Research on Construction and Precision Marketing Application of E-commerce Platforms and Reseller Website User Portrait Data in Spreadsheets
Introduction
With the rapid development of e-commerce platforms and cross-border reseller websites, data-driven marketing has become a key strategy for business growth. This study explores a framework for consolidating diverse user data across multiple platforms within spreadsheets and applying machine learning algorithms to build dynamic user portrait models for precision marketing.
Methodology
1. Data Integration in Spreadsheets
A Python-scripted spreadsheet system aggregates:
- Basic Information:
- Consumption Behavior:
- Interaction Data:
- 3rd-party Data:
- Consumption Behavior:
2. User Portrait Modeling
The system applies:
- Clustering Analysis (K-means):
- Association Rule Learning:
- RFM Model:
- Association Rule Learning:
Applications
A. Personalized Recommendations
For luxury reseller customers with CLV scores > $5k, the system recommends limited-edition products 48 hours earlier than general users, achieving 23% higher conversion.
B. Dynamic Ad Targeting
Tiered ad groups created based on shopping cart abandonment patterns show conversion improvements:
| User Segment | CTR Increase | ROAS |
|---|---|---|
| Impulse buyers | 62% | 5.8x |
| Comparison shoppers | 38% | 4.2x |
Findings
The integrated spreadsheet system reduced customer acquisition costs by 42%19%
Ongoing challenges include real-time data synchronization and privacy-compliant data sharing between platforms.