Product operation management methods and systems applied to cross-border e-commerce

Abstract

This invention relates to the field of cross-border e-commerce product operation technology, and provides a method and system for product operation management in cross-border e-commerce. The method includes: dividing product operation data into partitioned datasets; optimizing the partitioned datasets to obtain optimized datasets; performing multi-source analysis on the product operation data to construct an operation network for the operated products; querying potential users of the operated products; extracting target potential users from the potential users; constructing a screening matrix for the target potential users; screening the target potential users to obtain screened users; updating the screened users to obtain updated users; constructing an initial operation strategy for the operated products; conducting a supply risk assessment on the target operating users to obtain the assessment results; and constructing a cross-border e-commerce product operation management strategy. This invention can improve the efficiency of product operation management.

Description

Technical Field

[0001] This invention relates to the field of cross-border e-commerce product operation technology, and in particular to a product operation management method and system applied to cross-border e-commerce. Background Technology

[0002] In the cross-border e-commerce sector, merchandise operations management is a key element in improving business performance and customer satisfaction. Effective merchandise operations management methods ensure precise product promotion, reasonable inventory control, and accurate fulfillment of customer needs, thereby enhancing the competitiveness of cross-border e-commerce companies. Establishing a merchandise operations management system suitable for cross-border e-commerce helps companies accurately identify market demands, optimize product selection strategies, rationally plan inventory, and effectively track merchandise operations, thus improving operational efficiency and profitability. Simultaneously, companies can use data analysis to predict market trends and consumer preferences, adjust merchandise strategies in a timely manner to provide products and services that better meet market demands, and promote information flow and collaboration between different operating channels, different national markets, and various departments within the company, thereby reducing operational risks and improving overall operational efficiency.

[0003] Currently, the general approach to product operation management in cross-border e-commerce involves building a preliminary product operation framework using traditional data analysis tools and human experience to achieve basic product operation management. However, this method relies heavily on human judgment and limited data for many decisions. Given the massive amounts of product data and complex market environment involved in cross-border e-commerce, companies often lack precision in their product operation decisions when faced with large amounts of data and a volatile market, resulting in inefficient and unscientific product operation management. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a product operation management method applicable to cross-border e-commerce, which can improve the scientific nature of product operation management.

[0005] In a first aspect, the present invention provides a product operation management method applied to cross-border e-commerce, the method comprising:

[0006] Collect product operation data related to cross-border e-commerce, query the key operation attributes of the product operation data, divide the product operation data based on the key operation attributes to obtain a partitioned dataset, calculate the data gain in the partitioned dataset, and optimize the partitioned dataset based on the data gain to obtain an optimized dataset;

[0007] Multi-source analysis is performed on the product operation data to select data features of the optimized dataset based on the analysis results of the multi-source analysis, query the operational products in the cross-border e-commerce, calculate the mutual information value between the data features and the operational products, and construct the operation network of the operational products based on the mutual information value;

[0008] Obtain the operational channels of the operational product, query the potential users of the operational product based on the operational channels, query the sticky users of the operational product, analyze the user similarity between the potential users and the sticky users, extract target potential users from the potential users based on the user similarity, construct a screening matrix for the target potential users, and use the screening matrix to screen the target potential users to obtain screened users;

[0009] The selected users are updated using the operating network to obtain updated users. After the updated users are used as the target users for the operating products, an initial operating strategy for the operating products is constructed. A supply risk assessment is performed on the target users to obtain the assessment results. Based on the initial operating strategy and the assessment results, the product operation management strategy for the cross-border e-commerce is constructed.

[0010] Furthermore, based on the key operational attributes, the product operation data is segmented to obtain a segmented dataset, including:

[0011] The key operational attributes are transformed into vectors, and the centroids of the transformed vectors are constructed.

[0012] The product operation data is then vectorized to obtain the operation vector;

[0013] The vector distance between the operational vector and the centroid of the vector is calculated using the following formula:

[0014]

[0015] in, Represents vector distance. The spatial dimension of the operational vector. This represents the x-coordinate of the centroid of the i-th vector. The ordinate represents the centroid of the i-th vector;

[0016] Based on the vector distance, the product operation data is divided into data segments to obtain a segmented dataset.

[0017] Further, calculating the data gain in the partitioned dataset includes:

[0018] The information entropy in the partitioned dataset is calculated using the following formula:

[0019]

[0020] in, Let n represent the information entropy, and n represent the number of categories that divide the dataset. This represents the proportion of the j-th class of datasets;

[0021] Calculate the conditional entropy of the corresponding key attributes in the partitioned dataset. Based on the information entropy and the conditional entropy, calculate the data gain in the partitioned dataset using the following formula:

[0022]

[0023] in, Indicates data gain. Represents information entropy. This represents conditional entropy.

