Cross-border e-commerce self-operation item selection dynamic adjustment method and system based on machine learning

Abstract

The invention discloses a machine learning-based cross-border e-commerce self-operation product selection dynamic adjustment method and system, relates to the technical field of e-commerce product selection adjustment, and aims to construct a product selection adsorption quantity model, evaluate the sales attraction of different products in different market situations, analyze the market response condition of each product and improve the product selection efficiency. After the commodities are screened in combination with the output result of the commodity selection adsorption quantity model, the commodities are adaptively adjusted, the racking sequence is determined, the market response condition analysis result is sent to the dynamic optimization module, and based on the market response condition analysis result of the commodities, the discount strength of the commodities is dynamically adjusted by simulating the market dynamic state; according to the analysis of commodity sales data and market feedback, the shelf sequence and marketing promotion strategy of commodities are optimized. According to the adjustment method, through introduction of a machine learning technology and combination of a data driving mode, commodity sales data and market environment changes can be analyzed from multiple dimensions, and commodity selection and adjustment efficiency of a cross-border e-commerce platform is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of e-commerce product selection adjustment, in particular to a cross-border e-commerce self-operated product selection dynamic adjustment method and system based on machine learning. BACKGROUND

[0002] Cross-border e-commerce faces diversified market demand, complex logistics and tariff problems, and time-sensitive competitive environment. Merchants need to adjust the selection of goods according to changes in consumer demand, fluctuations in market trends and other external factors to maintain competitiveness. However, the traditional manual product selection method is not only inefficient, but also easy to miss market opportunities. Therefore, how to improve the accuracy and flexibility of product selection through intelligent means has become an important issue in the field of cross-border e-commerce.

[0003] The prior art has the following defects:

[0004] The traditional product selection method does not have enough flexibility to dynamically adjust the product listing order and promotion strategy, which may result in low sales of goods due to market changes or the influence of competitors. Ignoring the market reaction of goods after actual listing, lacking real-time monitoring and feedback mechanism, unable to timely adjust product selection and marketing strategy to adapt to changes in consumer demand, thereby reducing the market adaptability of goods and the overall sales efficiency of the platform.

[0005] Based on this, the present application proposes a cross-border e-commerce self-operated product selection dynamic adjustment method and system based on machine learning, which can analyze the sales data of goods and the changes of market environment from multiple dimensions by introducing machine learning technology and combining data-driven methods, significantly improving the efficiency of product selection and adjustment of cross-border e-commerce platforms. SUMMARY

[0006] The purpose of the present application is to provide a cross-border e-commerce self-operated product selection dynamic adjustment method and system based on machine learning to solve the problems in the background art.

[0007] In order to achieve the above purpose, the present application provides the following technical solution: a cross-border e-commerce self-operated product selection dynamic adjustment method based on machine learning, 1. A cross-border e-commerce self-operated product selection dynamic adjustment method based on machine learning, characterized in that: the adjustment method comprises the following steps:

[0008] S1: Collecting historical sales data of cross-border e-commerce platforms and obtaining market environment data, and performing data cleaning and standardization processing on all collected data;

[0009] S2: Integrating the product attributes and market environment data to construct a feature set, analyzing the feature set and extracting feature data related to product sales and market performance;

[0010] S3: Based on the collected product attribute data and market environment characteristics, construct a product selection attraction model, and evaluate the sales attractiveness of different products in different market scenarios through multiple rounds of calculation and simulation.

[0011] S4: After the products are listed, analyze the market response of each product, combine the product selection and absorption model output results to screen the products, adaptively adjust the products and determine the listing order.

[0012] S5: Based on the analysis of the market response to the product, dynamically adjust the discount level of the product by simulating market dynamics;

[0013] S6: Optimize the product listing order and marketing promotion strategies based on the analysis of product sales data and market feedback.

[0014] In a preferred embodiment, based on the analysis of market response to the product, the discount level of the product is dynamically adjusted by simulating market dynamics, including the following steps:

[0015] Based on the impact of price changes on sales volume, price elasticity is simulated, and the impact of price changes on sales volume is calculated by establishing a regression model between price and sales volume.

[0016] Simulate competitors' pricing behavior;

[0017] Based on seasonal and holiday factors, simulate the fluctuations in demand for goods under different market environments;

[0018] Based on market simulation and demand analysis, different discount levels are set for products according to the competitive landscape and market demand.

[0019] In a preferred embodiment, the product listing order and marketing promotion strategy are optimized based on the analysis of product sales data and market feedback, including the following steps:

[0020] Real-time tracking and collection of product sales data; analysis of product sales data and market feedback to determine the priority display order of different products.

[0021] After a product is listed, its marketing and promotion strategies are adjusted in real time based on its market performance, competitive landscape, and sales data.

[0022] By continuously monitoring product sales data, conversion rates, and changes in competitors, we can make real-time adjustments based on market changes.

[0023] Regularly review the market performance of each product and adjust its marketing strategy based on long-term data feedback.

[0024] In a preferred embodiment, after products are listed, the market response of each product is analyzed. Products are then screened based on the results of the product selection and absorption model. The products are then adaptively adjusted, and the listing order is determined. This includes the following steps:

[0025] The product selection and absorption model provides each product with a market adaptability score and potential sales capacity before it is listed. It obtains the market adaptability score by summing the normalized historical sales volume of similar products, promotional efforts, user attention, and platform traffic.

[0026] Based on the market performance and the scores output by the absorption capacity model, the products are ranked according to their potential sales capacity.

[0027] In a preferred embodiment, a product selection and attraction model is constructed based on collected product attribute data and market environment characteristics, including the following steps:

[0028] By combining product attributes with market environment data, a multi-dimensional input dataset is formed;

[0029] The training dataset contains historical sales records, with each record including product attribute features, market environment features, and sales performance labels for that product.

[0030] The feature variables are standardized to scale all input features to a uniform numerical range. For categorical features, one-hot encoding is used for numerical conversion.

[0031] The data is input into the SVM model for training. After training, a product selection attraction model is built to analyze the sales attractiveness of new or existing products in different market environments.

[0032] In a preferred embodiment, evaluating the sales appeal of different products in different market scenarios includes the following steps:

[0033] For each product to be evaluated, the product attribute features and current market environment features are input into the pre-trained SVM model, and the output is the probability that the product belongs to the category of high sales attractiveness or low sales attractiveness.

[0034] In a preferred embodiment, constructing a feature set by integrating product attributes with market environment data includes the following steps:

[0035] Product attribute data includes brand, category, price, inventory, and sales channels. Market environment data is used to supplement the market background information of the product, including consumer behavior data, competitive landscape, and market demand fluctuations.

[0036] The acquired data on brands, categories, prices, inventory, sales channels, consumer behavior, competitive landscape, and market demand fluctuations will be used to construct a feature set.

[0037] In a preferred embodiment, analyzing the feature set and extracting feature data related to product sales and market performance includes the following steps:

[0038] The data in the feature set is subjected to missing value imputation, standardization and normalization, multinomial feature construction and category encoding;

[0039] By using tree-based models or L1-regularized linear models, features relevant to product sales can be automatically selected.

[0040] After feature extraction and processing are completed, all product attribute features and market environment features will be integrated into a comprehensive feature set.

[0041] In a preferred embodiment, historical sales data from cross-border e-commerce platforms are collected, along with market environment data. The historical sales data includes product sales volume, inventory levels, pricing strategies, and promotional activity information. The market environment data includes changes in consumer preferences, competitors' pricing strategies, industry trends, and seasonal changes.

