Building E-commerce and Shopping Agent Platform User Profiles in Spreadsheets for Precision Marketing Applications

2025-04-27

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Introduction

In the digital commerce era, integrating user data from multiple e-commerce platforms and shopping agent websites into spreadsheet applications

Data Collection Framework

Multi-Source Data Aggregation

Our spreadsheet-based system collects three primary data categories from e-marketplaces (Amazon, eBay, Taobao)shopping agent platforms (Superbuy, Bhiner):

  • Demographics: Age, location, gender, income level
  • Transaction Records: Purchase history, frequency, product categories
  • Behavioral Signals: Click patterns, browsing duration, wishlist items

The import/sync functions enable real-time data updates through platform APIs and web scraping tools configured directly within the spreadsheet environment.

User Profile Modeling Techniques

Data Mining Algorithms Implementation

Using spreadsheet-embedded script functions (Google Apps Script/Python macros), we implement:

  1. RFM analysis
  2. Purchase pattern cluster analysis
  3. Natural language processing of product review sentiment
Profile Tagging Example
User ID Value Segment Category Preference Price Sensitivity
U10042 High-value repeat Electronics Low (Premium focus)

Precision Marketing Applications

Automated Campaign Execution

The integrated system triggers marketing actions when specific user threshold conditions

  • Personalized recommendations
  • Multi-channel ad placement
  • Dynamic discount offers

Early implementations show 18-35% improvement

Implementation Case Example

A beauty brand using this approach archived:

42% higher campaign ROI

Conclusion

This spreadsheet-centric solution democratizes big consumer analytics for small/medium merchants, providing:

Cost-effective alternative to enterprise CDP systems
Flexible integration with existing marketing workflows
Visual data exploration through conditional formatting/native charts

*Results vary based on dataset quality and implementation details

© 2023 E-Commerce Analytics Research Group

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