Introduction

With the rapid growth of e-commerce and cross-border purchasing services, effectively analyzing and utilizing user data has become crucial for businesses. This study explores the methodology of integrating user data from major e-commerce platforms and buying agent websites into spreadsheets to build comprehensive user profiles, which can then be applied in precision marketing strategies such as personalized recommendations and targeted advertising.

Data Collection and Integration in Spreadsheets

• Data Sources: User data is gathered from platforms like Amazon, Taobao, eBay, and popular buying agents such as Superbuy or Pandabuy. This includes basic information (age, gender, location), consumption behavior (purchase history, average spend), and interest preferences (browsing patterns, wishlist items).
• Spreadsheet Organization: Structured columns in spreadsheets (e.g., Google Sheets or Excel) are used to categorize data fields (demographics, transactions, engagements). Advanced tools like Google Apps Script or Power Query automate data integration from APIs or CSV exports.

Building User Profiles via Data Mining and Machine Learning

1. Preprocessing: Clean and normalize data (e.g., handling missing values, standardizing units) using spreadsheet functions or Python/R scripts.
2. Algorithm Application: Apply clustering (e.g., K-means for segmentation) or classification algorithms (e.g., decision trees for predicting preferences) via integrated tools like Google Sheets' Simple ML or Python embeddings.
3. Label Generation: Assign dynamic tags (e.g., "Budget Shopper," "Luxury Seeker") based on behavior patterns and update them regularly.

Precision Marketing Applications

Conclusion

Integrating fragmented user data into spreadsheets enables efficient profile modeling and actionable marketing insights. Future work may explore real-time profile updates via cloud-based spreadsheet dashboards.