[0042] This application also provides a machine learning-based cross-border e-commerce self-operated product selection dynamic adjustment system, including a data collection and processing module, a product analysis and adjustment module, and a dynamic optimization module;

[0043] Data collection and processing module: Collects historical sales data from cross-border e-commerce platforms, and acquires market environment data. It cleans and standardizes all collected data, integrates product attributes and market environment data to construct feature sets, analyzes feature sets, and extracts feature data related to product sales and market performance.

[0044] Product Analysis and Adjustment Module: Based on collected product attribute data and market environment characteristics, a product selection and absorption model is constructed. Through multiple rounds of calculation and simulation, the sales attractiveness of different products in different market scenarios is evaluated. After the products are put on the shelves, the market response of each product is analyzed. After filtering the products based on the output results of the product selection and absorption model, the products are adaptively adjusted and the shelf order is determined.

[0045] Dynamic optimization module: Based on the analysis of market response to products, the module dynamically adjusts product discounts by simulating market dynamics and optimizes product listing order and marketing strategies based on the analysis of product sales data and market feedback.

[0046] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0047] This invention constructs a product selection and absorption capacity model based on collected product attribute data and market environment characteristics. Through multiple rounds of calculation and simulation, it evaluates the sales attractiveness of different products in different market scenarios. After products are listed, the market response of each product is analyzed. Combining the output of the product selection and absorption capacity model, products are screened, adaptively adjusted, and their listing order is determined. The market response analysis results are sent to a dynamic optimization module. Based on the market response analysis results, the module dynamically adjusts product discounts by simulating market dynamics. Based on the analysis of product sales data and market feedback, the module optimizes the product listing order and marketing promotion strategies. This adjustment method, by introducing machine learning technology and combining it with a data-driven approach, can analyze product sales data and market environment changes from multiple dimensions, significantly improving the efficiency of product selection and adjustment on cross-border e-commerce platforms. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0049] Figure 1 This is a flowchart of the adjustment method of the present invention.

[0050] Figure 2 The system architecture diagram was adjusted for this invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: Please refer to Figure 1 As shown, this embodiment provides a method for dynamically adjusting self-operated product selection in cross-border e-commerce based on machine learning. The adjustment method includes the following steps:

[0053] S1: Collect and integrate cross-border e-commerce sales data and market environment data

[0054] First, historical sales data from cross-border e-commerce platforms is collected, including product sales volume, inventory levels, pricing strategies, and promotional activities. Simultaneously, market environment data is acquired, such as changes in consumer preferences, competitor pricing strategies, industry trends, and seasonal variations. All collected data undergoes data cleaning and standardization to ensure high quality and consistency, providing a solid foundation for subsequent analysis and decision-making.

[0055] S2: Extract and load product attributes and market environment characteristics

[0056] By integrating basic product attributes (such as brand, category, price, inventory, and sales channels) with market environment data (such as consumer behavior data, competitive landscape, and market demand fluctuations), a comprehensive feature set is constructed. This data is then analyzed in depth to extract features closely related to product sales and market performance. Feature engineering techniques are used to ensure the model effectively captures the relationship between products and the market environment, providing data support for subsequent product selection decisions.

[0057] S3: Construct a product selection and adsorption model

[0058] Based on collected product attribute data and market environment characteristics, a product selection and attraction model is constructed. This model considers the interrelationship between product characteristics (such as brand influence, price positioning, and promotional efforts) and the market environment (such as consumer preferences, seasonal changes, and competitor activities). Through multiple rounds of calculation and simulation, the sales attractiveness of different products under different market scenarios is evaluated, thereby determining the market adaptability and potential sales capacity of the products.

[0059] S4: Evaluate sales performance after the product is listed.

[0060] After products are listed, the market response to each product is analyzed. Based on the output of the product selection and absorption model, products with high sales potential are prioritized, products with poor market performance are identified and adjusted accordingly, and the listing order of products is determined.

[0061] S5: Dynamically adjust product pricing and promotion strategies

[0062] Based on the analysis of market response to the product, the discount level can be flexibly adjusted by simulating market dynamics (such as competitor pricing and market demand fluctuations). At this point, the product's attractiveness, inventory levels, and market demand must be considered to ensure that pricing and promotional activities maximize the product's sales potential, increase market share, and improve profitability.

[0063] S6: Optimize product listing order and marketing strategies

[0064] Based on the analysis of product sales data and market feedback, we further optimize the product listing order and marketing strategies. For products with excellent market performance, we increase their exposure and promotion efforts, and increase their display frequency on the platform; for products with poor performance, we adjust their listing order or their marketing strategies (such as repricing or repositioning). This dynamic adjustment ensures that the e-commerce platform remains competitive and maximizes market benefits.

[0065] This application constructs a product selection and attraction model based on collected product attribute data and market environment characteristics. Through multiple rounds of calculation and simulation, it evaluates the sales attractiveness of different products in different market scenarios. After products are listed, the market response of each product is analyzed. Combining the output of the product selection and attraction model, products are screened, adaptively adjusted, and their listing order is determined. The market response analysis results are sent to a dynamic optimization module. Based on the market response analysis results, the module dynamically adjusts product discounts by simulating market dynamics. Based on the analysis of product sales data and market feedback, the listing order and marketing strategies are optimized. This adjustment method, by introducing machine learning technology and combining it with a data-driven approach, can analyze product sales data and market environment changes from multiple dimensions, significantly improving the efficiency of product selection and adjustment on cross-border e-commerce platforms.

[0066] Please see Figure 2 As shown, this embodiment provides a machine learning-based cross-border e-commerce self-operated product selection dynamic adjustment system, including a data collection and processing module, a product analysis and adjustment module, and a dynamic optimization module;

[0067] Data collection and processing module: Collects historical sales data from cross-border e-commerce platforms, and acquires market environment data. It cleans and standardizes all collected data, integrates product attributes and market environment data to construct a feature set, analyzes the feature set and extracts feature data related to product sales and market performance, and sends the feature data to the product analysis and adjustment module.

[0068] Product Analysis and Adjustment Module: Based on collected product attribute data and market environment characteristics, a product selection and absorption model is constructed. Through multiple rounds of calculation and simulation, the sales attractiveness of different products in different market scenarios is evaluated. After the products are put on the shelves, the market response of each product is analyzed. After filtering the products based on the output results of the product selection and absorption model, the products are adaptively adjusted and the shelf order is determined. The market response analysis results are sent to the dynamic optimization module.

[0069] Dynamic optimization module: Based on the analysis of market response to products, the module dynamically adjusts product discounts by simulating market dynamics and optimizes product listing order and marketing strategies based on the analysis of product sales data and market feedback.

[0070] S1: Collect and integrate cross-border e-commerce sales data and market environment data

[0071] First, historical sales data from cross-border e-commerce platforms is collected, including product sales volume, inventory levels, pricing strategies, and promotional activities. Simultaneously, market environment data is acquired, such as changes in consumer preferences, competitor pricing strategies, industry trends, and seasonal variations. All collected data undergoes data cleaning and standardization to ensure high quality and consistency, providing a solid foundation for subsequent analysis and decision-making.

