CN117196700A - Product release optimization method and system of cross-border e-commerce platform - Google Patents
Product release optimization method and system of cross-border e-commerce platform Download PDFInfo
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- CN117196700A CN117196700A CN202311240007.6A CN202311240007A CN117196700A CN 117196700 A CN117196700 A CN 117196700A CN 202311240007 A CN202311240007 A CN 202311240007A CN 117196700 A CN117196700 A CN 117196700A
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Abstract
The invention discloses a product release optimizing method and a system of a cross-border e-commerce platform, which relate to the technical field of artificial intelligence, and the method comprises the following steps: obtaining a product warehouse to be put in; generating multiple types of products to be put in; obtaining a preset matrix construction constraint, and carrying out data integration on the multiple types of products to be put in according to the preset matrix construction constraint to generate a matrix of the products to be put in; constructing a user portrait set; carrying out product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes; obtaining a product release scheme through a product release decision maker in the cross-border e-commerce platform; according to the product release scheme, the product release of the cross-border e-commerce platform is executed, the problem that the product release method in the prior art is poor in yield and instantaneity due to insufficient rigor and insufficient completeness is solved, and good display about the product release effect is not achieved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a product release optimizing method and system of a cross-border e-commerce platform.
Background
With the development of the internet and mobile commerce, electronic commerce is also increasingly developed. Many businesses are beginning to establish e-commerce departments, and network marketing is an important component in e-commerce. Network marketing can promote business and product of trade company with low cost, high efficiency, by a wide margin on the network, but most enterprises can't be with the effectual conversion of client click rate to the yield, how accurate with the product input to target customer, can control marketing cost to the enterprise accurately, promote the product yield, have very important meaning.
The problem of poor yield and real-time performance caused by the insufficient rigor and insufficient completeness of the product release method in the prior art is that the final product release effect cannot be well displayed.
Disclosure of Invention
The application provides a product release optimizing method and system of a cross-border e-commerce platform, which solve the problems of poor yield and instantaneity caused by insufficient rigor and insufficient completeness of a product release method in the prior art, and realize that good exhibition about a product release effect cannot be obtained.
In view of the above problems, the application provides a product release optimization method of a cross-border e-commerce platform.
In a first aspect, the present application provides a product delivery optimization method for a cross-border e-commerce platform, the method comprising: obtaining a product release time zone constraint characteristic, and screening a product library in a cross-border electronic commerce platform according to the product release time zone constraint characteristic to obtain a product library to be released; generating multiple types of products to be put, wherein the multiple types of products to be put are obtained by carrying out cluster analysis on the product library to be put; obtaining a preset matrix construction constraint, and carrying out data integration on the multiple types of products to be put in according to the preset matrix construction constraint to generate a matrix of the products to be put in; the user terminal of the cross-border e-commerce platform is interacted to obtain user information bases corresponding to a plurality of users, and a user portrait set is constructed according to the user information bases; carrying out product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes; carrying out delivery decision analysis on the to-be-delivered product matrix according to the multiple product delivery value indexes by a product delivery decision maker in the cross-border electronic commerce platform to obtain a product delivery scheme; and executing the product release of the cross-border e-commerce platform according to the product release scheme.
In a second aspect, the present application provides a product delivery optimization system of a cross-border e-commerce platform, the system comprising: constraint characteristic acquisition module: obtaining a product release time zone constraint characteristic, and screening a product library in a cross-border electronic commerce platform according to the product release time zone constraint characteristic to obtain a product library to be released; the product generation module: generating multiple types of products to be put, wherein the multiple types of products to be put are obtained by carrying out cluster analysis on the product library to be put; and a data integration module: obtaining a preset matrix construction constraint, and carrying out data integration on the multiple types of products to be put in according to the preset matrix construction constraint to generate a matrix of the products to be put in; the information base acquisition module: the user terminal of the cross-border e-commerce platform is interacted to obtain user information bases corresponding to a plurality of users, and a user portrait set is constructed according to the user information bases; a value identification module: carrying out product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes; a delivery decision maker module: carrying out delivery decision analysis on the to-be-delivered product matrix according to the multiple product delivery value indexes by a product delivery decision maker in the cross-border electronic commerce platform to obtain a product delivery scheme; and a product release module: and executing the product release of the cross-border e-commerce platform according to the product release scheme.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
according to the product release optimizing method and system for the cross-border e-commerce platform, the product library in the cross-border e-commerce platform is screened according to the product release time zone constraint characteristics, multiple types of products to be released are generated, the preset matrix construction constraint is obtained, the multiple types of products to be released are subjected to data integration according to the preset matrix construction constraint, the user end of the cross-border e-commerce platform is generated, the user information libraries corresponding to multiple users are obtained, the user image set is constructed according to the user information library, the product release value recognition is carried out on the product matrix to be released according to the user image set, multiple product release value indexes are obtained, the product release scheme is obtained through the product release decision maker in the cross-border e-commerce platform, and finally the product release of the cross-border e-commerce platform is executed according to the product release scheme, so that the problem that the success rate and the real-time performance are poor due to insufficient in the product release method in the prior art is solved, and good effects about the release cannot be achieved.
