E-commerce platform cross-channel data fusion and analysis optimization method

Abstract

The invention discloses an e-commerce platform cross-channel data fusion and analysis optimization method. User behavior data, transaction data, commodity data and market information are collected in real time from multiple channels (such as official websites, mobile applications, social media and offline stores); performing standardization processing, abnormal value detection and missing value completion on the acquired data, and expanding small sample data by using a data enhancement technology to improve data quality; and encrypting the sensitive data. Through a self-attention mechanism and a deep learning method, correlation and features among different channel data can be accurately captured, so that a more comprehensive and efficient user portrait is generated. The analysis result after data fusion is more accurate, a reliable basis can be provided for platform decision making, the differential privacy technology and the encryption protocol are adopted, and the safety of user personal information and sensitive data is effectively guaranteed. The user trust of the platform can be improved, and the legal compliance of the platform in multiple regions can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet e-commerce, specifically an e-commerce platform cross-channel data fusion and analysis optimization method. BACKGROUND

[0002] The e-commerce platform cross-channel data fusion and analysis optimization method integrates user behavior data, sales data, market feedback, and product information from different sales channels (such as websites, mobile applications, social media, offline stores, etc.), achieving unified analysis and application of multi-channel data. This method uses big data technology and machine learning algorithms to clean, match, and associate data from each channel, building a comprehensive user profile, accurately predicting user needs, and optimizing marketing strategies, inventory management, and product recommendations. Through cross-channel data fusion, e-commerce platforms can gain a more comprehensive understanding of users, improve personalized marketing effectiveness, enhance customer experience, and achieve sales and operational efficiency improvements. For example, platforms can dynamically adjust based on user behavior trajectories across different channels, enabling precise pricing, promotional activities, and cross-platform inventory allocation, further improving conversion rates and profits.

[0003] While cross-channel data fusion and analysis optimization methods for e-commerce platforms offer significant advantages in improving sales and customer experience, existing technologies still face the following shortcomings and challenges: Data quality issues: Cross-channel data often comes from diverse sources, involving multiple systems and platforms. Inconsistent data formats, quality, and update frequencies across different channels can lead to data redundancy, missing data, or errors. Ensuring high-quality data is a major challenge during data cleaning, preprocessing, and fusion. Inaccurate or incomplete data can affect subsequent analysis and decision-making, leading to strategic missteps. Data privacy and security issues: Cross-channel data fusion involves large amounts of user behavior data and personal information, potentially raising privacy and security concerns. Failure to implement strict privacy protection measures during data collection and processing can lead to legal risks, particularly compliance with Europe's GDPR (General Data Protection Regulation) and other regional privacy regulations. User data leaks or misuse can severely impact the platform's reputation and user trust. Data silos and channel fragmentation: Although e-commerce platforms have implemented cross-channel data fusion, in practice, data from different channels can still be fragmented and siloed, particularly between traditional offline and online channels. It's difficult to seamlessly integrate online shopping behavior with offline store sales, customer feedback, and other data, limiting data integrity and analytical effectiveness. Cross-channel data integration often requires high computing and storage requirements, as it often involves massive amounts of real-time data streams and accumulated historical data, requiring high-performance computing and storage infrastructure. This places high demands on the platform's technical architecture, especially with large user bases, which can lead to system performance bottlenecks, impacting the real-time and accuracy of data processing. Complex cross-channel data analysis and modeling require not only addressing the sheer volume of data but also the heterogeneity of data from various sources, formats, and dimensions. For example, social media data, purchase history data, and browsing behavior data have different data structures and semantics, making it challenging to unify this heterogeneous data and conduct effective analysis and modeling. Data from different channels often lacks consistency, complicating algorithm design and modeling. Poor real-time performance and latency: E-commerce platforms often experience time delays in the multi-channel data collection and analysis process, especially when processing cross-channel data. This latency can impact real-time decision-making. For functions that rely heavily on real-time data, such as promotions, inventory allocation, and personalized recommendations, latency can lead to missed marketing opportunities and reduced conversion rates. Cross-channel coordination and collaboration are difficult. Even after integrating data from multiple channels, ensuring coordination and consistency across them remains a challenge. For example, online channels may push a large number of advertisements, while offline stores may employ different promotional strategies. It's difficult to uniformly plan and coordinate these activities, leading to ineffective or ineffective cross-channel marketing strategies.Over-reliance on algorithms and models, e-commerce platforms often rely on complex machine learning and data analysis models when conducting cross-channel data analysis. However, the accuracy and effectiveness of these models require a large amount of historical data to train, and if the data itself has problems or the model is not updated and optimized in time, it may lead to biased decisions. In addition, over-reliance on algorithms may lead to "black box" decision-making, making it difficult to intuitively explain and understand some important business decisions.