[0072] In the product selection and adjustment process on cross-border e-commerce platforms, data collection and integration is a crucial first step. This step begins with collecting historical sales data from the cross-border e-commerce platform, including but not limited to product sales volume, inventory levels, pricing strategies, promotional activities, user reviews, and feedback. This data provides the foundation for subsequent analysis, helping to understand the historical performance and market demand of different products. Sales volume data reflects the popularity of products, while inventory and pricing strategies help analyze the effectiveness of the platform's product management strategies. Promotional activity data helps analyze the impact of promotions on product sales, thereby optimizing future promotional activities.

[0073] Furthermore, obtaining market environment data is equally important. This includes external factors such as changes in consumer preferences, competitor pricing strategies, industry trends, and seasonal variations. This market environment data helps e-commerce platforms better understand external market dynamics and consumer demand. For example, changes in consumer preferences may reflect fluctuations in consumer interest or demand for a particular type of product; competitor pricing strategies can reveal the competitive landscape; and industry trends help e-commerce platforms anticipate future market developments. Seasonal variations (such as holidays and promotional seasons) are a particularly important factor to consider in cross-border e-commerce because the demand for goods varies significantly across different seasons, holidays, or promotional periods.

[0074] After data collection, it needs to be cleaned and standardized. The goal of data cleaning is to eliminate noise and outliers in the data and fill in missing data. For example, sales data may contain outliers, such as extreme peaks within a certain period, which may be due to inventory errors or promotional activities. By using methods such as IQR (Interquartile Range) or Z-Score (Standardized Deviation), these outliers can be identified and removed, ensuring that the data accurately reflects the market.

[0075] In addition, data standardization is also an important step in data preprocessing. Standardization can eliminate the influence of differences in units or scales in the data, making the variables comparable in the analysis. Common standardization methods include Min-Max standardization and Z-Score standardization. Z-Score standardization converts each data point to its standard deviation unit, resulting in a unitless standardized value, as shown in the formula: Where X represents the original data, μ is the mean of the data, and σ is the standard deviation of the data. This method allows for the standardization of data from different products and market environments, facilitating subsequent model analysis.

[0076] For example, if the sales data of the products we collect varies greatly across different time scales (such as days, weeks, and months), after standardization, the sales data of each product can be unified into the same standard scale, which facilitates subsequent comparison and analysis of each product.

[0077] Next, data integration is a crucial part of this step. When integrating sales data with market environment data, it's essential to ensure that each record has complete upstream and downstream information. For example, a product's sales volume, price, inventory, and promotional activities should be consistent with current competitor pricing, market trends, and consumer behavior data, and this data should be aligned by time and product. Only in this way can the model effectively understand the relationship between a product's market performance and environmental factors.

[0078] Through this process of data collection, cleaning, standardization, and integration, e-commerce platforms can obtain high-quality, structured, and comparable raw data, ensuring that subsequent analysis can more accurately reflect the relationship between product sales and market dynamics, and providing a solid data foundation for cross-border e-commerce product selection and strategy adjustment.

[0079] S2: Extract and load product attributes and market environment characteristics

[0080] By integrating basic product attributes (such as brand, category, price, inventory, and sales channels) with market environment data (such as consumer behavior data, competitive landscape, and market demand fluctuations), a comprehensive feature set is constructed. This data is then analyzed in depth to extract features closely related to product sales and market performance. Feature engineering techniques are used to ensure the model effectively captures the relationship between products and the market environment, providing data support for subsequent product selection decisions.

[0081] In the process of product selection for self-operated cross-border e-commerce, building a comprehensive feature set is a crucial step. This feature set needs to include not only the basic attributes of the products, but also various factors in the market environment to ensure that the product selection model can effectively identify the intrinsic relationship between products and market dynamics, thereby providing accurate support for product selection decisions.

[0082] Product attribute data is fundamental to understanding product market performance, including but not limited to the following aspects:

[0083] Brand: The influence of brand on consumer purchasing decisions cannot be ignored. Some well-known brands may have high consumer loyalty, therefore the role of brand in the product selection model needs to be weighted. The classification features of product brands can be processed using one-hot encoding to ensure that the model can identify brand categories.

[0084] Product Category: The product category (such as electronics, household goods, etc.) has a significant impact on sales performance, and different categories have different market demands. Category characteristics are usually processed using one-hot encoding or category label encoding.

[0085] Price: Price is one of the core factors influencing consumer purchasing decisions; therefore, price levels and volatility need to be included in the feature set. Price volatility can be measured by calculating the rate of price change (i.e., the ratio of the current price to the price at a certain past period), serving as one of the characteristics of price volatility.

[0086] Inventory: Product inventory levels are closely related to market demand. High inventory may indicate that demand is not meeting expectations, while low inventory may lead to missed sales opportunities. Inventory data usually needs to be analyzed in conjunction with sales data to generate derivative indicators such as "inventory turnover rate".

[0087] Sales channels: A product's sales channels also affect its market performance. For example, some products may only be sold on specific online platforms, while others may be sold simultaneously in physical stores and on multiple e-commerce platforms. The diversity of sales channels and their impact on sales volume need to be categorized using classification features.

[0088] Market environment data can supplement product market background information, providing a broader perspective for product selection models. This mainly includes the following aspects:

[0089] Consumer behavior data: This type of data includes information such as consumers' purchase history, browsing behavior, and click-through rates. By extracting features from user behavior, such as "purchase frequency," "average purchase interval," and "purchase category preference," we can better understand consumer demand trends.

[0090] Competitive Landscape: Competitors' pricing strategies, market share, and promotional activities are key factors influencing product sales. For example, if a competitor's product is priced significantly lower than yours, it may alter consumer purchasing preferences. Therefore, competitive landscape data can be quantified using features such as "competitive price fluctuations" and "market share."

[0091] Market demand fluctuations: The cross-border e-commerce market is typically significantly affected by seasonal changes and holiday promotions. By analyzing seasonal trends in historical sales data and extracting the "seasonal demand fluctuation" characteristic, models can help identify changes in market demand for goods at different times. For example, demand for a certain type of product may surge during the year-end promotional season, while demand may drop sharply during the off-season.

[0092] Feature engineering techniques can effectively improve the performance and accuracy of models. The specific methods are as follows:

[0093] Missing value imputation: For missing data, imputation is performed using the mean, median, or K-nearest neighbor algorithm to ensure data integrity. For categorical features, if there are many missing values, the most common category can be used for imputation.

[0094] Standardization and Normalization: Numerical features such as price and sales volume have large ranges of values. To prevent certain features from dominating the model training process, it is necessary to standardize or normalize the numerical data. Standardization methods typically use a transformation with a mean of zero and a standard deviation of one, while normalization compresses the data to the [0,1] interval.

[0095] Multinomial feature construction: By generating interaction terms between features such as product price and sales volume, inventory and promotion intensity (e.g., the product of price and promotion), higher-dimensional information can be provided to the model, helping it to capture more complex relationships between products and the market.

[0096] Category Encoding: For categorical features (such as product category, brand, etc.), one-hot encoding or label encoding can be used. One-hot encoding converts each category into a binary feature, while label encoding converts the categories into integer labels, suitable for features with ordered categories.

[0097] In predicting product sales, feature selection is a crucial step in improving model performance and reducing overfitting. Tree-based models (such as decision trees, random forests, and XGBoost) and L1-regularized linear models (such as Lasso regression) can automatically select the features most relevant to product sales. The following sections introduce these two methods and illustrate how they select features relevant to product sales through examples.