Drawings
FIG. 1 is a schematic flow diagram of a product release optimizing method of a cross-border e-commerce platform;
fig. 2 is a schematic diagram of a product delivery optimizing system structure of a cross-border e-commerce platform.
Reference numerals illustrate: the system comprises a constraint characteristic acquisition module 11, a product generation module 12, a data integration module 13, an information base acquisition module 14, a value identification module 15, a delivery decision maker module 16 and a product delivery module 17.
Detailed Description
The application provides a product release optimizing method and a system of a cross-border e-commerce platform, which are characterized in that a product release time zone constraint characteristic is obtained, a product library in the cross-border e-commerce platform is screened according to the product release time zone constraint characteristic, a plurality of types of products to be released are generated, a preset matrix construction constraint is obtained, the plurality of types of products to be released are subjected to data integration according to the preset matrix construction constraint, a user end of the cross-border e-commerce platform is generated, a plurality of user information libraries corresponding to users are obtained, a user image set is constructed according to the user information library, product release value identification is carried out on the product to be released matrix according to the user image set, a plurality of product release value indexes are obtained, release decision analysis is carried out on the product to be released according to the plurality of product release value indexes through a product decision maker in the cross-border e-commerce platform, a product release scheme is obtained, and finally, the product release of the cross-border e-commerce platform is executed according to the product release scheme. The problem that the product delivery method in the prior art is poor in yield and instantaneity due to insufficient rigor and insufficient completeness is solved, and good showing of the product delivery effect can not be achieved.
Example 1
As shown in fig. 1, the application provides a product release optimizing method and system of a cross-border e-commerce platform, wherein the method comprises the following steps:
obtaining a product release time zone constraint characteristic, and screening a product library in a cross-border electronic commerce platform according to the product release time zone constraint characteristic to obtain a product library to be released;
when the product is put in, different types of products are put in according to different conditions. For example, when the product is put in winter, the put-in product should conform to the use scene in winter, such as gloves, down jackets, thermos cups, etc., and the products which do not appear in winter, such as short sleeves, ice makers, etc., therefore, the put-in of the product needs to be limited. The method comprises the steps of obtaining current product release characteristics according to current product release time points, analyzing the product release characteristics, obtaining product release time zone constraint characteristics opposite to the product release characteristics, and obtaining product release time zone constraint characteristics. Screening a product library in a cross-border e-commerce platform according to the constraint characteristics of the product release time zone, wherein the product library is a product set which needs to be popularized and released for product advertisements in the cross-border e-commerce platform, screening the product library to obtain a product library to be released, wherein the product library to be released represents products conforming to the constraint characteristics of the product release time zone, and is to be popularized on the popularization platform in subsequent processing, so that a data basis is provided for generating multiple types of products to be released by carrying out cluster analysis on the product library to be released for the subsequent multiple types of products to be released.
Generating multiple types of products to be put, wherein the multiple types of products to be put are obtained by carrying out cluster analysis on the product library to be put;
obtaining a product library to be released according to the constraint characteristics of the product release time zone, obtaining various products in the product library to be released, and not obtaining related types of products in time when releasing certain types of products, if the products in the product library to be released are subjected to relation construction, integrating and associating the products with the same characteristics, and when releasing the products with certain characteristics or functions, releasing a series of products according to the characteristics and association information. The clustering analysis is to classify target data on the basis of similarity, perform clustering analysis on a product library to be put in, obtain characteristics of various products in the product library to be put in, perform characteristic analysis on the products, obtain characteristic values of the products, represent the characteristic values of the products in a two-dimensional image, represent all the products in the product library to be put in the two-dimensional image, construct a coordinate system, divide a range according to the distance between each product, take a certain distance as a distance range threshold, divide two products within the distance range threshold into categories to obtain a group, divide the distance range of all the product points to obtain a plurality of product categories, and output the obtained product categories and products existing therein to obtain a product library clustering analysis result. The product library acquires a clustering analysis result, builds constraint for a preset matrix to be obtained later, integrates data of multiple types of products to be put in according to the preset matrix, and provides a basis for generating a matrix of the products to be put in.
