Calculation method for predicting product sales prospect based on cross-border e-commerce data

Abstract

The invention discloses a calculation method for predicting a product sales prospect based on cross-border e-commerce data, and the method comprises a data collection module which comprises a data collection system. The data acquisition system acquires multi-dimensional data of historical sales volume, inventory conditions, market trends and customer behaviors of commodities in real time from a cross-border e-commerce platform, social media and an industry report channel, and preliminarily screens and arranges the data collected by the data acquisition system to ensure the integrity and accuracy of the data; and the data preprocessing module adopts a data cleaning technology to remove noise data and fill missing values, performs data normalization and standardization processing, and provides a high-quality data basis for subsequent analysis. According to the invention, through comprehensive collection of multi-dimensional data and application of an advanced intelligent algorithm, accurate prediction of the product sales prospect is realized, the prediction precision is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-border e-commerce, and in particular to a calculation method for predicting product sales prospects based on cross-border e-commerce data. Background Art

[0002] Cross-border e-commerce, or cross-border electronic commerce, is the abbreviation of cross-border e-commerce. It refers to a new type of e-commerce application model in which trading entities belonging to different countries or regions realize various commodity trading activities through e-commerce platforms, and realize the flow of commodities from sellers to buyers and other related activities through cross-border logistics.

[0003] The main functions of cross-border e-commerce include: Expanding the market: Cross-border e-commerce enables merchants to sell goods all over the world, breaking through geographical restrictions and expanding the market. Reducing costs: Cross-border e-commerce conducts commodity transactions and logistics distribution through the Internet platform, avoiding the intermediate links and logistics costs in traditional trade, and reducing the operating costs of enterprises. Improving efficiency: Cross-border e-commerce platforms improve the operating efficiency and customer satisfaction of enterprises through automated order processing, payment and logistics distribution systems. Promoting cultural exchanges: Cross-border e-commerce promotes cultural exchanges and understanding between different countries and regions. Promoting economic development: Cross-border e-commerce increases international trade and investment and promotes the economic development of participating countries. Supporting cross-border payments: Cross-border e-commerce needs to support payment methods and currencies in different countries and regions so that consumers can complete transactions conveniently. Promoting globalization: As one of the important drivers of globalization, cross-border e-commerce makes the global economy more closely connected by eliminating geographical and temporal restrictions.

[0004] However, when the existing technology is actually used, with the rapid development of the cross-border e-commerce industry, sales forecasting plays a vital role in the daily operations of merchants. However, the existing forecasting methods have many shortcomings, such as relying on manual experience calculations that are prone to errors and inefficiency, time series predictions that have too high requirements for the stability of sales data, and machine learning forecasting methods that are prone to forecasting deviations due to uneven data distribution. In particular, the "small order and fast return" business model of cross-border e-commerce, where new products are updated quickly and have a short life cycle, makes it difficult for traditional forecasting methods to adapt. Summary of the invention

[0005] The purpose of the present invention is to provide a calculation method for predicting product sales prospects based on cross-border e-commerce data, so as to solve the problem raised in the above background technology that with the rapid development of the cross-border e-commerce industry, sales forecasting plays a vital role in the daily operations of merchants. However, the existing prediction methods have many shortcomings, such as relying on manual experience calculations that are prone to errors and inefficiency, time series predictions that have too high requirements for the stability of sales data, and machine learning prediction methods that are prone to prediction deviations due to uneven data distribution. In particular, the "small order and fast return" business model of cross-border e-commerce has a fast speed of new products and a short life cycle, making it difficult for traditional prediction methods to adapt to the problem.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Data collection module: The data collection module includes a data collection system, which obtains multi-dimensional data on historical sales volume, inventory status, market trends and customer behavior from cross-border e-commerce platforms, social media and industry reporting channels in real time, and preliminarily screens and organizes the data collected by the data collection system to ensure the integrity and accuracy of the data;

[0008] Data preprocessing module: The data preprocessing module uses data cleaning technology to remove noise data and fill missing values, and performs data normalization and standardization processing to provide a high-quality data foundation for subsequent analysis. It uses sentiment analysis and text mining technology to process unstructured data of customer reviews and social media discussions to extract valuable sentiment tendencies and opinion information;

[0009] Feature extraction and selection module: The feature extraction and selection module extracts feature variables that have a significant impact on sales forecasting from the data preprocessed by the data preprocessing module based on statistical principles and machine learning feature engineering. The feature variables include sales change trends, seasonal fluctuations, and target customer group characteristics;

[0010] Intelligent prediction model building module: The intelligent prediction model building module adopts a hybrid model combining an integrated learning algorithm and a deep learning algorithm to train the extracted feature variables and build an efficient sales prediction model. The integrated learning algorithm includes a random forest and a gradient boosting tree algorithm, and the deep learning algorithm includes an LSTM and a GRU algorithm.