[0024] Furthermore, the step of performing multi-source analysis on the product operation data to select data features of the optimized dataset based on the analysis results of the multi-source analysis includes:

[0025] The product operation data is integrated to obtain integrated data;

[0026] Determine the analytical metrics for the integrated data;

[0027] Based on the aforementioned analytical indicators, a correlation analysis is performed on the integrated data to obtain the correlation analysis results.

[0028] Based on the aforementioned analytical indicators, trend analysis is performed on the integrated data to obtain trend analysis results;

[0029] Based on the aforementioned analytical indicators, the integrated data is compared and analyzed to obtain the comparative analysis results.

[0030] Based on the correlation analysis results, the trend analysis results, and the comparative analysis results, the data characteristics of the optimized dataset are determined.

[0031] Further, the step of querying the operational products in the cross-border e-commerce platform and calculating the mutual information value between the data features and the operational products includes:

[0032] The mutual information value between the data features and the operational goods is calculated using the following formula:

[0033]

[0034] in, Let 'a' represent a feature in data feature A, and 'b' represent a product in operational product B. Let a and b represent the joint probability distribution. Describes the marginal probability distribution of a. Describes the marginal probability distribution of b. This indicates the specific value b for the product B being operated. The specific value 'a' represents the data feature A.

[0035] Furthermore, the analysis of user similarity between the potential users and the sticky users includes:

[0036] The potential users and the sticky users are transformed into vectors to obtain a first vector and a second vector;

[0037] The vector similarity between the first vector and the second vector is calculated using the following formula:

[0038]

[0039] in, Represents vector similarity, Denotes the first vector. Represents the second vector. Denotes the magnitude of the first vector. Denotes the magnitude of the second vector. This represents the inclination of the spatial coordinate point i corresponding to the first vector. This represents the inclination of the spatial coordinate point i corresponding to the second vector;

[0040] Based on the vector similarity, the user similarity between the potential user and the sticky user is determined.

[0041] Furthermore, constructing the screening matrix for the target potential users includes:

[0042] Determine the screening dimensions for the target potential users and calculate the dimension weights for the screening dimensions;

[0043] Construct a matrix framework for the target potential users;

[0044] Obtain user data of the target potential users;

[0045] Based on the dimensional weights and the user data, the matrix framework is populated with data to obtain a filtering matrix.

[0046] Furthermore, the step of updating the screened users using the operating network to obtain updated users includes:

[0047] The interest set of the screened users is constructed using the operating network;

[0048] Calculate the degree of interest preference of the selected users for the interest set based on the interest set;

[0049] Based on the degree of interest preference, the screened users are updated to obtain updated users.

[0050] Furthermore, the step of constructing the interest set of the screened users using the operating network includes:

[0051] Query the user data of the filtered users;

[0052] Extract the data features from the user data;

[0053] The data features are clustered to obtain cluster features;

[0054] Construct interest tags for the clustering features;

[0055] The interest tags are weighted to obtain weighted tags;

[0056] After optimizing the weighted assignment labels, the interest set of the selected users is obtained.

[0057] Secondly, the present invention provides a product operation and management system applied to cross-border e-commerce, the system comprising:

[0058] The dataset optimization module is used to collect product operation data related to cross-border e-commerce, query the key operation attributes of the product operation data, divide the product operation data based on the key operation attributes to obtain a divided dataset, calculate the data gain in the divided dataset, and optimize the divided dataset based on the data gain to obtain an optimized dataset.

[0059] An operation network construction module is used to perform multi-source analysis on the product operation data, select data features of the optimized dataset based on the analysis results of the multi-source analysis, query the operational products in the cross-border e-commerce, calculate the mutual information value between the data features and the operational products, and construct the operation network of the operational products based on the mutual information value.

[0060] The user filtering module is used to obtain the operation channels of the operational products, query the potential users of the operational products based on the operation channels, query the sticky users of the operational products, analyze the user similarity between the potential users and the sticky users, extract target potential users from the potential users based on the user similarity, construct the filtering matrix of the target potential users, and use the filtering matrix to filter the target potential users to obtain filtered users.

[0061] The operation strategy construction module is used to update the screened users using the operation network to obtain updated users, and after using the updated users as the target operation users of the operation products, construct the initial operation strategy of the operation products, conduct supply risk assessment on the target operation users to obtain assessment results, and construct the cross-border e-commerce product operation management strategy based on the initial operation strategy and the assessment results.

