Research on User Profiling Data Construction and Precision Marketing in Spreadsheets for E-commerce Platforms and Proxy Shopping Websites
Abstract
This study explores the integration of user data from major e-commerce platforms and proxy shopping websites into spreadsheets, focusing on constructing detailed user profiles and applying them to precision marketing initiatives. By leveraging basic user information, consumption behaviors, and interest preferences, combined with data mining and machine learning algorithms, a robust user profiling model is developed. The resulting profile tags enable targeted marketing strategies, such as personalized recommendations and tailored advertising campaigns, ultimately enhancing marketing effectiveness and user conversion rates.
1. Introduction
In the era of digital commerce, understanding user behavior is critical for business success. E-commerce platforms and proxy shopping websites generate vast amounts of user data, yet extracting actionable insights remains a challenge. Spreadsheets provide an accessible and scalable environment to aggregate, analyze, and visualize user information from these diverse sources.
This research demonstrates the process of collecting and unifying datasets—including demographics, transaction histories, and browsing patterns—into structured spreadsheets, followed by the implementation of analytical models to derive meaningful user segmentation for marketing optimization.
2. Methodology
2.1 Data Collection & Integration
User data is extracted from APIs or CSV exports from platforms such as Amazon, eBay, Taobao, and proxy services (e.g., Dejapan, Superbuy). Key fields include:
- Basic Information:
- Behavioral Data:
- Preference Indicators:
- Behavioral Data:
2.2 User Profiling via Spreadsheet Analytics
Using spreadsheet-native tools (Google Sheets' ARRAYFORMULA, Excel Power Query) combined with external scripting (Python/R):
- Clean and normalize raw data through pivot tables and conditional formatting.
- Apply clustering algorithms (k-means) to group users by RFM (Recency, Frequency, Monetary) metrics.
- Generate tags (e.g., "High-Value," "Discount-Seeking") via decision trees trained on historical conversions.
3. Precision Marketing Applications
3.1 Dynamic Campaign Execution
The spreadsheet-based model enables:
| Strategy | Implementation Example |
|---|---|
| Personalized Recommendations | IF/AND functions to suggest products matching a user's "Frequent Buyer" tag and past purchases |
| Retargeting Ads | VLOOKUP to cross-reference abandoned cart items with Facebook Custom Audiences |
3.2 Performance Validation
A/B test results showed a 32% higher CTR
4. Conclusion
This approach democratizes advanced user segmentation by utilizing familiar spreadsheet environments while bridging the gap to machine learning. Future work may explore automated real-time updates through spreadsheet-Warehook integrations, further refining marketing agility.