[0098] Random Forest is an ensemble learning method that performs classification or regression by training multiple decision trees and voting on the results. It can automatically handle feature selection by evaluating the importance of features based on their contribution to the model's performance. For each feature, Random Forest calculates its "importance" when splitting the data in all trees, i.e., its contribution to reducing prediction error.

[0099] Train multiple decision trees: Each tree is trained using a different subset of the training data and a different subset of features.

[0100] Calculate feature importance: Each time a decision tree splits the data based on a certain feature, Random Forest records the change in the "information gain" or "Gini index" of that feature, thereby measuring the feature's contribution to classification or regression.

[0101] Sort features: By sorting all features, the Random Forest model places features with greater contributions in the front and features with smaller contributions at the back, ultimately forming a list of feature importances.

[0102] Suppose we are building a model to predict the sales volume of a product. The feature data includes: Brand, Price, Stock, Promotion, and Seasonality. After training with Random Forest, we may obtain the following feature importance ranking: Promotion, Stock, Price, Brand, Seasonality;

[0103] Based on this ranking, we can see that "Promotion" has the greatest impact on predicting the sales volume of the product, followed by "Stock" and "Price", while the contributions of "Brand" and "Seasonality" are relatively small. Based on these results, we can choose to remove unimportant features (such as "Brand" and "Seasonality") to simplify the model and improve its prediction performance.

[0104] The linear model with L1 regularization, commonly known as Lasso regression (Least Absolute Shrinkage and Selection Operator), performs feature selection by adding an L^1 regularization term to the loss function of the linear regression model. Lasso regression automatically performs feature selection by shrinking the coefficients of some features to zero. Different from ordinary linear regression, Lasso regression introduces a regularization term to penalize features with overly large coefficients, prompting the coefficients of some features to become zero, thereby eliminating irrelevant features.

[0105] Training a Lasso regression model: Lasso regression is similar to linear regression, but it adds an L1 regularization term to the loss function. By adjusting the regularization strength (controlled by the hyperparameter λ), Lasso regression gradually compresses the regression coefficients of certain features to zero.

[0106] Feature selection: During the regression process, Lasso selects features that have a significant impact on the model's predictions and compresses the coefficients of features that are irrelevant to sales predictions to zero.

[0107] Similarly, assuming we have the following features to predict product sales volume: Brand, Price, Stock, Promotion, Seasonality, and AdSpend, after training the Lasso regression model, the regression coefficients we obtain might be as follows: Brand: 0.5, Price: -0.2, Stock: 0.3, Promotion: 2.0, Seasonality: 0.0 (coefficient zero), AdSpend: 0.0 (coefficient zero). Through Lasso regression, the coefficients of the features "Seasonality" and "AdSpend" are compressed to zero, indicating that these two features do not have a significant effect in predicting product sales volume. At this point, we can choose to delete these two features, retaining only "Brand," "Price," "Stock," and "Promotion" as input features for the model.

[0108] Tree-based models (such as random forests) can automatically filter out features that have a significant impact on sales by calculating feature importance. They measure feature importance using "information gain" or the "Gini coefficient," thus helping to identify which features best predict a product's market performance.

[0109] L1-regularized linear models (Lasso regression) automatically compress the coefficients of some features to zero through a penalty term (L1 regularization), thereby achieving feature selection. It can effectively filter out the most predictive features from a large number of features, and is especially suitable for datasets with many and sparse features.

[0110] Through these two methods, e-commerce platforms can automatically identify and filter features that are highly correlated with product sales performance, thereby improving the accuracy of model predictions, reducing computational complexity, and ultimately optimizing product selection and marketing strategies.

[0111] Feature selection: By using tree-based models (such as Random Forest or XGBoost) or L1-regularized linear models, features that are highly relevant to product sales are automatically selected. For example, some product attributes may have a small impact on sales and can be removed in subsequent analysis to reduce computational complexity and avoid overfitting.

[0112] After completing the above feature extraction and processing, all product attribute features and market environment features will be integrated into a comprehensive feature set. This feature set not only includes the product's own attributes but also incorporates relevant data from the external market environment. In-depth analysis of these features provides effective data support for subsequent product selection models. For example, within a specific time period, the platform can analyze products from specific brands and price ranges, and optimize product selection and promotion strategies during specific holidays or promotional events, combining consumer behavior and competitive landscape. By incorporating these features into the model, the platform can dynamically adjust product listing strategies, pricing, and promotional activities to improve overall sales efficiency and profits. Through this process, cross-border e-commerce platforms can more accurately capture the complex relationship between products and the market environment, thus providing strong support for subsequent product selection decisions and strategy optimization.

[0113] S3: Construct a product selection and adsorption model

[0114] Based on collected product attribute data and market environment characteristics, a product selection and attraction model is constructed. This model considers the interrelationship between product characteristics (such as brand influence, price positioning, and promotional efforts) and the market environment (such as consumer preferences, seasonal changes, and competitor activities). Through multiple rounds of calculation and simulation, the sales attractiveness of different products under different market scenarios is evaluated, thereby determining the market adaptability and potential sales capacity of the products.

[0115] The product selection and attraction model aims to assess a product's sales appeal under different market scenarios by comprehensively considering product attributes and market environment characteristics, thereby determining its market adaptability and potential sales capacity. This model considers not only the product's intrinsic characteristics (such as brand influence, price positioning, and promotional efforts) but also the impact of external market environmental factors (such as consumer preferences, seasonal changes, and competitor activities). Through multiple rounds of calculation and simulation, the model can dynamically evaluate the performance of each product under specific market conditions and provide data support for subsequent product selection decisions.

[0116] First, it's necessary to combine key product attributes (such as brand, category, price, inventory, and promotional efforts) with market environment data (such as consumer behavior data, seasonal changes, competitor pricing strategies, and industry trends) to form a multi-dimensional input dataset. Product attribute data provides the basic characteristics of each product, while market environment data provides information on changes in external factors. Integrating this data comprehensively reflects a product's competitiveness and adaptability in the market.

[0117] For example, suppose a product has a low price, high inventory, but limited promotional efforts. If competitors are pricing it slightly lower, the model needs to assess the product's market attractiveness. In this case, price, inventory, and promotional efforts will serve as product characteristic inputs, while consumer behavior and competitor pricing will serve as market environment data inputs.

[0118] The core of the product selection and attraction model is to capture the relationship between products and the market environment through mathematical modeling. Commonly used techniques include:

[0119] Decision trees or random forests: These tree-structured models simulate the impact of different combinations of product attributes and market environments on sales attractiveness. Decision tree models can establish the relationship between product attributes and market environment on sales volume by progressively dividing the feature space.

[0120] Support Vector Machine (SVM): Used to evaluate a product's performance under changing market conditions by finding the decision boundary that maximizes the margin. This can help identify which products have higher sales appeal under different market conditions.

[0121] Support Vector Machines (SVMs), a commonly used supervised learning algorithm, are widely applied to classification and regression tasks. In the scenario of dynamic adjustment of self-operated product selection in cross-border e-commerce, SVMs can be used to identify which products are more attractive to buyers under different market conditions. The core idea is to find a decision boundary in the feature space that maximizes the class margin, thereby distinguishing between products with high and low sales attractiveness and providing a scientific basis for product selection and operational decisions.