[0062] Compared with existing technologies, the technical principles and beneficial effects of this solution are as follows:

[0063] This invention, through collecting product operation data related to cross-border e-commerce, including sales data, user search and browsing data, can gain insights into consumer needs and preferences, thus providing a basis for product selection and promotion. Furthermore, by performing multi-source analysis on the product operation data and selecting data features of the optimized dataset based on the analysis results, this invention can comprehensively understand the operational status and formulate more targeted and effective operational strategies, such as promotional activities or product improvements for specific regions. The invention also includes methods for querying operational products in cross-border e-commerce and calculating the mutual information value between data features and operational products to reveal the degree of correlation between data features and operational products. Furthermore, by acquiring the operational channels of the operational products and querying potential users based on these channels, new customer groups can be discovered, expanding the market coverage of the products, increasing sales opportunities, and developing more targeted and personalized marketing strategies based on the characteristics and needs of potential users, thereby improving marketing effectiveness and conversion rates. In addition, this embodiment of the invention utilizes the operational network to update the screened users, allowing the updated users to adapt to market changes. With changes in market dynamics and user needs, timely updates to the screened users can ensure the capture of the latest potential valuable users. Based on the initial operational strategy and the evaluation results, constructing a product operation management strategy for cross-border e-commerce can improve operational efficiency and effectiveness. By comprehensively considering factors such as the initial strategy and supply risks, operational activities can be planned more comprehensively and accurately, avoiding blindness and one-sidedness, thereby improving operational efficiency and final effectiveness and reducing risks. Adjusting and optimizing the operational strategy based on the supply risk assessment results helps to proactively address potential supply problems and reduce business risks caused by supply interruptions and delays. The present invention provides a product operation management method and system applicable to cross-border e-commerce, which can improve the scientific nature of product operation management. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating a product operation management method applied to cross-border e-commerce, as provided in an embodiment of the present invention.

[0067] Figure 2 This is a schematic diagram of a module of a product operation management system applied to cross-border e-commerce, provided as an embodiment of the present invention. Detailed Implementation

[0068] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0069] This invention provides a product operation management method for cross-border e-commerce. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this invention: a server, a terminal, etc. In other words, the product operation management method for cross-border e-commerce can be executed by software or hardware installed on a terminal device or a server device. The software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0070] See Figure 1 The diagram shown is a flowchart illustrating a product operation management method applied to cross-border e-commerce, according to an embodiment of the present invention. Figure 1 The product operation management method described in the article, applied to cross-border e-commerce, includes:

[0071] S1. Collect product operation data related to cross-border e-commerce, query the key operation attributes of the product operation data, divide the product operation data based on the key operation attributes to obtain a divided dataset, calculate the data gain in the divided dataset, and optimize the divided dataset based on the data gain to obtain an optimized dataset.

[0072] The embodiments of the present invention can collect product operation data related to cross-border e-commerce by collecting product sales data, user search and browsing data, etc., to gain insights into consumer needs and preferences, thereby providing a basis for product selection and promotion. The product operation data can be obtained through e-commerce platforms, such as Amazon or Google Analytics.

[0073] Furthermore, this embodiment of the invention allows for targeted decision-making based on key operational attributes obtained by querying the product operation data, thereby improving the accuracy and efficiency of decision-making. These key operational attributes refer to characteristics or indicators that have a critical and significant impact on the effectiveness, efficiency, and profitability of product operations, such as target market, product category, price range, brand awareness, and product quality.

[0074] Furthermore, in this embodiment of the invention, by dividing the product operation data based on the key operational attributes, a divided dataset can be obtained, which can classify a large amount of disordered data, facilitating data analysis.

[0075] As an embodiment of the present invention, the step of dividing the product operation data based on the operational focus attribute to obtain a partitioned dataset includes: performing vector transformation on the operational focus attribute and constructing the vector centroid of the transformed vector; performing vector transformation on the product operation data to obtain an operation vector; and calculating the vector distance between the operation vector and the vector centroid using the following formula:

[0076]

[0077] in, Represents vector distance. The spatial dimension of the operational vector. This represents the x-coordinate of the centroid of the i-th vector. The ordinate represents the centroid of the i-th vector;

[0078] Based on the vector distance, the product operation data is divided into data segments to obtain a segmented dataset.

[0079] It should be noted that the vector transformations can all be performed using a pre-configured vector transformation matrix. The vector centroid of the transformed vector can be constructed by using a small sample set of initial points, i.e., the initial vector centroid, based on the key operational attributes, such as small samples of the target market, small samples of product categories, and small samples of price ranges.

[0080] The present invention, through the calculation of data gain in the partitioned dataset, can help users determine which attributes or features are more valuable for distinguishing different data categories, thereby selecting more representative and influential features in subsequent analysis.

[0081] As an embodiment of the present invention, calculating the data gain in the partitioned dataset includes: calculating the information entropy in the partitioned dataset using the following formula:

[0082]

[0083] in, Let n represent the information entropy, and n represent the number of categories that divide the dataset. This represents the proportion of the j-th class of datasets;

[0084] Calculate the conditional entropy of the corresponding key attributes in the partitioned dataset. Based on the information entropy and the conditional entropy, calculate the data gain in the partitioned dataset using the following formula:

[0085]

[0086] in, Indicates data gain. Represents information entropy. This represents conditional entropy.

[0087] Furthermore, in this embodiment of the invention, by optimizing the partitioned dataset based on the data gain, the optimized dataset can reduce the time and computational resource consumption for processing and analyzing data, thereby improving work efficiency.