[0011] Forecast result output and evaluation module: The forecast result output and evaluation module outputs the sales forecast result of the product in the future period of time, and the sales forecast result includes the predicted sales volume and the prediction error rate.

[0012] Preferably, the data collection system also covers the collection of market trend data, which includes seasonal changes, holiday impacts and competitor activities, and the market trend data is obtained from e-commerce platforms, social media and industry report channels through crawler technology.

[0013] Preferably, the data collection system also covers the collection of customer behavior data, which includes user browsing records, purchase history and evaluation feedback. The customer behavior data is obtained through the e-commerce platform's own user behavior analysis system to capture consumer preferences and purchasing habits.

[0014] Preferably, the data preprocessing module adopts a combination of automation and manual review during the data cleaning process to ensure the accuracy and consistency of the data, and reasonably fills or deletes missing values ​​according to data distribution and business logic, and normalizes and standardizes the data so that data of different dimensions and scales can be effectively compared and analyzed in the same model.

[0015] Preferably, the feature extraction and selection module introduces external variables, which include macroeconomic indicators and policy changes, as auxiliary input features of the model.

[0016] Preferably, the model design of the intelligent prediction model building module takes into account seasonal factors and holiday impacts, and ensures the accuracy and timeliness of the prediction results through an adaptive adjustment mechanism.

[0017] Preferably, the prediction result output and evaluation module provides a visual display of the prediction results, and the visual display includes a sales trend chart and a target customer group distribution chart, so that merchants can intuitively understand the prediction results.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. The present invention improves prediction accuracy: by comprehensively collecting multi-dimensional data and applying advanced intelligent algorithms, accurate prediction of product sales prospects can be achieved, prediction accuracy can be improved, and operating costs can be reduced: it helps merchants to reasonably arrange inventory and production plans, and reduce cost waste caused by inventory backlogs or out-of-stock situations; marketing strategies based on prediction results can be formulated to improve sales conversion rates;

[0020] 2. The present invention also improves market competitiveness: merchants can timely grasp market trends and changes in consumer demand, adjust product strategies and market positioning; accurate customer group analysis and personalized recommendations improve customer satisfaction and loyalty. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a schematic diagram of the overall flow of a calculation method for predicting product sales prospects based on cross-border e-commerce data. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 The present invention provides a technical solution: comprising:

[0024] Data acquisition module:

[0025] The data collection module includes a data collection system, which obtains multi-dimensional data on product historical sales, inventory status, market trends and customer behavior from cross-border e-commerce platforms, social media and industry reporting channels in real time, and performs preliminary screening and sorting of the data collected by the data collection system to ensure the integrity and accuracy of the data;

[0026] The data collection system also collects market trend data, including seasonal changes, holiday impacts, and competitor activities. Market trend data is obtained from e-commerce platforms, social media, and industry report channels through crawler technology;

[0027] The data collection system also covers the collection of customer behavior data, which includes user browsing records, purchase history and evaluation feedback. Customer behavior data is obtained through the e-commerce platform’s own user behavior analysis system and is used to capture consumer preferences and purchasing habits.

[0028] Data preprocessing module:

[0029] The data preprocessing module uses data cleaning technology to remove noise data and fill missing values, and performs data normalization and standardization to provide a high-quality data foundation for subsequent analysis. It uses sentiment analysis and text mining technology to process unstructured data of customer reviews and social media discussions to extract valuable sentiment tendencies and opinion information;

[0030] During the data cleaning process, the data preprocessing module adopts a combination of automation and manual review to ensure the accuracy and consistency of the data. For missing values, they are reasonably filled or deleted according to data distribution and business logic. Data is normalized and standardized so that data of different dimensions and scales can be effectively compared and analyzed in the same model.

[0031] Feature extraction and selection module:

[0032] The feature extraction and selection module is based on statistical principles and machine learning feature engineering. It extracts characteristic variables that have a significant impact on sales forecasting from the data preprocessed by the data preprocessing module. The characteristic variables include sales trend, seasonal fluctuations and target customer group characteristics.

[0033] The feature extraction and selection module introduces external variables, including macroeconomic indicators and policy changes, as auxiliary input features of the model.

[0034] Intelligent prediction model building blocks:

[0035] The intelligent prediction model construction module adopts a hybrid model that combines ensemble learning algorithms with deep learning algorithms to train the extracted feature variables and build an efficient sales prediction model. The ensemble learning algorithms include random forest and gradient boosting tree algorithms, and the deep learning algorithms include LSTM and GRU algorithms.

[0036] The model design of the intelligent prediction model building module takes into account seasonal factors and holiday impacts, and ensures the accuracy and timeliness of the prediction results through an adaptive adjustment mechanism.

[0037] Prediction result output and evaluation module:

[0038] The forecast result output and evaluation module outputs the sales forecast results of the product in the future period. The sales forecast results include the forecast sales volume and the forecast error rate.

[0039] The forecast result output and evaluation module provides a visual display of the forecast results, including sales trend charts and target customer group distribution charts, making it easier for merchants to intuitively understand the forecast results.