[0004] To this end, we propose an e-commerce platform cross-channel data fusion and analysis optimization method. SUMMARY

[0005] To achieve the above purpose, the present application provides the following technical scheme: an e-commerce platform cross-channel data fusion and analysis optimization method, comprising the following steps:

[0006] Data collection and preprocessing:

[0007] Real-time collection of user behavior data, transaction data, product data and market information from multiple channels (such as official website, mobile application, social media, offline store, etc.);

[0008] Standardize the collected data, detect outliers, complete missing values, and use data augmentation techniques to expand small sample data to improve data quality;

[0009] Sensitive data is encrypted to ensure the security of data transmission and storage.

[0010] Multi-modal data fusion and unified modeling:

[0011] Use multi-modal data fusion algorithm to efficiently fuse data from different channels and build a unified data representation;

[0012] Use deep neural networks and other machine learning algorithms to model the fused data and generate accurate user profiles and behavior predictions;

[0013] Use self-attention mechanism for feature selection to reduce redundant information and improve data fusion effectiveness.

[0014] Real-time data stream processing and incremental learning:

[0015] Based on technologies such as Apache Kafka and Apache Flink, build a real-time data stream processing architecture to process, clean and fuse real-time data, ensuring the timeliness of analysis;

[0016] Use incremental learning algorithms to dynamically update analysis models based on real-time data, such as online K-means clustering and online random forest methods, for real-time prediction and recommendation optimization.

[0017] Privacy protection and compliance:

[0018] In the data fusion process, differential privacy technology is adopted to ensure the privacy of user data and prevent data leakage and misuse;

[0019] Through encryption transmission protocol (such as SSL / TLS), the security of data in the transmission process is ensured, and the requirements of GDPR and other data protection regulations are met.

[0020] Intelligent decision-making and optimization strategy:

[0021] Based on the fusion data and real-time analysis results, reinforcement learning is used to optimize the marketing strategy, inventory management, and advertising of e-commerce platforms, etc.

[0022] Q-learning and other reinforcement learning algorithms are used for multi-objective optimization, and product pricing, advertising investment, and promotion activities are dynamically adjusted through user behavior feedback to improve the profitability and user experience of the platform.

[0023] Preferably, the multi-modal data fusion algorithm uses a deep learning-based self-attention mechanism to calculate the correlation between different data sources and generate efficient data fusion representations.

[0024] Preferably, the incremental learning algorithm is a K-means clustering algorithm based on online learning, which can update the clustering model in real time according to new user behavior data and provide personalized product recommendations.

[0025] Preferably, the real-time data stream processing architecture is based on Apache Kafka and Apache Flink, which realizes low-latency and high-throughput data stream processing and fusion.

[0026] Preferably, the reinforcement learning optimization strategy uses Q-Learning algorithm to adjust the platform marketing and resource allocation strategy through feedback loop to maximize global revenue.

[0027] Compared with the prior art, the present application provides an e-commerce platform cross-channel data fusion and analysis optimization method, which has the following advantages:

[0028] 1、The e-commerce platform cross-channel data fusion and analysis optimization method can accurately capture the correlation and features between different channel data through self-attention mechanism and deep learning method, thereby generating more comprehensive and efficient user portraits. The analysis results after data fusion are more accurate, which can provide reliable basis for platform decision-making. The use of differential privacy technology and encryption protocol effectively safeguards the security of user personal information and sensitive data. This not only helps to improve the user trust of the platform, but also ensures the legal compliance of the platform in multiple regions (such as GDPR in the European Union), reducing legal risks.

[0029] 2、The e-commerce platform cross-channel data fusion and analysis optimization method enables the platform to quickly respond to changes in a highly dynamic market environment through real-time data stream processing architecture and incremental learning methods. The ability to analyze and update models in real time enables the platform to more flexibly adjust pricing, personalized recommendations, and marketing optimization.

[0030] 3、The e-commerce platform cross-channel data fusion and analysis optimization method introduces reinforcement learning for intelligent decision optimization, enabling the platform to continuously optimize marketing strategies and resource allocation based on multi-party feedback, improving the efficiency and effectiveness of the platform in advertising, promotional activities, and inventory management. Through real-time learning and feedback mechanisms, the system can accurately adjust and optimize various strategies to maximize profits.