Obtaining a preset matrix construction constraint, and carrying out data integration on the multiple types of products to be put in according to the preset matrix construction constraint to generate a matrix of the products to be put in;
when putting the products, not only the products required by the current user but also the products related to the products required by the target user can be put, the user can generate interest in the products related to the required products when browsing the required products, for example, when buying the down jackets, the user pushes winter thermal wear related to the down jackets, the purchase rate of the winter thermal wear by the user can be increased, the putting matrix of the products is built according to the relevance, and the matrix is required to be restrained, so that the matrix products are limited by matrix restraint. And arranging the released products according to the strength of the relevance to obtain a preset matrix constraint. According to the matrix constraint, product matrix construction is carried out, products are arranged according to the matrix constraint, the relevance from top to bottom is gradually weakened, multiple types of products to be put in are arranged in a combined mode according to the relevance between the products to be put in, a product matrix to be put in is obtained, the product matrix to be put in is obtained, product put-in value recognition is carried out on the product matrix to be put in according to a user image set, and a data basis is provided for obtaining multiple product put-in value indexes.
The user terminal of the cross-border e-commerce platform is interacted to obtain user information bases corresponding to a plurality of users, and a user portrait set is constructed according to the user information bases;
the user information base comprises user purchase records and browsing records, the user browsing records represent interesting contents of the user, the user can obtain the type of the released products of the user according to analysis of the interesting contents of the user, and a product matrix to be released of the user is constructed according to the type of the released products of the user. The purchase record is obtained record information of actual purchase of the browsed product by the user, the purchase record is analyzed on the basis of the browsed record to obtain the favorite product types, favorite product prices, activities and the like of the user, the product which is not purchased in the browsed record is analyzed to obtain the difference between the product which is not purchased and the purchased product, the difference analysis is carried out to obtain a difference analysis result, the reason of the user for selecting the purchased product can be obtained through the difference analysis result, the characteristics of the product purchased by the user are carried out according to the purchase reason, and the characteristics are optimized for delivering the product. The user portraits are user preference tag sets, product characteristics are obtained through characteristic analysis of a user information base of a user, user tags are generated according to the product characteristics, and the user tag sets are constructed according to the user tags to obtain the user portraits. The method comprises the steps of connecting user ends of a cross-border e-commerce platform, acquiring client information in the cross-border e-commerce platform to obtain user information bases corresponding to a plurality of users, constructing a user portrait set according to the user information bases, and providing a data base for carrying out product release value identification on a product matrix to be released according to the user portrait set in the follow-up process to obtain a plurality of product release value indexes.
Carrying out product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes;
when the product is put into the user, besides the product which the user possibly has an interest and the product required by the user, the consideration factor of the user on the product possibly has the value of the product, the product putting manufacturer can confirm whether the product needs to be put into the user according to the selection of the user, the product putting can bring higher value for the product, the put product has the value of preferential putting, the user on different consumption levels can select the product on different levels, two parameters are required to be defined for the reasons, the product value and the user value, the product value is the price of the product, the consumption level is the user value is the user consumption level, and the user tends to the consumption habit. The method comprises the steps of carrying out product release value identification on a release product matrix through a user portrait set, obtaining a value identification result, representing the value identification result by a unified standard, conveniently comparing product values to obtain a product release value index, and obtaining the product release value index, so that a data basis is provided for a subsequent release decision analysis of the product matrix to be released according to a plurality of product release value indexes through a product release decision maker in a cross-border electronic commerce platform.
Carrying out delivery decision analysis on the to-be-delivered product matrix according to the multiple product delivery value indexes by a product delivery decision maker in the cross-border electronic commerce platform to obtain a product delivery scheme;
analyzing a plurality of product input value indexes and a to-be-input product matrix, analyzing the to-be-input product matrix by constructing a decision model, analyzing the to-be-input product matrix by the decision model, remarkably improving input decision efficiency, constructing a decision model, obtaining a product input decision device, firstly constructing a framework according to a neural network model, inputting the first-step processing data in the to-be-input product matrix into a decision adjustment channel according to a corresponding relation, integrating the first-step processing data into a decision adjustment channel, obtaining a basic scheme of the to-be-input product matrix, integrating the first-step processing data into a second-step processing scheme, and outputting the second-step processing scheme of the to-be-input product matrix according to a corresponding relation, integrating the first-step processing data into a basic scheme of the to-be-input product, and outputting the basic scheme of the second-step processing scheme.
And executing the product release of the cross-border e-commerce platform according to the product release scheme.
According to the release scheme, the product release of the cross-border e-commerce platform is a release mode in theory, the factors considered by an actual user when receiving the product release are various, the market change is large, the market dynamics is required to be monitored and obtained in real time, for example, when marine pollution occurs in news, the sales volume of marine products can be seriously influenced, but the influence factors related to the marine product pollution are not in the release scheme, so that the release effect is far less than expected, the market dynamics is required to be monitored in real time, and the product release scheme of the product release of the cross-border e-commerce platform is adjusted and optimized in time. According to the optimized product release scheme, the product of the cross-border e-commerce platform is released, so that the product release of the cross-border e-commerce platform is more in line with the current market trend, and the sales rate is improved.
