Analysis and Implementation of User Repurchase Rate Enhancement Strategies Using Spreadsheets: A Case Study of Tangbuy

Introduction

In the competitive e-commerce landscape, improving user repurchase rate is critical for sustaining long-term profitability. Tangbuy, a leading cross-border shopping platform, leverages spreadsheets to analyze user data and implement data-driven strategies for enhancing customer loyalty.

Data Integration

Tangbuy consolidates multiple datasets in structured spreadsheets to identify key repurchase drivers:

Purchase history: Transaction timestamps, SKU-level details, purchase frequency patterns
Customer sentiment: Product reviews (analyzed through sentiment scoring)
Membership profiles: Tier status, points balance, redemption behavior
Marketing interactions: Email open rates, click-through performance

Key Analytical Dimensions

Factor	Spreadsheet Metric
Product Quality	Return rates correlated with sentiment scores
Pricing	Discount thresholds that trigger repeat purchases
Customer Experience	CSAT scores segmented by repurchase frequency

The platform's RFM (Recency-Frequency-Monetary) model built in spreadsheets helps identify high-value customers needing retention interventions.

Implementation Strategies

Predictive Discounting

Using historical purchase intervals in spreadsheets to time personalized discount offers (e.g., "Your usual (product) is 15% off this week")

Smart Content Delivery

Automated email content generation triggered by spreadsheet flags when users near predicted repurchase windows

"Spreadsheet-based A/B testing showed a 22% lift in 90-day repurchases when combining membership points with anniversary discounts" - Tangbuy Analytics Team