[0088] Optionally, based on the data gain, the partitioned dataset is optimized to obtain an optimized dataset. Data optimization can be performed using the data gain calculation results. For example, we have various attribute data for a product, such as price, color, size, brand, origin, and material. By calculating the data gain, we find that price and brand attributes contribute significantly to predicting product sales, while color and size contribute relatively less. Therefore, when optimizing the dataset, we can retain only the data for price and brand attributes and remove the data for color and size to reduce data volume and improve analysis efficiency.

[0089] S2. Perform multi-source analysis on the product operation data, select data features of the optimized dataset based on the analysis results of the multi-source analysis, query the operating products in the cross-border e-commerce, calculate the mutual information value between the data features and the operating products, and construct the operation network of the operating products based on the mutual information value.

[0090] This invention provides a comprehensive understanding of operational status by performing multi-source analysis on the product operation data and selecting data features of the optimized dataset based on the analysis results. This allows for the development of more targeted and effective operational strategies, such as promotional activities or product improvements for specific regions.

[0091] As an embodiment of the present invention, the step of performing multi-source analysis on the commodity operation data to select data features of the optimized dataset based on the analysis results of the multi-source analysis includes: integrating the commodity operation data to obtain integrated data; determining the analysis indicators of the integrated data; performing correlation analysis on the integrated data based on the analysis indicators to obtain correlation analysis results; performing trend analysis on the integrated data based on the analysis indicators to obtain trend analysis results; performing comparative analysis on the integrated data based on the analysis indicators to obtain comparative analysis results; and determining the data features of the optimized dataset based on the correlation analysis results, the trend analysis results, and the comparative analysis results.

[0092] Optionally, the data integration of the product operation data to obtain integrated data refers to data operations that ensure data consistency and accuracy, which can be achieved through data cleaning. The determination of analytical indicators can be based on operational goals, identifying key analytical indicators such as sales revenue, profit margin, customer satisfaction, and market share, specifically determined according to actual analytical needs. The correlation analysis of the integrated data based on the analytical indicators to obtain correlation analysis results refers to performing correlation analysis between data from different data sources and the determined analytical indicators. For example, analyzing the correlation between product mention frequency on social media and sales revenue, or the relationship between logistics delivery time and customer satisfaction, can be achieved through regression analysis. Functional analysis, specifically, involves performing trend analysis on the integrated data based on the analytical indicators to obtain trend analysis results. This involves analyzing the changing trends of various indicators over time to understand the dynamic development of product operations. This analysis can be performed using time series functions. Comparative analysis, based on the analytical indicators, involves comparing data from different regions, product lines, and time periods to identify differences and advantages. This comparative analysis can be performed by constructing data charts. Finally, based on the correlation analysis results, trend analysis results, and comparative analysis results, the data characteristics of the optimized dataset are determined. Data characteristics closely related to operational goals can be selected based on the above analysis results, such as…

[0093] In cross-border e-commerce, data from sales platforms (including sales volume, price, return rate, etc.), user reviews on social media (evaluations of product quality, packaging, service, etc.), and delivery time and cost data from the logistics system are integrated. Correlation analysis reveals a strong positive correlation between product return rates and the number of negative reviews about product quality on social media. Trend analysis shows a significant upward trend in sales of a particular product line during specific seasons. Comparative analysis reveals a high demand for specific colors and styles of goods in certain regions.

[0094] Furthermore, this embodiment of the invention reveals the degree of correlation between data features and operational products by querying the operational products in the cross-border e-commerce platform and calculating the mutual information value between the data features and the operational products. A strong mutual information value indicates a close relationship between the features and the products, while a weaker mutual information value indicates a weaker relationship, thereby optimizing operational strategies.

[0095] As an embodiment of the present invention, querying the operational products in the cross-border e-commerce and calculating the mutual information value between the data features and the operational products includes: calculating the mutual information value between the data features and the operational products using the following formula:

[0096]

[0097] in, Let 'a' represent a feature in data feature A, and 'b' represent a product in operational product B. Let a and b represent the joint probability distribution. Describes the marginal probability distribution of a. Describes the marginal probability distribution of b. This indicates the specific value b for the product B being operated. The specific value 'a' represents the data feature A.

[0098] Furthermore, this embodiment of the invention, by constructing an operational network for the operated goods based on the mutual information values, can intuitively display the complex relationship between data features and the operated goods, facilitating quick understanding and insight for users. For example, for a certain clothing product in cross-border e-commerce, data features such as color, size, price, and fabric, along with the clothing product itself, are used as nodes. After calculating the mutual information values ​​between these features and the product, weights are assigned to the connections, and a network is drawn. If the mutual information value between color and the product is large, and the connection weight is high, it indicates that color has a significant impact on the operation of the clothing product. By analyzing the network, targeted promotion or inventory optimization can be implemented based on color.