Further, the method further comprises:
obtaining a first product based on the product library;
carrying out product release time zone matching degree analysis on the first product based on the product release time zone constraint characteristics to obtain first product time zone matching degree;
Judging whether the first product time zone matching degree is smaller than a preset time zone matching degree or not;
if the first product time zone matching degree is greater than or equal to the preset time zone matching degree, marking the first product as a first product to be put, and adding the first product to be put into the product warehouse to be put.
When the product libraries are screened, the product libraries conforming to the time zone constraint characteristics of the product delivery are acquired according to the time zone constraint characteristics of the product delivery, the conformity degree of the product libraries conforming to the time zone constraint characteristics of the product delivery is screened, the product libraries with high conformity degree are output, the product libraries with low conformity degree are removed, and the product libraries in the cross-border electronic commerce platform are screened. The method comprises the steps of firstly obtaining a first product from a product library, then analyzing the product release time zone matching degree of the first product according to the constraint characteristics of the product release time zone to obtain a product release time zone matching degree result, outputting the product release time zone matching degree result to obtain a first product time zone matching degree, setting a preset time zone matching degree, judging the size of the first product time zone matching degree and the preset time zone matching degree, judging that the product meets release standards if the first product time zone matching degree is greater than or equal to the preset time zone matching degree, marking the first product as a first product to be released, adding the first product to be released into a product library to be released, obtaining the product library to be released, and subsequently generating multiple types of products to be released, wherein the multiple types of products to be released provide a data basis through cluster analysis of the product library to be released.
Further, the method further comprises:
collecting sales record data of a plurality of similar products of the first product to obtain sales records of the products in the classes;
based on the constraint characteristics of the product release time zone, respectively calibrating the simultaneous zone sales records of the product sales records in the multiple classes to obtain multiple simultaneous zone sales records;
based on the product sales records in the plurality of classes, respectively calculating sales volume duty ratios of the sales records in the same time zone to obtain a plurality of first sales volume duty ratio coefficients, and performing de-maximization on the plurality of first sales volume duty ratio coefficients to obtain a first sales volume duty ratio record;
and generating the first product time zone matching degree, wherein the first product time zone matching degree is obtained by carrying out average calculation on the first sales volume duty ratio record.
When the matching degree is analyzed, whether the matched product has sales potential or not needs to be considered, if the product only accords with the matching degree of the time zone when the product is put in, but no user can purchase the product, the putting of the product is not meaningful and causes resource waste, so that the selling property of the product needs to be evaluated and judged, and the product can be analyzed from the selling record. Collecting sales record data of a plurality of similar products of a first product, carrying out summarized analysis on sales conditions of the product and related products to obtain a plurality of in-class product sales records, respectively carrying out simultaneous zone sales record calibration on the plurality of in-class product sales records according to constraint characteristics of a product release time zone to obtain a plurality of calibration results, outputting the calibration results to obtain a plurality of simultaneous zone sales records, analyzing sales condition information, respectively carrying out sales ratio calculation on the plurality of simultaneous zone sales records according to the plurality of in-class product sales records, namely dividing sales of the product by total product sales to obtain a first sales ratio coefficient, obtaining a plurality of first item sales ratio coefficients, carrying out minimization on the plurality of first sales ratio coefficients, removing maximum and minimum values of the plurality of first sales ratio coefficients, leaving other first sales ratio coefficients, and obtaining the first sales ratio records. And finally, carrying out average value calculation on the first sales volume duty ratio record to obtain an average value calculation result, outputting the result to obtain a first product time zone matching degree, and obtaining the first product time zone matching degree to provide a data basis for subsequently judging whether the first product time zone matching degree is smaller than a preset time zone matching degree.
Further, the method further comprises:
obtaining a first preset construction constraint, wherein the first preset construction constraint is that a plurality of products to be put in the same class of products to be put are placed in the same column;
obtaining a second preset construction constraint, wherein the second preset construction constraint is a descending constraint that a plurality of products to be put in the same column meet the product time zone matching degree;
obtaining a third preset construction constraint, wherein the third preset construction constraint comprises automatic zero padding for the data missing column;
obtaining a fourth preset construction constraint, wherein the fourth preset construction constraint is a descending constraint that the same row meets the product type difference;
generating the preset matrix construction constraint according to the first preset construction constraint, the second preset construction constraint, the third preset construction constraint and the fourth preset construction constraint.
