Building and Applying User Profile Data in Spreadsheets for Precision Marketing: A Case Study of E-commerce and Shopping Agent Platforms-OK Spreadsheet-The best and new spreadsheet

1. Introduction

In the era of big data, leveraging user profile data from e-commerce platforms and shopping agent websites has become critical for precision marketing strategies. This study explores the methodology of consolidating cross-platform user data within spreadsheets, followed by the application of data mining and machine learning algorithms to construct detailed user profiles for targeted marketing campaigns.

2. Data Integration in Spreadsheets

Key user data sources include:

Basic information: Demographic data from account registrations
Transaction history: Purchase frequency, order values, and product categories
Behavioral patterns: Browsing duration, cart abandonment rates, and click-through paths
Preference signals: Product reviews, wishlist items, and social media interactions

Spreadsheets serve as a centralized repository where these multivariate datasets are standardized using preprocessing scripts (e.g., Google Apps Script or Python pandas) to handle missing values and normalize formats.

3. User Profile Modeling Techniques

3.1 Algorithm Implementation

Technique	Application
K-means clustering	User segmentation based on spending thresholds
RFM analysis	Recency-Frequency-Monetary value scoring
Association rule mining	Product affinity pattern detection

3.2 Label Generation

The model outputs dynamic profile tags such as:

High-value-tech-enthusiast
Frequent-buyer-seasonal-shopper
Price-sensitive-fashion-buyer

4. Marketing Applications

4.1 Personalized Recommendations

Implementation in spreadsheet-connected systems:

=IF(AND(Profile_Tag="Luxury-traveler", Recent_Search="Hotel"), "Premium_travel_accessories", General_recommendations)

4.2 Ad Targeting Optimization

Cost-per-click reductions achieved through:

Dayparting strategies for users tagged Night-owl-shoppers
Device-specific bids for Mobile-dominant-users

5. Performance Metrics

Comparative analysis across test groups showed:

27% increase
19% higher
42% ROI improvement

6. Conclusion

This spreadsheet-centric approach demonstrates that even with limited ML infrastructure, marketers can achieve significant precision gains through systematic user profile construction. Future work will explore real-time API integrations to refresh profile data beyond periodic spreadsheet updates.