[0122] First, a training dataset needs to be prepared. This dataset should contain a large number of historical sales records. Each record includes product attributes (such as brand, price, promotional information, inventory, etc.), market environment characteristics (such as competitor pricing, market demand fluctuations, seasonality, consumer behavior characteristics, etc.), and the product's sales performance label under specific market conditions (such as high sales / low sales, or multiple levels grouped according to sales volume). During the data processing stage, the feature variables need to be standardized, scaling all input features to a uniform numerical range to avoid biases in model training caused by different units of measurement. For categorical features, such as brand or product category, one-hot encoding or similar methods can be used for numerical representation.

[0123] After data preprocessing, the data is input into the SVM model for training. The training objective of SVM is to find an optimal decision hyperplane that maximizes the margin between samples of different categories (high-selling-appeal and low-selling-appeal products). If the data is not linearly separable, a kernel function (such as a radial basis function kernel or a multinomial kernel) can be used to map the feature space to a higher dimension to achieve non-linear classification. During model training, the SVM algorithm automatically selects a subset of "support vectors" that play a decisive role in the classification boundary—that is, sample points near the boundary. These support vectors collectively determine the final decision boundary.

[0124] After training, the model can be used to analyze the sales appeal of new or existing products under different market conditions. For each product to be evaluated, the system inputs its attribute features and current market environment features into the pre-trained SVM model. The model outputs the probability or score that the product belongs to the high or low sales appeal category. Through the model results, the platform can further identify the products with the greatest potential under specific environments and conditions, providing strong data support for operational decisions such as product listing priority, pricing, and promotions.

[0125] In practical applications, to ensure the reliability and business applicability of the model, it is also necessary to evaluate the generalization ability of SVM through methods such as cross-validation, and to regularly review and update the model based on actual feedback. For example, if it is found that the accuracy of the model decreases in a certain market environment, it may be necessary to supplement with new sample data or adjust the feature selection method to improve the stability and business value of the model.

[0126] In summary, Support Vector Machines (SVMs) construct decision boundaries by maximizing class margins, enabling a systematic and scientific evaluation of a product's sales appeal under different market conditions. This helps e-commerce platforms efficiently achieve dynamic product selection and strategy optimization.

[0127] To fully capture the complex interaction between products and the market environment, the model needs to perform multiple rounds of calculations and simulations. In this process, the interaction features between product characteristics and market environment data will be generated and used as new input features. For example, the combination of product price and promotion intensity (i.e., discounted price) may have different impacts on the attractiveness of the product; at the same time, the pricing fluctuations of competitors may make the product price show different attractiveness in different market environments. These interaction features can be calculated in the following ways: for example, by multiplying the product price by the seasonal fluctuation factors of the market, an interaction term of "price-seasonal fluctuation" is generated to analyze the performance of the price under different seasonal conditions. Through multiple rounds of simulation calculations of the performance of the product in different market environments (such as highly competitive, strong demand, etc.), the model can dynamically adjust the sales forecast of the product. The calculation results of each round will be used as the input for the next round to further optimize the prediction of the market attractiveness of the product.

[0128] Based on the modeling and simulation results of the above-mentioned product characteristics and market environment characteristics, the model will evaluate the adsorption amount of each product in a specific market situation, that is, the sales attractiveness and market adaptability of the product. The evaluation results can be carried out through the following steps:

[0129] According to the model output, calculate the adsorption amount score of each product, which is based on the comprehensive performance of the product and the market environment. A product with a higher score indicates a higher sales potential under the current market conditions, while a product with a lower score indicates a poor market adaptability and may require adjustments to its pricing or promotion strategies.

[0130] Rank the products according to the adsorption amount score. Products with higher rankings should be given priority for listing or promotion, while products with lower rankings may need to be adjusted or taken off the shelves. For example, assume that the model calculates that the adsorption amount of a certain product in the promotion season is 90 points and only 40 points in the off-season. Then this product has strong market attractiveness in the promotion season and needs to adjust its pricing or promotion strategies to enhance its attractiveness in the off-season.

[0131] The final output of the model not only provides the market adaptability score of the product but also can provide optimization suggestions for subsequent product selection decisions. For example, for products with low sales attractiveness, the model can suggest adjusting their pricing strategies, strengthening promotion efforts, or optimizing their inventory configuration. Through multiple rounds of simulation and optimization calculations, the model will dynamically provide real-time product adjustment plans to ensure that the platform can quickly respond to market changes.

[0132] The key to building a product selection and attraction model lies in combining product attributes with external market environment data, utilizing multi-round calculations and feature interaction analysis to accurately assess the sales attractiveness and market adaptability of products under different market conditions. This model not only provides a scientific basis for product selection and promotion strategies but also dynamically adjusts product performance according to changes in the market environment, ensuring that the platform maintains high market adaptability and sales potential in a dynamic competitive environment.

[0133] S4: Evaluate sales performance after the product is listed.

[0134] After products are listed, the market response to each product is analyzed. Based on the output of the product selection and absorption model, products with high sales potential are prioritized, products with poor market performance are identified and adjusted accordingly, and the listing order of products is determined.

[0135] After a product is successfully listed, evaluating its sales performance is a crucial step in the product selection process for cross-border e-commerce. By analyzing the market response to each product, e-commerce platforms can promptly identify underperforming items and make corresponding adjustments to improve overall sales efficiency. This step not only helps optimize the product listing order but also provides real-time feedback for subsequent product promotion, pricing, and sales strategies. By combining the output of the product selection and absorption model, the product's performance in the market can be effectively evaluated and dynamically optimized. Post-listing sales performance should be tracked in real time, collecting the following data:

[0136] Sales data: Sales volume is the most direct market feedback indicator, reflecting consumer acceptance of products and purchasing intentions. It's necessary to record daily or weekly sales figures in real time and compare them with historical sales data to see if expected targets have been met.

[0137] Click-through rate (CTR) and conversion rate: CTR represents the percentage of a product that is clicked after it is displayed, while conversion rate (CR) measures the percentage of a product that is ultimately purchased after a click. A high CTR but a low conversion rate may indicate a problem with the product's visual presentation or pricing, while a low CTR may reflect insufficient product appeal, potentially requiring optimization of product display or improved marketing.

[0138] Inventory changes: Inventory levels are directly related to sales volume. If inventory changes too quickly or too slowly, it may indicate problems with product pricing, promotional strategies, or inventory management.

[0139] The product selection and absorption capacity model provides each product with a market suitability score and potential sales capacity before it is listed. After listing, the model's output can be validated and adjusted by combining actual sales data and market response. By calculating the difference between the product's actual sales performance and the market performance predicted by the absorption capacity model, the model's predictive accuracy can be evaluated and poorly performing products can be identified. For example, suppose the absorption capacity model predicts a product's market attractiveness score of 85 points and expected sales of 1000 units. However, if actual sales are only 600 units, the product's market performance is significantly lower than expected, requiring further analysis. In this case, it can be analyzed whether the product failed to reach the expected sales volume due to excessively high pricing, insufficient promotional efforts, or the activities of market competitors.

[0140] Market adaptability score calculation logic: Select key features (such as historical sales volume of similar products, promotional efforts, user attention, platform traffic, etc.), normalize the historical sales volume of similar products, promotional efforts, user attention, and platform traffic, so that the value range of historical sales volume of similar products, promotional efforts, user attention, and platform traffic is mapped to [0.1]. Summate the normalized historical sales volume of similar products, promotional efforts, user attention, and platform traffic to obtain the market adaptability score.