[0099] S3. Obtain the operation channels of the operation product, query the potential users of the operation product based on the operation channels, query the sticky users of the operation product, analyze the user similarity between the potential users and the sticky users, extract target potential users from the potential users based on the user similarity, construct the screening matrix of the target potential users, and use the screening matrix to screen the target potential users to obtain screened users.

[0100] This invention, through obtaining the operational channels of the products being operated, allows for the discovery of new customer groups by querying potential users of the products based on these channels. This expands the market coverage of the products, increases sales opportunities, and enables the development of more targeted and personalized marketing strategies tailored to the characteristics and needs of potential users, thereby improving marketing effectiveness and conversion rates. The operational channels can be obtained through market research or data analysis, and the potential users can be acquired through platform user data mining.

[0101] It should be noted that the term "sticky users" usually refers to users who have a high dependence on the products offered and who frequently purchase and use them. This can be obtained through membership system data, user behavior tracking, and data analysis tools.

[0102] Furthermore, by analyzing the user similarity between potential users and active users, this embodiment of the invention can understand the similarities between potential users and active users, thereby enabling cross-border e-commerce merchants to develop more targeted marketing campaigns and improve the success rate of converting potential users into active users.

[0103] As an embodiment of the present invention, the analysis of user similarity between the potential user and the sticky user includes: performing vector transformation on the potential user and the sticky user to obtain a first vector and a second vector, and calculating the vector similarity between the first vector and the second vector using the following formula:

[0104]

[0105] in, Represents vector similarity, Denotes the first vector. Represents the second vector. Denotes the magnitude of the first vector. Denotes the magnitude of the second vector. This represents the inclination of the spatial coordinate point i corresponding to the first vector. This represents the inclination of the spatial coordinate point i corresponding to the second vector;

[0106] Based on the vector similarity, the user similarity between the potential user and the sticky user is determined.

[0107] It should be explained that if the calculated result of the vector similarity is not less than the preset similarity value, it means that the potential user and the sticky user are highly similar users. The preset similarity value is 0.5, but it can also be set according to the actual application scenario.

[0108] This invention allows for more accurate prediction of target potential users' needs by extracting target potential users from the potential users, enabling better product preparation and inventory management in advance.

[0109] Furthermore, by constructing the screening matrix for the target potential users, the embodiments of the present invention can quickly and systematically screen out target potential users who meet specific criteria from a large number of potential users, thereby improving the efficiency and accuracy of the screening.

[0110] As an embodiment of the present invention, constructing the screening matrix of the target potential users includes: determining the screening dimensions of the target potential users and calculating the dimension weights of the screening dimensions, constructing the matrix framework of the target potential users, obtaining the user data of the target potential users, and filling the matrix framework with data based on the dimension weights and the user data to obtain the screening matrix.

[0111] Optionally, the selection dimensions for the target potential users can be determined using market research techniques (such as questionnaires and user interviews) and data analysis tools. The weights of the selection dimensions can be assigned using the Analytic Hierarchy Process (AHP) or expert evaluation scoring. The matrix framework can be built using data structures in programming (such as two-dimensional arrays). The process of filling the matrix framework with data based on the dimension weights and the user data to obtain the selection matrix is ​​as follows:

[0112] Based on the dimensional weights, the scores of each potential user on each dimension are calculated and aggregated to obtain a comprehensive score. A threshold for the comprehensive score is determined, and potential users with scores higher than the threshold are assigned to the same dimension.

[0113] Furthermore, this embodiment of the invention utilizes the aforementioned screening matrix to screen potential users. The screened users can be determined by comprehensively considering multiple factors such as age, spending power, purchase history, and browsing behavior, thereby accurately identifying the potential user group most likely to convert into actual customers. This provides strong support for subsequent precision marketing and personalized services. For example, to construct a screening matrix for potential users on an e-commerce platform, dimensions include age (weight 0.2), spending amount in the past three months (weight 0.3), number of times browsing specific product pages (weight 0.2), and number of times adding items to the cart but not purchasing (weight 0.3). The criteria are set as follows: age between 20 and 40 years old, spending amount greater than 500 yuan in the past three months, browsing specific product pages more than 10 times, and adding items to the cart but not purchasing more than 3 times. Then, potential user data is collected to populate the matrix, and a comprehensive score is calculated. A threshold of 70 points is set; those scoring above 70 points are considered potential users.

[0114] S4. Use the operating network to update the screened users to obtain updated users. After using the updated users as the target operating users of the operating products, construct the initial operating strategy for the operating products. Conduct a supply risk assessment on the target operating users to obtain the assessment results. Based on the initial operating strategy and the assessment results, construct the product operation management strategy for the cross-border e-commerce.

[0115] This invention, through the use of the operational network to update the screened users, allows the updated users to adapt to market changes. As market dynamics and user needs change, timely updates to the screened users can ensure the capture of the latest potential valuable users.

