Constructing User Personas from E-Commerce and Shopping Agent Platform Data in Spreadsheets for Precision Marketing-Pandabuy Spreadsheet-The best spreadsheet

Abstract

This study explores a spreadsheet-based methodology for integrating cross-platform e-commerce user data (including Taobao, Amazon, eBay, and shopping agent platforms) to construct multidimensional user personas. By combining data mining techniques with Spreadsheet-native machine learning functions, we demonstrate an accessible approach to precision marketing without specialized software.

1. Data Collection Frameworks

1.1 Unified Data Structure

Standardized columns across platforms:

Demographics:
Behavioral:
Transactional:
Temporal:

1.2 Cross-Platform Integration

Using IMPORTXMLQUERY
=QUERY({IMPORTXML("taobao_data","//user");IMPORTXML("amazon_data","//user")}, "SELECT Col1,Col2 WHERE Col3 3")

2. Persona Modeling Techniques 2.1 Cluster Analysis in Spreadsheets Four-step methodology with native functions: Normalization: =(A2-MIN(A:A))/(MAX(A:A)-MIN(A:A)) Distance matrix: Euclidean calculations via SQRT(SUMSQ()) K-means implementation using iterative ARRAYFORMULA Silhouette scoring validation 2.2 Predictive Modeling Algorithm Spreadsheet Implementation Accuracy Purchase Propensity Logistic regression with LINEST 78.6% CLV Prediction Exponential smoothing: FORECAST(... R²=0.81 3. Marketing Applications 3.1 Dynamic Segmentation Real-time categorization using nested IFS =IFS(AND(B2>100,D2 Sports" >0.7), "Premium Athletic Enthusiasts", AND(F2("Cosmetics")>0.4,G2<25), "GenZ Beauty Shopper", ...) 3.2 Campaign Performance Figure 1: Conversion lift from persona-based targeting vs generic campaigns 4. Limitations Current constraints include ≈500,000 row limits in spreadsheet platforms and need for periodic API refresh scheduling. Future work will explore Apps Script integrations for automated model retraining.