[0141] Based on the market performance and the scores output by the adsorption model, products can be ranked according to their potential sales capacity, prioritizing those with higher sales potential for further promotion. For example, products with high adsorption model scores should have their exposure, promotion efforts, and advertising budgets increased to improve their sales performance. Meanwhile, for poorly performing products, their listing order may need to be reassessed. For instance, some products, even with high adsorption scores in the model, may need to be postponed or temporarily removed from the platform if their actual sales performance is unsatisfactory. Through this dynamic adjustment, the platform can ensure that products with the greatest market potential receive maximum sales opportunities. For products with unsatisfactory sales performance, adjustments need to be made in the following ways:

[0142] Price Adjustment: If product sales fall short of expectations and inventory buildup is severe, price reductions and promotions can be considered to stimulate demand. Price adjustments should be made in conjunction with market demand and the competitive landscape to avoid blindly lowering prices and damaging profit margins.

[0143] Promotion and marketing strategies: If a product is not selling well, it may be due to a lack of effective marketing. Increasing advertising efforts and combining them with promotional methods such as coupons and limited-time discounts can attract consumers' attention.

[0144] Inventory Adjustment: If products sell slowly and there is excess inventory, the platform can reduce inventory costs by decreasing the frequency of inventory replenishment and avoid slow-moving products occupying valuable storage space.

[0145] Product description optimization: In some cases, poor sales may be due to unappealing product descriptions or images. Optimizing product pages, updating with more attractive product images, or improving product copy can effectively increase consumer purchase intent.

[0146] By analyzing market reactions to products, the platform can adjust the order in which products are listed in real time. For example, during a promotional season, products with high sales potential can be listed earlier to ensure they receive more exposure. For poorly performing products, the platform can remove them or adjust their display position to avoid wasting promotional resources.

[0147] In addition, based on product performance, the platform can dynamically adjust how products are displayed, such as placing high-performing products on the homepage or at the forefront of relevant categories to increase their exposure; while products with lower sales can be recommended in a personalized way through recommendation engines or special pages to ensure that each product is displayed in front of the right consumers.

[0148] By establishing a real-time feedback mechanism, the platform can promptly capture market performance and sales data of products and make adaptive adjustments based on this feedback. As product market performance changes over time, the platform must continuously monitor and adjust to ensure that its product selection strategy always aligns with market demand. For example, a product may initially perform well after being listed, but sales may decline as competition intensifies or market demand changes. In this case, the platform can adjust its pricing and promotional strategies, or change its market positioning, based on real-time feedback, thereby optimizing the product's sales performance.

[0149] By analyzing and evaluating the market performance of products after they are listed, cross-border e-commerce platforms can adjust product pricing, inventory, promotion strategies, and listing order in real time to improve overall sales efficiency. Combined with the output of the product selection and absorption model, the platform can accurately identify products with poor market performance and make timely adjustments, thereby improving the market adaptability and sales potential of these products. This process ensures that the platform can flexibly respond to market changes, optimize resource allocation, and maximize profits.

[0150] S5: Dynamically adjust product pricing and promotion strategies

[0151] Based on the analysis of market response to the product, the discount level can be flexibly adjusted by simulating market dynamics (such as competitor pricing and market demand fluctuations). At this point, the product's attractiveness, inventory levels, and market demand must be considered to ensure that pricing and promotional activities maximize the product's sales potential, increase market share, and improve profitability.

[0152] In the operation of cross-border e-commerce platforms, product pricing and promotional strategies directly impact sales performance and market competitiveness. Based on market response to products, platforms need to simulate market dynamics (such as competitor pricing and market demand fluctuations) to flexibly adjust product discounts, pricing strategies, and promotional activities. This process requires considering not only product attractiveness, inventory levels, and market demand, but also ensuring that pricing and promotional activities maximize product sales potential and enhance the platform's market share and profitability. The following are the detailed steps for dynamically adjusting product pricing and promotional strategies.

[0153] First, it's necessary to collect market performance data after the product is listed, including but not limited to actual sales volume, conversion rate, and inventory changes. Simultaneously, it's also crucial to analyze external market factors, such as competitors' pricing strategies, industry trends, and fluctuations in consumer demand. This data provides the foundation for adjusting pricing and promotional strategies.

[0154] The platform needs to monitor competitors' pricing in real time, especially price fluctuations and the frequency and intensity of promotional activities. If a competitor significantly lowers prices within a certain period, the platform may need to adjust its own product prices or launch corresponding promotional activities to maintain competitiveness. Analyzing factors such as seasonal changes in market demand, holiday effects, and industry trends helps the platform predict market demand at different times. For example, certain products may experience higher demand during specific holidays or promotional seasons; in such cases, pricing and promotional efforts can be adjusted based on demand fluctuations.

[0155] Through market dynamics simulation, the platform can proactively evaluate product pricing and promotional strategies. This simulation process can be conducted using the following methods:

[0156] This study simulates price elasticity to understand the impact of price changes on sales volume. Specifically, when a product's price increases, its sales volume may decrease; conversely, a price decrease may stimulate sales growth. A regression model between price and sales volume is established to calculate the impact of price changes on sales volume. For products with high price elasticity, price changes have a significant impact on sales volume; while for products with low price elasticity, the change in sales volume is relatively limited.

[0157] Simulate competitor pricing behavior. Suppose a competitor significantly lowers prices over a certain period; the platform needs to assess whether this price reduction will affect its own product sales. If the competitor's price is significantly lower than its own, and consumers are price-sensitive, the platform may need to enhance the product's appeal by lowering prices or increasing promotional efforts.

[0158] Based on seasonality, holidays, and other factors, the platform simulates demand fluctuations for goods under different market conditions. It can use predictive models (such as time series analysis) to forecast market demand for the coming weeks or months, thereby adjusting pricing and promotional strategies accordingly.

[0159] Based on market simulation and demand analysis, the platform can flexibly adjust product pricing strategies according to the following aspects:

[0160] Based on the competitive landscape and market demand, platforms can set different discount levels for products. For example, if a product performs poorly during periods of intense competition, a higher discount can attract consumers; during peak demand periods, prices can be moderately increased to capitalize on the surge in market demand. For different product categories, platforms can adjust pricing ranges based on consumer purchasing preferences and price sensitivity. For instance, for high-end products, pricing strategies may focus more on brand premium; while for low-priced products, pricing strategies may focus more on competitive pricing and rapid response. Limited-time discounts and promotional activities (such as spending thresholds, free gifts, etc.) can effectively stimulate consumer purchasing desires. Platforms can use algorithms to calculate the impact of different promotional activities on sales, flexibly adjusting the timing and intensity of promotions to maximize product sales. Inventory levels are crucial for adjusting pricing and promotional strategies. Platforms need to monitor product inventory changes in real time and adjust pricing and promotional strategies accordingly.

[0161] When a product has high inventory and slow sales, price adjustments or increased promotional efforts may be necessary. Appropriate price reductions or increased promotional activities can accelerate inventory turnover and avoid cost pressures from overstocking. Conversely, if a product sells quickly but inventory is running low, price increases or reduced promotional efforts can control the sales speed and prevent sales disruptions or poor customer experience due to insufficient inventory.

[0162] Adjustments to pricing and promotional strategies are not static but rather a continuous optimization process. After adjusting product pricing and promotional strategies, the platform needs to monitor product sales data and market feedback in real time, including metrics such as sales volume changes, conversion rates, and inventory levels. This feedback data will help the platform evaluate the effectiveness of the adjusted strategies and make further optimizations in a timely manner.

