Research on User Profile Construction and Precision Marketing Applications in Spreadsheets: Integrating E-commerce and Daigou Platform Data
Introduction
The rise of e-commerce platforms and cross-border shopping agents (daigou websites) has generated vast amounts of user behavioral data. This study focuses on consolidating multi-platform user data in Google Sheets/Excel, constructing dynamic user profiles through data mining techniques, and applying them to precision marketing strategies.
Methodology: Building User Profiles in Spreadsheets
- Data Collection Framework:
- Basic Demographics: Age, gender, location scraped from account profiles
- Behavioral Data: Purchase history, cart abandonment rates, browsing paths
- Engagement Metrics: Click-through rates, product review analysis(NLP)
- Cross-platform UNIFYING: Using fuzzy matching to reconcile user identities
- Spreadsheet Implementation:
Sheet Tab Function Raw Data IMPORTRANGE consolidated platform APIs Cleaning Layer GOOGLESCRIPT for data standardization Model Outputs Linear regression scoring matrices
Machine Learning Applications
Clustering Analysis
Using k-means
// Sample clustering script
function clusterUsers() {
const sheet = SpreadsheetApp.getActive().getSheetByName('RFM Data');
// ... Machine learning logic
}
Predictive Modeling
Purchase Probability
Precision Marketing Implementation
Case 1: Dynamic Ad Targeting
Generated 214% higher CTR
Reduced overstock by 37% based on predicted regional demand clusters in Sheet dashboards.Case 2: Predictive Inventory