Research on User Profile Data Construction in Spreadsheets and Precision Marketing Applications for E-Commerce Platforms and Buying Agents
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:basic informationconsumption behaviorinterest preferences
- Spreadsheet Organization:Google Apps ScriptPower Query
Building User Profiles via Data Mining and Machine Learning
-
Preprocessing:
- Algorithm Application:Google Sheets' Simple MLPython embeddings.
- Label Generation:
Precision Marketing Applications
Case Study: Personalized Recommendations
Example: Users tagged as "Frequent Tech Buyers" receive promotions on new gadgets via email campaigns auto-generated from spreadsheet-linked tools (e.g., Mailchimp).
Case Study: Targeted Ads
Iterate ad content (e.g., Facebook ads) by uploading segmented audience lists from spreadsheets, reducing CPA
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.