[0116] As an embodiment of the present invention, updating the screened users using the operating network to obtain updated users includes: constructing an interest set of the screened users using the operating network, and calculating the degree of interest preference of the screened users towards the interest set using the following formula:

[0117]

[0118] in, Indicates the degree of interest and preference. This indicates a set of users who have been excessively filtered in relation to the aforementioned interest set. This represents the ratings of user R for interest i. This represents the average mean of the interests of the filtered user R in the user's already rated interest. Represents a set of users to be filtered. This represents the rating vector for filtering user R;

[0119] Based on the degree of interest preference, the screened users are updated to obtain updated users.

[0120] Optionally, the process of updating the selected users based on the degree of interest preference can be achieved by rating the selected users according to the degree of interest preference, and updating the users based on the rating results. For example, a rating between 0.1 and 0.5 indicates a fair rating, between 0.5 and 0.7 indicates a moderate rating, and between 0.7 and 1 indicates a high rating. Users with a rating of 0.7 or higher can be extracted as target users.

[0121] As an optional embodiment of the present invention, the step of constructing the interest set of the screened users using the operating network includes: querying the user data of the screened users, extracting the data features of the user data, clustering the data features to obtain clustering features, constructing interest tags for the clustering features, assigning weights to the interest tags to obtain weighted tags, and optimizing the weighted tags to obtain the interest set of the screened users.

[0122] The user data features can be derived from frequently browsed product categories, purchased brands, and followed promotional activities. Clustering these features to obtain clustering characteristics means grouping similar interest features together. This can be done using clustering algorithms. The interest tags can be constructed using Java-generated code. Weighting the interest tags involves assigning weights to different interest tags based on factors such as the frequency and depth of user behavior in each interest category, representing the user's level of interest in that interest. The weighted tag assignment is then optimized.

[0123] A user's interest set can be formed by integrating all interest tags and weights. Simultaneously, the interest set can be continuously optimized, removing noise and inaccurate interest tags, and updating weights to reflect changes in user interests.

[0124] By designating the updated users as the target users for the operational products, the embodiments of the present invention can construct an initial operational strategy for the operational products in a targeted manner, avoiding waste of resources and blind investment, making operational activities more efficient, and by formulating strategies based on the characteristics and needs of the target users, can better meet their expectations and improve user satisfaction.

[0125] Optionally, the initial operating strategy may include product strategy, pricing strategy, channel strategy, and promotion strategy based on the target users.

[0126] The product strategy refers to optimizing product characteristics and launching new product lines to meet the needs of target users. The pricing strategy refers to formulating pricing, promotion and discount schemes based on the price sensitivity of target users. The channel strategy refers to selecting suitable channels for target users to obtain information and make purchases, such as specific e-commerce platforms and social media. The promotion strategy refers to determining the methods, content and platforms for advertising, as well as the forms of activities to interact with users.

[0127] Furthermore, by conducting a supply risk assessment on the target operating user, the embodiments of the present invention can help users identify potential supply problems in advance, such as stockouts and delayed deliveries, and thus take timely measures to prevent them.

[0128] Optionally, the supply risk assessment of the target operating user can be conducted by identifying risk factors, collecting data, quantifying risks, and calculating a comprehensive risk score.

[0129] The risk factors identified may include: supplier factors such as stability, financial condition, production capacity, and delivery capability; logistics factors such as reliability of transportation methods, stability of transportation routes, and complexity of customs clearance; market demand fluctuations, the magnitude and predictability of demand changes from target users; and raw material supply factors such as supply stability and price fluctuations. Data collection may include: obtaining relevant information from suppliers, such as financial statements and production plans; analyzing market research data and historical sales data to understand demand trends; and collecting service level data from logistics partners. Risk quantification may include: setting a scoring standard for each risk factor, for example, a score of 1-5, where 1 represents low risk and 5 represents high risk; and assigning a score to each risk factor based on the collected data. The calculated comprehensive risk score can be obtained by weighting and summing the scores of each risk factor according to the set weights. For example, if the supplier's unstable financial condition scores 4 points, the logistics transportation route is susceptible to weather conditions scores 3 points, and the target user's demand fluctuates significantly scores 4 points, with each factor having equal weight, the comprehensive risk score is 3.67 points, which is considered medium risk. Further analysis revealed that the supplier's financial problems may have led to supply disruptions, necessitating the search for alternative suppliers or the establishment of inventory buffers.

[0130] Furthermore, the embodiments of the present invention, by constructing the cross-border e-commerce commodity operation management strategy based on the initial operation strategy and the evaluation results, can improve operational efficiency and effectiveness: by comprehensively considering factors such as the initial strategy and supply risks, it can plan operational activities more comprehensively and accurately, avoiding blindness and one-sidedness, thereby improving operational efficiency and final effectiveness, reducing risks, and adjusting and optimizing the operation strategy according to the supply risk assessment results, which helps to deal with possible supply problems in advance and reduce business risks caused by supply interruptions, delays, etc.