[0031] 4、The e-commerce platform cross-channel data fusion and analysis optimization method can eliminate data barriers between different channels and help the platform achieve cross-channel coordinated operation through efficient data fusion and modeling methods. This means that the platform can push personalized recommendations, pricing adjustments, and promotional information across channels based on a unified user profile, improving user experience and conversion rates. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described below in a clear and complete manner. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0033] EMBODIMENT

[0034] Embodiment of e-commerce platform cross-channel data fusion and analysis optimization method

[0035] The e-commerce platform cross-channel data fusion and analysis optimization method comprises the following steps:

[0036] Data collection and preprocessing:

[0037] Real-time collection of user behavior data, transaction data, product data, and market information from multiple channels (such as official website, mobile application, social media, offline stores, etc.);

[0038] Standardize the collected data, detect outliers, complete missing values, and use data augmentation techniques to expand small sample data to improve data quality;

[0039] Sensitive data is encrypted to ensure the security of data transmission and storage.

[0040] Multi-modal data fusion and unified modeling:

[0041] Using multi-modal data fusion algorithms, data from different channels is efficiently fused to construct a unified data representation.

[0042] Using machine learning algorithms such as deep neural networks, the fused data is modeled to generate accurate user profiles and behavior predictions.

[0043] Using self-attention mechanisms for feature selection, redundant information is reduced, and the effectiveness of data fusion is improved.

[0044] Real-time data stream processing and incremental learning:

[0045] Based on technologies such as Apache Kafka and Apache Flink, a real-time data stream processing architecture is constructed to process, clean, and fuse real-time data, ensuring the timeliness of analysis.

[0046] Using incremental learning algorithms, such as online K-means clustering and online random forests, the analysis model is dynamically updated based on real-time data for real-time prediction and recommendation optimization.

[0047] Privacy protection and compliance:

[0048] In the data fusion process, differential privacy technology is used to ensure user data privacy and prevent data leakage and misuse.

[0049] Through encryption transmission protocols such as SSL / TLS, data security during transmission is ensured, and compliance with data protection regulations such as GDPR is achieved.

[0050] Intelligent decision-making and optimization strategy:

[0051] Based on fused data and real-time analysis results, reinforcement learning is used to optimize e-commerce platform marketing strategies, inventory management, and ad placement.

[0052] Using reinforcement learning algorithms such as Q-learning for multi-objective optimization, product pricing, ad spending, and promotional activities are dynamically adjusted based on user behavior feedback to improve platform profitability and user experience.

[0053] Specifically, the multi-modal data fusion algorithm uses a deep learning-based self-attention mechanism to calculate the correlation between different data sources and generate an efficient data fusion representation.

[0054] Specifically, the incremental learning algorithm is an online learning-based K-means clustering algorithm that can update the clustering model in real time based on new user behavior data, providing personalized product recommendations.

[0055] Specifically, the real-time data stream processing architecture is built based on Apache Kafka and Apache Flink, achieving low-latency and high-throughput data stream processing and fusion.

[0056] Specifically, the reinforcement learning optimization strategy uses the Q-Learning algorithm to adjust the platform marketing and resource allocation strategies through a feedback loop, achieving global revenue maximization.

[0057] Through the above technical solutions, in the present application, through the self-attention mechanism and deep learning method, the correlation and characteristics between different channel data can be accurately captured, so as to generate a more comprehensive and efficient user portrait. The analysis result after data fusion is more accurate, which can provide reliable basis for platform decision-making. The differential privacy technology and encryption protocol are adopted to effectively protect the safety of user personal information and sensitive data. This not only helps to improve the user trust of the platform, but also ensures the legal compliance of the platform in multiple regions (such as the GDPR of the European Union), reduces the legal risk. Through the real-time data stream processing architecture and the incremental learning method, the platform can quickly respond to changes in the highly dynamic market environment. The ability of real-time analysis and real-time updating of the model enables the platform to more flexibly adjust the pricing, personalized recommendation and marketing optimization. The introduction of reinforcement learning for intelligent decision optimization enables the platform to continuously optimize marketing strategies and resource allocation based on multi-party feedback, improving the efficiency and effectiveness of the platform in advertising, promotion activities and inventory management. Through real-time learning and feedback mechanism, the system can accurately adjust and optimize various strategies to maximize profits. Through efficient data fusion and modeling methods, the present application can eliminate the data gap between different channels and help the platform achieve cross-channel collaborative operation. This means that the platform can push personalized recommendations, pricing adjustments and promotion information across channels based on a unified user portrait, thereby improving user experience and conversion rate, attraction and stickiness. At the same time, accurate recommendations can also improve purchase conversion rates and increase platform sales revenue.

[0058] Data acquisition and fusion

[0059] Data acquisition:

[0060] The e-commerce platform collects user behavior data in real time through data interfaces with online malls, mobile applications, social media (such as Weibo, WeChat, Facebook, etc.) and offline stores. Data types include browsing records, click records, search keywords, product reviews, etc.

