Constructing User Profiles in Spreadsheets via E-commerce and Shopping Agent Platforms Data for Precision Marketing Applications
Abstract
This study explores the methodology for integrating user data from major e-commerce platforms and overseas shopping agent websites into spreadsheet formats (e.g., Excel, Google Sheets) to construct comprehensive customer profiles. By combining basic demographics, purchasing behaviors, and interest preferences with data mining techniques, we develop machine learning-powered user portrait models that enable targeted marketing strategies.
1. Data Integration Framework
- Multi-platform Data Aggregation: Techniques for collecting standardized user data (age, gender, location) and behavioral metrics (browsing patterns, purchase history, cart abandonment rates) across platforms like Amazon, Taobao, and shopping agent services
- Spreadsheet Data Structuring: Optimal column arrangements for in-house data warehousing with fields for RFM (Recency, Frequency, Monetary) analysis, product category preferences, and seasonal buying trends
- Third-party API Integration: Formulas and script solutions (Google Apps Script/Python) for automating data pulls from platform APIs into spreadsheet formats
| UserID | Demographics | Purchase Frequency | Preferred Categories | CLV Score |
|---|---|---|---|---|
| UA20394 | F, 25-34, Tier1 | 4.2/mo | Skincare, Luxury | 8.7 |
2. Machine Learning Implementation
- Predictive Modeling:
Leveraging spreadsheet-integrated ML tools (Google Sheets' BigQuery ML, Python libraries via Colab) to cluster users based on behavioral patterns and predict:
- Next purchase timing
- Price sensitivity thresholds
- Cross-sell potential
- Tag Generation System:
Automated labeling through natural language processing of search queries and review sentiments, outputting tags like:
"premium-seeker","discount-sensitive","impulse-buyer".
3. Precision Marketing Applications
3.1 Personalized Recommendations
Implementing spreadsheet-based recommendation engines using collaborative filtering formulas against user similarity matrices maintained in tabular format.
3.2 Dynamic Ad Targeting
Exporting user segment scores (0-100 propensity indexes for product categories) to advertising platforms through CSV pipelines updated hourly.
Results:
4. Conclusion
Through standardized spreadsheet architectures acting as centralized customer data platforms (CDPs), marketers can achieve enterprise-grade segmentation without specialized CRM systems. The methodology proves particularly valuable for cross-border e-commerce scenarios where data fragmentation across shopping agent platforms typically impedes unified analysis.