Constructing a preset matrix construction constraint, wherein the preset matrix construction constraint is formed by combining a plurality of first preset construction constraints, and the preset construction constraints are arranged in a layered manner to form the preset matrix construction constraint. Placing a plurality of products to be put in the same class of products to be put in the same column to obtain a column of products to be put in, and outputting the column of products to be put in as a first column parameter to obtain a first preset construction constraint; outputting a plurality of products to be put in meeting descending order constraint of the product time zone matching degree in the same column to obtain a descending order constraint product column, and outputting the descending order constraint product column to obtain a second preset construction constraint, wherein the descending order constraint is that the matching degree is continuously reduced, and the descending order constraint of the product time zone matching degree is that the product time zone matching degree of the plurality of products to be put in the same column is lower; similarly, outputting a plurality of products to be put in which the descending constraint of the product time zone matching degree is met in the same column to obtain a descending constraint product column, outputting the descending constraint product column, automatically zero-filling the data missing column, and outputting the obtained product column to obtain a third preset construction constraint; adding products different from the current product type into the product column, namely, taking the descending constraint of the product type difference satisfied by the same row as a new preset construction constraint to obtain a fourth preset construction constraint; carrying out layered integrated splicing on the first preset construction constraint, the second preset construction constraint, the third preset construction constraint and the fourth preset construction constraint to obtain an integrated splicing result, generating a preset matrix construction constraint, acquiring the preset matrix construction constraint, and providing a data basis for carrying out product release value identification on a to-be-released product matrix according to a user image set to obtain a plurality of product release value indexes.
Further, the method further comprises:
obtaining a first user based on the plurality of users;
based on the user information base, a first user basic information set and a first user product purchase record set corresponding to the first user are obtained;
constructing a first user basic portrait based on the first user basic information set;
acquiring according to a first user product purchase record, arranging the first user product purchase record into a first user product purchase record set, wherein the first user product purchase record set comprises all purchase product information of a user, the purchase product information comprises a plurality of different types of products, carrying out product type preference analysis on the first user product purchase record set, extracting characteristics of all products in the first user product purchase record set, carrying out characteristic analysis on the products, outputting characteristic analysis results to obtain a first product type preference analysis result, carrying out data fusion on a first user basic portrait according to the first product type preference analysis result, completing combination of two items of data, outputting the combination result to generate a first user portrait, and adding the first user portrait to a user portrait set. And randomly selecting one user from a plurality of users for output, and taking the user as a first user. Extracting information from a user information base of a first user to obtain a first user basic information set and a first user product purchase record set corresponding to the first user, carrying out data construction on the first user basic information set, carrying out feature extraction on the first user basic information set, carrying out cluster analysis on the features, obtaining a cluster analysis result, outputting the cluster analysis result as a label, carrying out user portrayal generation according to the label to obtain a first user basic image, carrying out product type preference analysis on the first user product purchase record set, carrying out feature extraction on the first product type, taking the feature as a feature label, adding the feature label into the corresponding basic image to be used as a first product type preference analysis result, carrying out data fusion on the first user basic portrayal according to the first product type preference analysis result to generate a first user portrayal, adding the first user portrayal to the user portrayal set, and constructing the user portrayal set to identify a product release value to a product matrix to be released according to the user portrayal set, so as to obtain a plurality of product release value indexes, and providing a data basis.
Further, the method further comprises:
obtaining an mth product to be put in based on the product matrix to be put in;
carrying out sales prediction on the mth product to be put in based on the user portrait set to obtain product prediction sales;
obtaining unit product profit of the mth product to be put in, and multiplying the unit product profit by the product forecast sales volume to obtain the mth product put in value;
obtaining the total value of the products to be put in the same column corresponding to the mth product to be put in;
generating an mth product release value index corresponding to the mth product to be released, and adding the mth product release value index to the multiple product release value indexes, wherein the mth product release value index is obtained by calculating the ratio of the mth product release value to the total value of the same-column products.
And carrying out product release value analysis on the product matrix to be released according to the user portrait set, obtaining a value analysis result, and calculating a product release value index according to the analysis result. Extracting products to be subjected to value analysis in a to-be-put product matrix to obtain corresponding to-be-put products, obtaining a user portrait set corresponding to the to-be-put products according to the to-be-put products, obtaining the similarity of the current products according to user preference conditions and purchase records in the user portrait set, and calculating sales of the products according to the similarity, wherein the sales are expressed as predicted conditions of the products, namely predicted sales of the products. Obtaining product information of the product according to a cross-border e-commerce platform, extracting profit of the product according to the product information to obtain unit product profit of the product to be released, multiplying the unit product profit by product forecast sales to obtain release value of the product to be released, carrying out full-platform search according to the cross-border e-commerce platform to obtain the release total value of the product in the same column corresponding to the product to be released, carrying out ratio calculation according to the release value of the product and the release total value of the product in the same column, namely dividing the release value of the product by the release total value of the product in the same column, and obtaining a calculation result, wherein the calculation result is a product release value index. The method comprises the steps of correspondingly acquiring a corresponding relation between a product to be put and a product putting value index, adding the product putting value index into a plurality of product putting value indexes, and acquiring the plurality of product putting value indexes, so that a data basis is provided for a subsequent product putting decision maker in a cross-border electronic commerce platform to perform putting decision analysis on a product matrix to be put according to the plurality of product putting value indexes, and obtaining a product putting scheme.