[0131] like Figure 2 The diagram shown is a functional module diagram of a product operation and management system applied to cross-border e-commerce according to the present invention.

[0132] The product operation management system 200 for cross-border e-commerce described in this invention can be installed in an electronic device. Depending on the functions implemented, the product operation management system for cross-border e-commerce may include a dataset optimization module 201, an operation network construction module 202, a user filtering module 203, and an operation strategy construction module 204. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0133] In this embodiment of the invention, the functions of each module / unit are as follows:

[0134] The dataset optimization module 201 is used to collect product operation data related to cross-border e-commerce, query the key operation attributes of the product operation data, divide the product operation data based on the key operation attributes to obtain a divided dataset, calculate the data gain in the divided dataset, and optimize the divided dataset based on the data gain to obtain an optimized dataset.

[0135] The operation network construction module 202 is used to perform multi-source analysis on the product operation data, select data features of the optimized dataset according to the analysis results of the multi-source analysis, query the operating products in the cross-border e-commerce, calculate the mutual information value between the data features and the operating products, and construct the operation network of the operating products based on the mutual information value.

[0136] The user filtering module 203 is used to obtain the operation channels of the operation product, query the potential users of the operation product based on the operation channels, query the sticky users of the operation product, analyze the user similarity between the potential users and the sticky users, extract target potential users from the potential users based on the user similarity, construct the filtering matrix of the target potential users, and use the filtering matrix to filter the target potential users to obtain filtered users.

[0137] The operation strategy construction module 204 is used to update the screened users using the operation network to obtain updated users, and after using the updated users as the target operation users of the operation products, construct an initial operation strategy for the operation products, conduct a supply risk assessment on the target operation users to obtain the assessment results, and construct the cross-border e-commerce product operation management strategy based on the initial operation strategy and the assessment results.

[0138] In detail, the modules in the commodity operation management system 200 applied to cross-border e-commerce described in this embodiment of the invention adopt the same usage as described above. Figure 1 This method uses the same technical means as the commodity operation and management method described in the article, and can produce the same technical effect, so it will not be elaborated here.

[0139] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A product operation management method applied to cross-border e-commerce, characterized in that, The method includes: Collect product operation data related to cross-border e-commerce, query the key operational attributes of the product operation data, divide the product operation data based on the key operational attributes to obtain a partitioned dataset, calculate the data gain in the partitioned dataset, and optimize the partitioned dataset based on the data gain to obtain an optimized dataset. Calculating the data gain in the partitioned dataset includes: calculating the information entropy in the partitioned dataset using the following formula: in, Let n represent the information entropy, and n represent the number of categories that divide the dataset. This represents the proportion of the j-th class of datasets; Calculate the conditional entropy of the corresponding key attributes in the partitioned dataset. Based on the information entropy and the conditional entropy, calculate the data gain in the partitioned dataset using the following formula: in, Indicates data gain. Represents information entropy. Represents conditional entropy; Multi-source analysis is performed on the product operation data to select data features for the optimized dataset based on the analysis results. The operational products in the cross-border e-commerce are then queried, and the mutual information value between the data features and the operational products is calculated. Based on the mutual information value, an operational network for the operational products is constructed. The step of querying the operational products in the cross-border e-commerce platform and calculating the mutual information value between the data features and the operational products includes: The mutual information value between the data features and the operational goods is calculated using the following formula: in, Let 'a' represent a feature in data feature A, and 'b' represent a product in operational product B. Let a and b represent the joint probability distribution. Describes the marginal probability distribution of a. Describes the marginal probability distribution of b. This indicates the specific value b for the product B being operated. This represents the specific value 'a' of data feature A; The process involves: acquiring the operational channels of the product; querying potential users and active users of the product based on these channels; analyzing the user similarity between the potential users and active users; extracting target potential users from the potential users based on the user similarity; constructing a screening matrix for the target potential users; and using the screening matrix to screen the target potential users to obtain screened users. The analysis of the user similarity between the potential users and active users includes: transforming the potential users and active users into vectors to obtain a first vector and a second vector; and calculating the vector similarity between the first vector and the second vector using the following formula: in, Represents vector similarity, Denotes the first vector. Represents the second vector. Denotes the magnitude of the first vector. Denotes the magnitude of the second vector; Based on the vector similarity, the user similarity between the potential user and the sticky user is determined; The selected users are updated using the operational network to obtain updated users. These updated users are then used as target users for the operational products. An initial operational strategy for the operational products is then constructed. A supply risk assessment is performed on the target users to obtain the assessment results. Based on the initial operational strategy and the assessment results, a product operation management strategy for the cross-border e-commerce is constructed. The step of updating the screened users using the operating network to obtain updated users includes: The interest set of the screened users is constructed using the operating network; Calculate the degree of interest preference of the selected users for the interest set based on the interest set; Based on the degree of interest preference, the screened users are updated to obtain updated users.