[0163] First, analyze the relationship between price changes and sales volume changes based on historical sales data. Regression analysis (such as linear regression or multinomial regression) can typically be used to establish a price-sales regression model. The regression model calculates the impact on sales volume (i.e., price elasticity) when the product price decreases or increases. For example, if the regression results show that sales volume increases by 20% for every 10% decrease in the product price, it indicates that the product has high price elasticity and is suitable for stimulating sales through discounts.

[0164] The platform needs to monitor competitors' pricing strategies. For example, if a competitor significantly lowers the price of similar products, the platform needs to adjust its own pricing or increase discounts in a timely manner to counter the price war. This can be achieved by crawling competitor price data, comparing it in real time, and setting trigger conditions. For instance, if a major competitor's price is more than 10% lower than the platform's products, the platform can automatically trigger that product to participate in promotions or increase discounts.

[0165] Based on historical sales data, analyze the sales trends of goods across different seasons and holidays. Using time series analysis and other methods, predict changes in demand for goods during specific promotional seasons (such as Black Friday, Singles' Day, and the Christmas season). Based on simulation results, discounts can be appropriately reduced and profits increased during peak demand periods; while discounts can be increased to stimulate sales during off-peak seasons or when clearing inventory.

[0166] Based on the simulation results above, and considering the current market performance, competitive landscape, and inventory pressure of each product, a reasonable discount level can be set for each product. For example, if a product is in a low-demand season with high competition and high inventory, the platform can increase the discount (e.g., 30% or higher); while if it is in a peak sales season with low competition, the discount can be appropriately reduced (e.g., within 10%) to increase the profit per unit.

[0167] Suppose a cross-border e-commerce platform offers a smart speaker currently priced at $100. Based on historical data and market dynamics, the platform performs the following simulations and adjustments:

[0168] Regression modeling revealed that for every $10 price reduction, sales of the smart speaker increased by an average of 200 units.

[0169] Major competitors reduced the price of similar smart speakers to $90 during promotional periods, $10 less than this platform.

[0170] With the year-end shopping season approaching, the platform anticipates a 30% increase in market demand for smart speakers.

[0171] Considering the current high inventory levels, the platform's decision-makers have chosen to lower the product price to $88, with a limited-time 20% discount. They expect this to significantly increase sales, help clear inventory, and boost year-end market share.

[0172] In this approach, price adjustments and discount levels are derived from multi-dimensional market simulations and demand analysis, effectively balancing sales growth and market competitiveness. This avoids reliance solely on human experience, enabling dynamic and scientific pricing and promotions. Through the above process and examples, it's clear that dynamically adjusting discount levels is not a simple "gut feeling" decision, but a systematic decision-making process based on data analysis and dynamic market simulation. This method helps cross-border e-commerce platforms maintain price competitiveness and profitability in a complex and ever-changing market environment.

[0173] For example, if a product's sales increase after a pricing adjustment, but inventory remains tight, the platform may need to further strengthen inventory management or control sales growth by raising prices. On the other hand, if sales fail to increase significantly after the adjustment, the platform may need to re-evaluate the effectiveness of promotional activities or make minor price adjustments.

[0174] The intensity and frequency of promotional activities directly influence consumers' purchasing decisions. Platforms can flexibly adjust the intensity of promotions based on product sales and changes in the market environment: if a product sells poorly during a specific period, promotional activities can be strengthened (such as increasing discounts or extending the promotion period) to stimulate consumer purchasing desire. When product sales are stable or market demand is rising, platforms can appropriately reduce the intensity of promotional activities to ensure profit margins and avoid excessive discounts that could damage brand image.

[0175] Dynamically adjusting product pricing and promotional strategies is a crucial means for cross-border e-commerce platforms to maintain competitiveness and improve sales efficiency. By comprehensively analyzing market dynamics, actual product sales, competitor pricing strategies, and inventory levels, the platform can flexibly adjust product pricing and promotional efforts to maximize product sales potential in a changing market environment. Through a real-time feedback mechanism, the platform can continuously optimize pricing strategies and promotional activities, ensuring long-term stable profitability and market share growth.

[0176] S6: Optimize product listing order and marketing strategies

[0177] Based on the analysis of product sales data and market feedback, we further optimize the product listing order and marketing strategies. For products with excellent market performance, we increase their exposure and promotion efforts, and increase their display frequency on the platform; for products with poor performance, we adjust their listing order or their marketing strategies (such as repricing or repositioning). This dynamic adjustment ensures that the e-commerce platform remains competitive and maximizes market benefits.

[0178] The order in which products are listed and the marketing strategies employed directly impact an e-commerce platform's sales performance and market share. By analyzing product sales data and market feedback, e-commerce platforms can optimize their product listing order and marketing strategies accordingly. Specifically, for products with excellent market performance, their exposure and promotional efforts should be increased; while for poorly performing products, the listing order or marketing strategies need to be adjusted, such as repricing or repositioning. Through this dynamic adjustment, the platform can flexibly respond to changes in market demand, maintain its competitiveness, and maximize market benefits.

[0179] First, the platform needs to track and collect product sales data in real time, including sales volume, conversion rate, inventory, and promotional effectiveness. This data can be collected in real time through the e-commerce backend system, serving as the basis for optimizing product listing order and marketing strategies. Sales volume is the most direct market feedback, while conversion rate reflects the efficiency of converting product attractiveness into consumer purchase intent. By analyzing this data, it's possible to identify which products are performing exceptionally well and which are experiencing sales lag.

[0180] By analyzing product sales data and market feedback, the platform can determine the priority order for displaying different products. Specifically:

[0181] For products with strong sales and high conversion rates, the platform should prioritize displaying them in prominent positions on the homepage, category pages, and recommendation system. For example, if a mobile phone experiences a surge in sales and achieves its conversion rate target during a promotion, the platform can place the product in a featured position on the homepage or in the "Hot Selling" section to increase its exposure. For poorly performing products, their display frequency on the platform can be reduced by adjusting their listing order or position. For example, if a product has some sales but a low conversion rate and large inventory backlog, the platform can remove it from the platform, postpone its listing, or reduce its display frequency, thereby saving marketing costs and reducing invalid traffic.

[0182] After products are listed, optimizing marketing and promotion strategies is equally crucial. The platform needs to adjust these strategies in real time based on market performance, competitive landscape, and sales data.

[0183] For high-performing products, platforms can increase their exposure and appeal by increasing advertising budgets, boosting social media promotion, and running search engine ads. Furthermore, leveraging personalized recommendations and dynamic pricing technologies to launch customized marketing strategies for different user groups can further increase sales. For products with poor sales, platforms may need to reassess their pricing strategies. If a product is priced too high, appropriate price reductions, discounts, or limited-time promotions can stimulate consumer purchasing desire. Additionally, prices can be flexibly adjusted based on market demand fluctuations and competitor price changes. If a product performs poorly in the market, it may be due to unclear market positioning or an unappealing product description. Platforms can analyze consumer needs, adjust product positioning and descriptions, and optimize product page images and copy to enhance their appeal. For example, for a skincare product, the platform can adjust its efficacy descriptions or highlight its target age groups based on user feedback.