Further, the method further comprises:
obtaining a plurality of real-time sales of products;
calculating the in-class sales volume ratio of the real-time sales volumes of the products to generate a plurality of in-class sales volume ratio coefficients;
judging whether the in-class sales volume ratio coefficients meet preset sales volume ratio constraint or not;
if any one of the plurality of in-class sales volume duty ratio coefficients does not meet the preset sales volume duty ratio constraint, generating a product delivery optimization instruction,
and adjusting the product release scheme according to the product release optimization instruction to obtain an optimized product release scheme, and executing the product release of the cross-border e-commerce platform according to the optimized product release scheme.
After the product of the cross-border e-commerce platform is put according to the product putting scheme, the product needs to be adjusted in time according to the dynamic condition of the sales volume of the market, the sales volume of the market is sensitive to change, the fluctuation is large, and the sales volume needs to be monitored in real time. The method comprises the steps that a cross-border e-commerce platform monitors product sales in real time, a plurality of product real-time sales are obtained through obtaining product sales data in the cross-border e-commerce platform, in-class sales ratio calculation is conducted on the product real-time sales, and a plurality of in-class sales ratio coefficients are generated, wherein the in-class sales ratio refers to the ratio of the product to the product type; comparing the in-class sales volume ratio coefficients to meet the preset sales volume ratio constraint, and obtaining a comparison result, if the comparison result is that any in-class sales volume ratio coefficient in the in-class sales volume ratio coefficients is larger than or equal to the preset sales volume ratio constraint, judging that the sales volume of the current product meets or exceeds the expected value, and the product delivery effect is qualified; if the comparison result is that any in-class sales volume duty ratio coefficient of the plurality of in-class sales volume duty ratio coefficients is smaller than the preset sales volume duty ratio constraint, judging that the current sales volume of the product is lower than the expected sales volume, adjusting the current product to generate a product release optimizing instruction, adjusting a product release scheme according to the product release optimizing instruction, correspondingly changing the matching degree of the product, generating a scheme of adjusting content, obtaining an optimized product release scheme, and executing product release across the electronic commerce platform according to the optimized product release scheme. According to the optimized product release scheme, the product of the cross-border e-commerce platform is released, so that the product release of the cross-border e-commerce platform is more in line with the current market trend, and the sales volume is increased.
Example 2
Based on the same inventive concept as the product release optimizing method of the cross-border e-commerce platform in the foregoing embodiment, as shown in fig. 2, the present application provides a product release optimizing system of the cross-border e-commerce platform, where the system includes:
constraint feature acquisition module 11: the constraint characteristic acquisition module 11 is used for acquiring constraint characteristics of a product release time zone, and screening product libraries in a cross-border electronic commerce platform according to the constraint characteristics of the product release time zone to acquire a product library to be released;
product generation module 12: the product generation module 12 is configured to generate multiple types of products to be put, where the multiple types of products to be put are obtained by performing cluster analysis on the product library to be put;
the data integration module 13: the data integration module 13 is configured to obtain a preset matrix construction constraint, and perform data integration on the multiple types of products to be put according to the preset matrix construction constraint to generate a matrix of products to be put;
information base acquisition module 14: the information base acquisition module 14 is used for interacting the user end of the cross-border e-commerce platform to acquire user information bases corresponding to a plurality of users, and constructing a user portrait set according to the user information bases;
Value recognition module 15: the value recognition module 15 is configured to perform product release value recognition on the to-be-released product matrix according to the user portrait set, so as to obtain a plurality of product release value indexes;
the delivery decision maker module 16: the delivery decision maker module 16 is configured to perform delivery decision analysis on the to-be-delivered product matrix according to the multiple product delivery value indexes through a product delivery decision maker in the cross-border e-commerce platform, so as to obtain a product delivery scheme;
product release module 17: the product release module 17 is configured to execute product release of the cross-border e-commerce platform according to the product release scheme.
Further, the constraint characteristic acquisition module 11 includes the following execution steps:
obtaining a first product based on the product library;
carrying out product release time zone matching degree analysis on the first product based on the product release time zone constraint characteristics to obtain first product time zone matching degree;
judging whether the first product time zone matching degree is smaller than a preset time zone matching degree or not;
if the first product time zone matching degree is greater than or equal to the preset time zone matching degree, marking the first product as a first product to be put, and adding the first product to be put into the product warehouse to be put.
