherein the significance index is used to indicate the significance of the user with the user portrait value in the sales of the product, and the significance is calculated based on the proportion of the group with a certain characteristic in the target group and the proportion of the group with the same characteristic in the whole group; The obtaining of the user portrait table, multiple commodity attributes and sales data corresponding to each commodity attribute includes: obtaining total order data of commodities of a preset type, wherein the total order data includes commodities sold in each order, commodity attributes, sales quantity, sales amount, and user information in the order; Based on the pre-configured user portrait table and the total order data, data association processing is performed through user information and / or product attributes to obtain a temporary data table, which includes the user portrait table, the multiple product attributes and the sales data corresponding to each product attribute.

2. The method according to claim 1, characterized in that: The method further comprises: Based on the obtained user preference degree for the product corresponding to each product attribute, and the significance index of the user corresponding to each user portrait value in the sales of the product, a user preference attribute degree analysis table is obtained; the user attribute preference degree analysis table includes the preference data of users for different product attributes under different user portrait values.

3. The method according to claim 2, characterized in that The method further comprises: A product recommendation strategy is determined based on the data in the user preference attribute degree analysis table; the product recommendation strategy is used to indicate product attributes that are on an upward trend and / or a user portrait value of at least one major consumer group corresponding to each product attribute.

4. The method according to claim 2 or 3, characterized in that: The method further comprises: The user preference attribute degree analysis table is visualized, and the visualized user preference attribute degree analysis table is output through a graphical user interface.

5. The method according to any one of claims 1 to 3, characterized in that: The calculating and obtaining the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount includes: The ratio of the third sales amount to the fourth sales amount is calculated to obtain the user's preference for the commodity corresponding to the commodity attribute.

6. The method according to any one of claims 1 to 3, characterized in that: The step of calculating and obtaining a significance index according to the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount includes: Calculating a ratio of the first sales amount to the second sales amount to obtain a first ratio; Calculating a ratio of the third sales amount to the fourth sales amount to obtain a second ratio; The ratio between the first ratio and the second ratio is calculated to obtain the significance index.

7. A commodity data processing device, characterized in that: include: An acquisition module is used to acquire a user portrait table, multiple commodity attributes, and sales data corresponding to each commodity attribute. The sales data includes information about all users who purchased commodities corresponding to the commodity attributes and sales amounts. The multiple commodity attributes are different attribute characteristics of commodities of the same type. A processing module is used for obtaining, for any user portrait value and any commodity attribute in the user portrait table, a first sales amount of the commodity corresponding to the commodity attribute of the user under the user portrait value and a second sales amount of all commodities corresponding to the multiple commodity attributes under the user portrait value according to the sales data corresponding to each commodity attribute; The processing module is further used to obtain, according to the sales data corresponding to each commodity attribute, a third sales amount of the commodity corresponding to the commodity attribute and a fourth sales amount of all commodities corresponding to the multiple commodity attributes; The processing module is further used to calculate and obtain the user's preference for the commodity corresponding to the commodity attribute according to the third sales amount and the fourth sales amount; The processing module is further used to calculate a significance index according to the first sales amount, the second sales amount, the third sales amount, and the fourth sales amount, wherein the significance index is used to indicate the significance of the user with the user portrait value in the sales of the commodity, and the significance is calculated based on the proportion of the group with a certain characteristic in the target group and the proportion of the group with the same characteristic in the whole group; The acquisition module is specifically used to: acquire total order data of commodities of a preset type, wherein the total order data includes commodities sold in each order, commodity attributes, sales quantity, sales amount, and user information in the order; Based on the pre-configured user portrait table and the total order data, data association processing is performed through user information and / or product attributes to obtain a temporary data table, which includes the user portrait table, the multiple product attributes and the sales data corresponding to each product attribute.

8. An electronic device, comprising: A processor, a memory, and computer program instructions stored in the memory and executable on the processor, wherein the processor is used to implement the method for processing commodity data as described in any one of claims 1 to 6 when executing the computer program instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for processing commodity data according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it is used to implement the method for processing commodity data according to any one of claims 1 to 6.

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