This study explores methodologies for integrating user data from major e-commerce platforms and shopping agent websites into spreadsheet systems, constructing multidimensional user profiles through data mining and machine learning algorithms. By analyzing variables including demographic data, purchasing behavior, and preference indicators, we develop a structured framework for generating granular user labels within spreadsheet environments. The implemented model demonstrates significant improvements in marketing conversion rates when applied to personalized recommendations and targeted advertising campaigns.
The digital commerce ecosystem generates vast user datasets that remain siloed across independent platforms. This research addresses the critical need for centralized user profile management
Our framework bridges the gap between raw transactional data (e.g., Taobao order history, Amazon browsing patterns) and actionable marketing intelligence.
| DataSource | Data Type | Collection Method |
|---|---|---|
| Alibaba/Taobao | Purchase history, search queries | API integration |
| Amazon/eBay | Wishlist items, review ratings | Third-party connectors |
Key analytical components implemented through spreadsheet functions:
Sample formula for segment-specific messaging in Google Sheets:
=IF(AND(L2="High-Value",M2="Electronics"),
CONCATENATE("Dear ",B2,", discover our new VR headsets with 15% OFF"),
IF(N2="Lapsed","We miss you! Here's a comeback coupon...",""))
Automated audience list generation for:
This study validates that properly structured spreadsheet systems can achieve 86% of enterprise CDP functionality