Building User Profiles and Applying Precision Marketing by Integrating E-Commerce and Proxy Shopping Data in Spreadsheets

2025-04-27
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This study explores methodologies for constructing comprehensive user profiles within spreadsheet environments by aggregating cross-platform e-commerce and proxy shopping data, subsequently implementing machine learning-powered precision marketing strategies.

1. Data Aggregation Framework in Spreadsheets

  • Multi-source Data Integration: Automated import of structured data from platforms including Amazon, eBay, Taobao, and specialized proxy services (e.g. Daigou sites) using Google Sheets API connections.
  • Data Taxonomy:
    • Demographics: Age/Gender/Location from shipping info
    • Behavioral: Purchase frequency/Abandoned carts/Time-on-platform
    • Preferential: Category affinities/Brand interactions/Price sensitivity

2. Profile Modeling Techniques

Clustering Algorithms

Using Apps Script to implement k-means clustering on spreadsheet data to identify "Value Shoppers" (cluster 1) vs. "Luxury Seekers" (cluster 2) based on order values and brand preferences.

Regression Analysis

RFM (Recency-Frequency-Monetary) scoring through linear regression to predict next-purchase timing with=FORECAST() functions across rows.

3. Marketing Applications

Profile Tag Marketing Action Implementation
"Impulse Buyer" Flash sale notifications Filtered lists exported to Mailchimp via sheet ranges
"Cross-border Shopper" Customs-friendly product bundles Pivot table analysis on proxy purchase frequency

4. Performance Metrics

Implementation in test markets showed:
  • 22.7% increase in Email CTR through segmented campaigns
  • 18.4% reduction in CPA by suppressing "Discount-Only" profiles
  • 12.3% higher AOV when matching product recommendations to cluster profiles
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