Further, the constraint characteristic acquisition module 11 includes the following execution steps:
collecting sales record data of a plurality of similar products of the first product to obtain sales records of the products in the classes;
based on the constraint characteristics of the product release time zone, respectively calibrating the simultaneous zone sales records of the product sales records in the multiple classes to obtain multiple simultaneous zone sales records;
based on the product sales records in the plurality of classes, respectively calculating sales volume duty ratios of the sales records in the same time zone to obtain a plurality of first sales volume duty ratio coefficients, and performing de-maximization on the plurality of first sales volume duty ratio coefficients to obtain a first sales volume duty ratio record;
and generating the first product time zone matching degree, wherein the first product time zone matching degree is obtained by carrying out average calculation on the first sales volume duty ratio record.
Further, the data integration module 13 includes the following steps:
obtaining a first preset construction constraint, wherein the first preset construction constraint is that a plurality of products to be put in the same class of products to be put are placed in the same column;
obtaining a second preset construction constraint, wherein the second preset construction constraint is a descending constraint that a plurality of products to be put in the same column meet the product time zone matching degree;
Obtaining a third preset construction constraint, wherein the third preset construction constraint comprises automatic zero padding for the data missing column;
obtaining a fourth preset construction constraint, wherein the fourth preset construction constraint is a descending constraint that the same row meets the product type difference;
generating the preset matrix construction constraint according to the first preset construction constraint, the second preset construction constraint, the third preset construction constraint and the fourth preset construction constraint.
Further, the information base obtaining module 14 includes the following steps:
based on the user information base, a first user basic information set and a first user product purchase record set corresponding to the first user are obtained;
constructing a first user basic portrait based on the first user basic information set;
carrying out product type preference analysis based on the first user product purchase record set to obtain a first product type preference analysis result;
and carrying out data fusion on the first user basic portrait according to the first product type preference analysis result to generate a first user portrait, and adding the first user portrait to the user portrait set.
Further, the value recognition module 15 includes the following steps:
Obtaining an mth product to be put in based on the product matrix to be put in;
carrying out sales prediction on the mth product to be put in based on the user portrait set to obtain product prediction sales;
obtaining unit product profit of the mth product to be put in, and multiplying the unit product profit by the product forecast sales volume to obtain the mth product put in value;
obtaining the total value of the products to be put in the same column corresponding to the mth product to be put in;
generating an mth product release value index corresponding to the mth product to be released, and adding the mth product release value index to the multiple product release value indexes, wherein the mth product release value index is obtained by calculating the ratio of the mth product release value to the total value of the same-column products.
Further, the product release module 17 comprises the following execution steps:
obtaining a plurality of real-time sales of products;
calculating the in-class sales volume ratio of the real-time sales volumes of the products to generate a plurality of in-class sales volume ratio coefficients;
judging whether the in-class sales volume ratio coefficients meet preset sales volume ratio constraint or not;
if any one of the plurality of in-class sales volume duty ratio coefficients does not meet the preset sales volume duty ratio constraint, generating a product delivery optimization instruction,
And adjusting the product release scheme according to the product release optimization instruction to obtain an optimized product release scheme, and executing the product release of the cross-border e-commerce platform according to the optimized product release scheme.
Through the foregoing detailed description of the product release optimizing method of the cross-border e-commerce platform, those skilled in the art can clearly know the product release optimizing method of the cross-border e-commerce platform in the embodiment, and for the device disclosed in the embodiment, the description is relatively simple because the device corresponds to the method disclosed in the embodiment, and relevant places refer to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The product release optimizing method of the cross-border e-commerce platform is characterized by comprising the following steps of:
obtaining a product release time zone constraint characteristic, and screening a product library in a cross-border electronic commerce platform according to the product release time zone constraint characteristic to obtain a product library to be released;
generating multiple types of products to be put, wherein the multiple types of products to be put are obtained by carrying out cluster analysis on the product library to be put;
obtaining a preset matrix construction constraint, and carrying out data integration on the multiple types of products to be put in according to the preset matrix construction constraint to generate a matrix of the products to be put in;
the user terminal of the cross-border e-commerce platform is interacted to obtain user information bases corresponding to a plurality of users, and a user portrait set is constructed according to the user information bases;
carrying out product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes;
carrying out delivery decision analysis on the to-be-delivered product matrix according to the multiple product delivery value indexes by a product delivery decision maker in the cross-border electronic commerce platform to obtain a product delivery scheme;
and executing the product release of the cross-border e-commerce platform according to the product release scheme.
2. The method of claim 1, wherein screening the product library in the cross-border e-commerce platform according to the product release time zone constraint feature to obtain a product library to be released comprises:
obtaining a first product based on the product library;
carrying out product release time zone matching degree analysis on the first product based on the product release time zone constraint characteristics to obtain first product time zone matching degree;
judging whether the first product time zone matching degree is smaller than a preset time zone matching degree or not;
if the first product time zone matching degree is greater than or equal to the preset time zone matching degree, marking the first product as a first product to be put, and adding the first product to be put into the product warehouse to be put.