2. The method according to claim 1, characterized in that, The process of partitioning the product operation data based on the key operational attributes to obtain a partitioned dataset includes: The key operational attributes are transformed into vectors, and the centroids of the transformed vectors are constructed. The product operation data is then vectorized to obtain the operation vector; Calculate the vector distance between the operational vector and the centroid of the vector: Based on the vector distance, the product operation data is divided into data segments to obtain a segmented dataset.

3. The method according to claim 1, characterized in that, The step of performing multi-source analysis on the product operation data, and selecting data features of the optimized dataset based on the analysis results of the multi-source analysis, includes: The product operation data is integrated to obtain integrated data; Determine the analytical metrics for the integrated data; Based on the aforementioned analytical indicators, a correlation analysis is performed on the integrated data to obtain the correlation analysis results. Based on the aforementioned analytical indicators, trend analysis is performed on the integrated data to obtain trend analysis results; Based on the aforementioned analytical indicators, the integrated data is compared and analyzed to obtain the comparative analysis results. Based on the correlation analysis results, the trend analysis results, and the comparative analysis results, the data characteristics of the optimized dataset are determined.

4. The method according to claim 1, characterized in that, The construction of the screening matrix for the target potential users includes: Determine the screening dimensions for the target potential users and calculate the dimension weights for the screening dimensions; Construct a matrix framework for the target potential users; Obtain user data of the target potential users; Based on the dimensional weights and the user data, the matrix framework is populated with data to obtain a filtering matrix.

5. The method according to claim 1, characterized in that, The construction of the interest set for the screened users using the operating network includes: Query the user data of the filtered users; Extract the data features from the user data; The data features are clustered to obtain cluster features; Construct interest tags for the clustering features; The interest tags are weighted to obtain weighted tags; After optimizing the weighted assignment labels, the interest set of the selected users is obtained.

6. A product operation management system applied to cross-border e-commerce, wherein the system implements the method as described in any one of claims 1-5, characterized in that, The system includes: The dataset optimization module is used to collect product operation data related to cross-border e-commerce, query the key operational attributes of the product operation data, divide the product operation data based on the key operational attributes to obtain a partitioned dataset, calculate the data gain in the partitioned dataset, and optimize the partitioned dataset based on the data gain to obtain an optimized dataset. Calculating the data gain in the partitioned dataset includes: calculating the information entropy in the partitioned dataset using the following formula: in, Let n represent the information entropy, and n represent the number of categories that divide the dataset. This represents the proportion of the j-th class of datasets; Calculate the conditional entropy of the corresponding key attributes in the partitioned dataset. Based on the information entropy and the conditional entropy, calculate the data gain in the partitioned dataset using the following formula: in, Indicates data gain. Represents information entropy. Represents conditional entropy; The operation network construction module performs multi-source analysis on the product operation data, selects data features of the optimized dataset based on the analysis results of the multi-source analysis, queries the operational products in the cross-border e-commerce, calculates the mutual information value between the data features and the operational products, and constructs the operation network of the operational products based on the mutual information value. The step of querying the operational products in the cross-border e-commerce platform and calculating the mutual information value between the data features and the operational products includes: The mutual information value between the data features and the operational goods is calculated using the following formula: in, Let 'a' represent a feature in data feature A, and 'b' represent a product in operational product B. Let a and b represent the joint probability distribution. Describes the marginal probability distribution of a. Describes the marginal probability distribution of b. This indicates the specific value b for the product B being operated. This represents the specific value 'a' of data feature A; The user filtering module is used to obtain the operational channels of the operational product, query potential users of the operational product based on the operational channels, query the sticky users of the operational product, analyze the user similarity between the potential users and the sticky users, extract target potential users from the potential users based on the user similarity, construct a filtering matrix for the target potential users, and use the filtering matrix to filter the target potential users to obtain filtered users. The user similarity analysis between the potential users and the sticky users includes: transforming the potential users and the sticky users into vectors to obtain a first vector and a second vector, and calculating the vector similarity between the first vector and the second vector using the following formula: in, Represents vector similarity, Denotes the first vector. Represents the second vector. Denotes the magnitude of the first vector. Denotes the magnitude of the second vector; Based on the vector similarity, the user similarity between the potential user and the sticky user is determined; The operation strategy construction module is used to update the screened users using the operation network to obtain updated users. After using the updated users as the target operation users for the operation products, an initial operation strategy for the operation products is constructed. A supply risk assessment is performed on the target operation users to obtain the assessment results. Based on the initial operation strategy and the assessment results, the cross-border e-commerce product operation management strategy is constructed. The step of updating the screened users using the operating network to obtain updated users includes: The interest set of the screened users is constructed using the operating network; Calculate the degree of interest preference of the selected users for the interest set based on the interest set; Based on the degree of interest preference, the screened users are updated to obtain updated users.

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