[0040] Example: Taking a smart phone electronic product on a cross-border e-commerce platform as an example, the implementation process of the present invention is described in detail:

[0041] S1. Data collection: The data collection module obtains historical sales data, user reviews, and purchase history of smartphones from e-commerce platforms, and collects market trends and competitor information from social media and industry reports;

[0042] S2, data preprocessing: clean, organize, normalize and standardize the data collected in S1 above, use sentiment analysis technology to process user reviews and extract sentiment tendencies;

[0043] S3, feature extraction: extract sales trend, seasonal fluctuations and target customer group characteristics, and introduce macroeconomic indicators as auxiliary input;

[0044] S4, model construction: Combine ensemble learning and deep learning algorithms to build a hybrid prediction model and train the features extracted in S3 above;

[0045] S5. Forecast output: Output the predicted sales volume and forecast error rate of smartphones in the next three months; provide additional information of sales trend chart and target customer group distribution chart.

[0046] The beneficial effects of the present invention include:

[0047] Improve forecasting accuracy: Through comprehensive collection of multi-dimensional data and application of advanced intelligent algorithms, accurate forecasting of product sales prospects can be achieved and forecasting accuracy can be improved;

[0048] Reduce operating costs: Help merchants arrange inventory and production plans reasonably, reduce cost waste caused by inventory backlog or out-of-stock; formulate marketing strategies based on forecast results to improve sales conversion rate;

[0049] Improve market competitiveness: Merchants can promptly grasp market trends and changes in consumer demand, and adjust product strategies and market positioning; accurate customer group analysis and personalized recommendations improve customer satisfaction and loyalty.

[0050] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A calculation method for predicting product sales prospects based on cross-border e-commerce data, characterized by: include: Data collection module: The data collection module includes a data collection system, which obtains multi-dimensional data on historical sales volume, inventory status, market trends and customer behavior from cross-border e-commerce platforms, social media and industry reporting channels in real time, and preliminarily screens and organizes the data collected by the data collection system to ensure the integrity and accuracy of the data; Data preprocessing module: The data preprocessing module uses data cleaning technology to remove noise data and fill missing values, and performs data normalization and standardization processing to provide a high-quality data foundation for subsequent analysis. It uses sentiment analysis and text mining technology to process unstructured data of customer reviews and social media discussions to extract valuable sentiment tendencies and opinion information; Feature extraction and selection module: The feature extraction and selection module extracts feature variables that have a significant impact on sales forecasting from the data preprocessed by the data preprocessing module based on statistical principles and machine learning feature engineering. The feature variables include sales change trends, seasonal fluctuations, and target customer group characteristics; Intelligent prediction model building module: The intelligent prediction model building module adopts a hybrid model combining an integrated learning algorithm and a deep learning algorithm to train the extracted feature variables and build an efficient sales prediction model. The integrated learning algorithm includes a random forest and a gradient boosting tree algorithm, and the deep learning algorithm includes an LSTM and a GRU algorithm. Forecast result output and evaluation module: The forecast result output and evaluation module outputs the sales forecast result of the product in the future period of time, and the sales forecast result includes the predicted sales volume and the prediction error rate.

2. The method for calculating product sales prospects based on cross-border e-commerce data according to claim 1, characterized in that: The data collection system also covers the collection of market trend data, which includes seasonal changes, holiday impacts and competitor activities. The market trend data is obtained from e-commerce platforms, social media and industry report channels through crawler technology.

3. The method for calculating product sales prospects based on cross-border e-commerce data according to claim 2, characterized in that: The data collection system also covers the collection of customer behavior data, which includes user browsing records, purchase history and evaluation feedback. The customer behavior data is obtained through the e-commerce platform’s own user behavior analysis system to capture consumer preferences and purchasing habits.

4. The method for calculating product sales prospects based on cross-border e-commerce data according to claim 3, characterized in that: During the data cleaning process, the data preprocessing module adopts a combination of automation and manual review to ensure the accuracy and consistency of the data. For missing values, it reasonably fills or deletes them according to data distribution and business logic, and normalizes and standardizes the data so that data of different dimensions and scales can be effectively compared and analyzed in the same model.

5. The method for calculating product sales prospects based on cross-border e-commerce data according to claim 4, characterized in that: The feature extraction and selection module introduces external variables, including macroeconomic indicators and policy changes, as auxiliary input features of the model.

6. The method for calculating product sales prospects based on cross-border e-commerce data according to claim 5, characterized in that: The model design of the intelligent prediction model building module takes into account seasonal factors and holiday impacts, and ensures the accuracy and timeliness of the prediction results through an adaptive adjustment mechanism.

7. The method for calculating product sales prospects based on cross-border e-commerce data according to claim 6, characterized in that: The prediction result output and evaluation module provides a visual display of the prediction results, and the visual display includes a sales trend chart and a target customer group distribution chart, so that merchants can intuitively understand the prediction results.

Images