3. The method of claim 2, wherein performing product placement time zone matching analysis on the first product based on the product placement time zone constraint features to obtain a first product time zone matching comprises:
collecting sales record data of a plurality of similar products of the first product to obtain sales records of the products in the classes;
based on the constraint characteristics of the product release time zone, respectively calibrating the simultaneous zone sales records of the product sales records in the multiple classes to obtain multiple simultaneous zone sales records;
Based on the product sales records in the plurality of classes, respectively calculating sales volume duty ratios of the sales records in the same time zone to obtain a plurality of first sales volume duty ratio coefficients, and performing de-maximization on the plurality of first sales volume duty ratio coefficients to obtain a first sales volume duty ratio record;
and generating the first product time zone matching degree, wherein the first product time zone matching degree is obtained by carrying out average calculation on the first sales volume duty ratio record.
4. The method of claim 1, wherein obtaining a preset matrix build constraint comprises:
obtaining a first preset construction constraint, wherein the first preset construction constraint is that a plurality of products to be put in the same class of products to be put are placed in the same column;
obtaining a second preset construction constraint, wherein the second preset construction constraint is a descending constraint that a plurality of products to be put in the same column meet the product time zone matching degree;
obtaining a third preset construction constraint, wherein the third preset construction constraint comprises automatic zero padding for the data missing column;
obtaining a fourth preset construction constraint, wherein the fourth preset construction constraint is a descending constraint that the same row meets the product type difference;
generating the preset matrix construction constraint according to the first preset construction constraint, the second preset construction constraint, the third preset construction constraint and the fourth preset construction constraint.
5. The method of claim 1, wherein constructing a user representation set from the user information base comprises:
obtaining a first user based on the plurality of users;
based on the user information base, a first user basic information set and a first user product purchase record set corresponding to the first user are obtained;
constructing a first user basic portrait based on the first user basic information set;
carrying out product type preference analysis based on the first user product purchase record set to obtain a first product type preference analysis result;
and carrying out data fusion on the first user basic portrait according to the first product type preference analysis result to generate a first user portrait, and adding the first user portrait to the user portrait set.
6. The method of claim 1, wherein performing product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes, comprising:
obtaining an mth product to be put in based on the product matrix to be put in;
carrying out sales prediction on the mth product to be put in based on the user portrait set to obtain product prediction sales;
Obtaining unit product profit of the mth product to be put in, and multiplying the unit product profit by the product forecast sales volume to obtain the mth product put in value;
obtaining the total value of the products to be put in the same column corresponding to the mth product to be put in;
generating an mth product release value index corresponding to the mth product to be released, and adding the mth product release value index to the multiple product release value indexes, wherein the mth product release value index is obtained by calculating the ratio of the mth product release value to the total value of the same-column products.
7. The method of claim 1, wherein after executing the product release of the cross-border e-commerce platform according to the product release scheme, further comprising:
obtaining a plurality of real-time sales of products;
calculating the in-class sales volume ratio of the real-time sales volumes of the products to generate a plurality of in-class sales volume ratio coefficients;
judging whether the in-class sales volume ratio coefficients meet preset sales volume ratio constraint or not;
if any one of the plurality of in-class sales volume duty ratio coefficients does not meet the preset sales volume duty ratio constraint, generating a product delivery optimization instruction,
And adjusting the product release scheme according to the product release optimization instruction to obtain an optimized product release scheme, and executing the product release of the cross-border e-commerce platform according to the optimized product release scheme.
8. A product release optimization system across e-commerce platforms, the system comprising:
constraint characteristic acquisition module: obtaining a product release time zone constraint characteristic, and screening a product library in a cross-border electronic commerce platform according to the product release time zone constraint characteristic to obtain a product library to be released;
the product generation module: generating multiple types of products to be put, wherein the multiple types of products to be put are obtained by carrying out cluster analysis on the product library to be put;
and a data integration module: obtaining a preset matrix construction constraint, and carrying out data integration on the multiple types of products to be put in according to the preset matrix construction constraint to generate a matrix of the products to be put in;
the information base acquisition module: the user terminal of the cross-border e-commerce platform is interacted to obtain user information bases corresponding to a plurality of users, and a user portrait set is constructed according to the user information bases;
a value identification module: carrying out product release value identification on the to-be-released product matrix according to the user portrait set to obtain a plurality of product release value indexes;
A delivery decision maker module: carrying out delivery decision analysis on the to-be-delivered product matrix according to the multiple product delivery value indexes by a product delivery decision maker in the cross-border electronic commerce platform to obtain a product delivery scheme;
and a product release module: and executing the product release of the cross-border e-commerce platform according to the product release scheme.
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