[0061] The data acquisition system pre-processes and standardizes the raw data according to the data format of different channels.

[0062] Data fusion and modeling:

[0063] Fusion of different channel data using self-attention mechanism. For example, combine purchase history from online mall with interaction data on social platforms to generate a unified user profile.

[0064] Model training through deep neural networks to build precise user behavior prediction models, predicting users' potential needs and purchase tendencies.

[0065] Privacy protection:

[0066] During data transmission and storage, use SSL / TLS encryption protocol to ensure data security during transmission. At the same time, user personal information and sensitive data are processed through differential privacy technology to prevent data leakage.

[0067] Real-time data stream processing and incremental learning

[0068] Real-time data stream processing:

[0069] Use Apache Kafka to process real-time data streams from multiple channels, ensuring seamless integration between different systems.

[0070] Use Apache Flink for real-time data stream processing, combined with incremental learning algorithms for data cleaning, denoising, and fusion, and real-time push to decision-making systems.

[0071] Incremental learning and model updating:

[0072] Use online learning algorithms based on K-means clustering to update user clustering models in real time. According to new user behavior data, dynamically adjust user groups for personalized recommendations.

[0073] Reinforcement learning and strategy optimization

[0074] Reinforcement learning optimization:

[0075] Through reinforcement learning algorithms such as Q-learning, the platform can continuously adjust the recommendation strategy based on user historical behavior, ad clicks, purchase feedback, etc.

[0076] The system updates the strategy in real time according to user interaction behavior, such as conducting promotional activities at specific time periods or dynamically adjusting product pricing according to market demand.

[0077] Intelligent decision-making:

[0078] Based on real-time data feedback and user behavior data on the platform, automatically optimize advertising, promotional activities, inventory management, etc. to improve the overall efficiency of platform operation.

[0079] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A cross-channel data fusion and analysis optimization method for an e-commerce platform, characterized by: The following steps are involved: Data collection and preprocessing: Collect user behavior data, transaction data, product data, and market information in real time from multiple channels (such as official websites, mobile applications, social media, offline stores, etc.); Standardize the collected data, detect outliers, fill in missing values, and use data enhancement technology to expand small sample data to improve data quality; Encrypt sensitive data to ensure the security of data transmission and storage. Multimodal data fusion and unified modeling: Adopt multimodal data fusion algorithms to efficiently fuse data from different channels and build a unified data representation; Use machine learning algorithms such as deep neural networks to model the fused data and generate accurate user profiles and behavior predictions; Use the self-attention mechanism for feature selection to reduce redundant information and improve the effect of data fusion. Real-time data stream processing and incremental learning: Based on technologies such as Apache Kafka and Apache Flink, we build a real-time data stream processing architecture to process, cleanse, and integrate real-time data to ensure timely analysis. Use incremental learning algorithms to dynamically update analysis models based on real-time data, such as online K-means clustering, online random forest, and other methods for real-time prediction and recommendation optimization. Privacy Protection and Compliance: During the data fusion process, differential privacy technology is used to ensure the privacy of user data and prevent data leakage and abuse; Encrypted transmission protocols (such as SSL / TLS) are used to ensure data security during transmission and comply with data protection regulations such as GDPR. Intelligent decision-making and optimization strategies: Based on integrated data and real-time analysis results, reinforcement learning is used to optimize the marketing strategy, inventory management, advertising placement, etc. of e-commerce platforms; We use reinforcement learning algorithms such as Q-learning for multi-objective optimization, and dynamically adjust product pricing, advertising investment, and promotional activities through user behavior feedback to improve the platform's profitability and user experience.

2. The cross-channel data fusion and analysis optimization method for an e-commerce platform according to claim 1, characterized in that: The multimodal data fusion algorithm adopts a self-attention mechanism based on deep learning to generate efficient data fusion representation by calculating the correlation between different data sources.

3. According to the cross-channel data fusion and analysis optimization method of the e-commerce platform in claim 1, the incremental learning algorithm is a K-means clustering algorithm based on online learning, which can update the clustering model in real time according to new user behavior data and provide personalized product recommendations.

4. The cross-channel data fusion and analysis optimization method for an e-commerce platform according to claim 1, characterized in that: The real-time data stream processing architecture is built on Apache Kafka and Apache Flink to achieve low-latency, high-throughput data stream processing and fusion.

5. The cross-channel data fusion and analysis optimization method for an e-commerce platform according to claim 1 is characterized by: The reinforcement learning optimization strategy uses the Q-Learning algorithm to adjust the platform marketing and resource allocation strategies through feedback loops to maximize global benefits.