[0184] To ensure continuous optimization of product listing order and marketing strategies, the platform needs to establish a dynamic adjustment mechanism. By continuously monitoring changes in product sales data, conversion rates, and competitors, the platform can make real-time adjustments based on market changes.

[0185] For products currently on promotion, the platform can adjust the promotional intensity and duration based on real-time sales data. If a product experiences low sales at the beginning of a promotion, the platform can increase the promotional intensity or extend the promotional period; conversely, it can end the promotion early if sales are strong. The platform can use A / B testing to experiment with different product listing orders, pricing strategies, and promotional activities, comparing the effectiveness of different strategies and optimizing subsequent product promotion strategies accordingly. For example, for the same product, the platform can test different product titles, images, and descriptions, and adjust the display strategy based on user click-through rates and purchase rates.

[0186] Optimizing product listing order and marketing strategies is not just a short-term adjustment process; it requires planning in conjunction with long-term strategies. Platforms should regularly review the market performance of each product and adjust their marketing strategies based on long-term data feedback. By establishing a periodic review mechanism, platforms can better adapt to market changes and ensure the continued effectiveness of marketing strategies. For example, during specific off-seasons, some products may not be suitable for large-scale promotional activities. In this case, the platform can adjust the display position of these products, reduce advertising investment, and simultaneously optimize seasonality by adjusting the product mix. Optimizing product listing order and marketing strategies is a crucial step in improving e-commerce platform sales and market share. By analyzing product sales data and market feedback, platforms can identify high-performing products and increase their exposure and promotional efforts, while adjusting the listing order or marketing strategies of underperforming products. Dynamic adjustments and real-time feedback mechanisms enable platforms to flexibly respond to market changes, maximize product sales potential, and ensure the platform maintains its competitiveness in a highly competitive market.

[0187] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0188] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A machine learning-based method for dynamically adjusting self-operated product selection in cross-border e-commerce, characterized by: The adjustment method includes the following steps: S1: Collect historical sales data from cross-border e-commerce platforms, and acquire market environment data. Clean and standardize all collected data. S2: After integrating product attributes and market environment data, construct a feature set, analyze the feature set, and extract feature data related to product sales and market performance; S3: Based on the collected product attribute data and market environment characteristics, construct a product selection attraction model, and evaluate the sales attractiveness of different products in different market scenarios through multiple rounds of calculation and simulation. S4: After the products are listed, analyze the market response of each product, combine the product selection and absorption model output results to screen the products, adaptively adjust the products and determine the listing order. S5: Based on the analysis of the market response to the product, dynamically adjust the discount level of the product by simulating market dynamics; S6: Optimize the product listing order and marketing promotion strategies based on the analysis of product sales data and market feedback.

2. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning as described in claim 1, characterized in that: Based on the analysis of market response to the product, the discount level is dynamically adjusted by simulating market dynamics, including the following steps: Based on the impact of price changes on sales volume, price elasticity is simulated, and the impact of price changes on sales volume is calculated by establishing a regression model between price and sales volume. Simulate competitors' pricing behavior; Based on seasonal and holiday factors, simulate the fluctuations in demand for goods under different market environments; Based on market simulation and demand analysis, different discount levels are set for products according to the competitive landscape and market demand.

3. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning as described in claim 2, characterized in that: Based on the analysis of product sales data and market feedback, optimize the product listing order and marketing promotion strategies, including the following steps: Real-time tracking and collection of product sales data; analysis of product sales data and market feedback to determine the priority display order of different products. After a product is listed, its marketing and promotion strategies are adjusted in real time based on its market performance, competitive landscape, and sales data. By continuously monitoring product sales data, conversion rates, and changes in competitors, we can make real-time adjustments based on market changes. Regularly review the market performance of each product and adjust its marketing strategy based on long-term data feedback.

4. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning according to claim 3, characterized in that: After products are listed, the market response to each product is analyzed. Based on the product selection and absorption rate model output, products are screened, and adaptive adjustments are made to determine the listing order. This includes the following steps: The product selection and absorption model provides each product with a market adaptability score and potential sales capacity before it is listed. It obtains the market adaptability score by summing the normalized historical sales volume of similar products, promotional efforts, user attention, and platform traffic. Based on the market performance and the scores output by the absorption capacity model, the products are ranked according to their potential sales capacity.

5. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning according to claim 1, characterized in that: Based on the collected product attribute data and market environment characteristics, a product selection and attraction model is constructed, including the following steps: By combining product attributes with market environment data, a multi-dimensional input dataset is formed; The training dataset contains historical sales records, with each record including product attribute features, market environment features, and sales performance labels for that product. The feature variables are standardized to scale all input features to a uniform numerical range. For categorical features, one-hot encoding is used for numerical conversion. The data is input into the SVM model for training. After training, a product selection attraction model is built to analyze the sales attractiveness of new or existing products in different market environments.

6. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning as described in claim 5, characterized in that: Assessing the sales appeal of different products in different market contexts includes the following steps: For each product to be evaluated, the product attribute features and current market environment features are input into the pre-trained SVM model, and the output is the probability that the product belongs to the category of high sales attractiveness or low sales attractiveness.

7. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning according to claim 1, characterized in that: After integrating product attributes with market environment data, a feature set is constructed, including the following steps: Product attribute data includes brand, category, price, inventory, and sales channels. Market environment data is used to supplement the market background information of the product, including consumer behavior data, competitive landscape, and market demand fluctuations. The acquired data on brands, categories, prices, inventory, sales channels, consumer behavior, competitive landscape, and market demand fluctuations will be used to construct a feature set.

8. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning according to claim 7, characterized in that: Analyze the feature set and extract feature data related to product sales and market performance, including the following steps: The data in the feature set is subjected to missing value imputation, standardization and normalization, multinomial feature construction and category encoding; By using tree-based models or L1-regularized linear models, features relevant to product sales can be automatically selected. After feature extraction and processing are completed, all product attribute features and market environment features will be integrated into a comprehensive feature set.

9. The method for dynamic adjustment of self-operated product selection in cross-border e-commerce based on machine learning according to claim 1, characterized in that: Collect historical sales data from cross-border e-commerce platforms, and simultaneously acquire market environment data. Historical sales data includes product sales volume, inventory levels, pricing strategies, and promotional activity information. Market environment data includes changes in consumer preferences, competitors' pricing strategies, industry trends, and seasonal changes.

10. A machine learning-based dynamic adjustment system for self-operated cross-border e-commerce products, used to implement the adjustment method described in any one of claims 1-9, characterized in that: It includes a data collection and processing module, a product analysis and adjustment module, and a dynamic optimization module; Data collection and processing module: Collects historical sales data from cross-border e-commerce platforms, and acquires market environment data. It cleans and standardizes all collected data, integrates product attributes and market environment data to construct feature sets, analyzes feature sets, and extracts feature data related to product sales and market performance. Product Analysis and Adjustment Module: Based on collected product attribute data and market environment characteristics, a product selection and absorption model is constructed. Through multiple rounds of calculation and simulation, the sales attractiveness of different products in different market scenarios is evaluated. After the products are put on the shelves, the market response of each product is analyzed. After filtering the products based on the output results of the product selection and absorption model, the products are adaptively adjusted and the shelf order is determined. Dynamic optimization module: Based on the analysis of market response to products, the module dynamically adjusts product discounts by simulating market dynamics and optimizes product listing order and marketing strategies based on the analysis of product sales data and market feedback.

Images