Introduction

Tangbuy, a cross-border e-commerce platform specializing in overseas procurement (代购), has implemented a data-driven approach to improve user repurchase rates. By leveraging spreadsheets to consolidate and analyze customer behavior data, Tangbuy identifies key factors influencing repeat purchases and executes targeted strategies to enhance customer loyalty. This article outlines the methodology and outcomes of this initiative.

Data Consolidation in Spreadsheets

Tangbuy aggregates the following user data in spreadsheet templates for analysis:

• Transaction History: Purchase timestamps, product categories, frequency, and spending amounts.
• Feedback Metrics: Product ratings, review sentiment, and customer support logs.
• Membership Data: Tier status, point balances, and exclusive reward usage.

Pivot tables and formula-driven dashboards (e.g., VLOOKUP, COUNTIF) segment users by recency, frequency, and monetary (RFM) value to prioritize high-potential cohorts.

Key Repurchase Drivers Identified

Correlation analysis revealed critical levers impacting repurchase behavior:

Implemented Strategies

Tangbuy executed four spreadsheet-coordinated initiatives:

1. Dynamic Loyalty Tiers: Spreadsheet automation triggered tier upgrades when users crossed spend thresholds, with real-time points calculators synced to the platform's API.
2. Personalized Email Campaigns: Exported user segments from spreadsheets powered Mailchimp workflows with product recommendations based on last purchased category (±30 days).
3. Win-back Promotions: Conditional formatting flagged lapsed users (>90 days inactive) for tailored discount codes (generated via RAND/INDEX formulas).
4. Service Guarantees: FAQ-resolution timelines logged in spreadsheets reduced complaint-to-resolution duration by 41%.

Performance Measurement

Quarterly metrics were tracked in a "Strategy Dashboard" tab:

==================================================  
| Metric             | Q1  | Q2   | Δ     |
|--------------------|-----|------|-------|
| 30-day Repurchase% | 14% | 21%  | +50%↑ |
| Avg. Order Value   | $78 | $104 | +33%↑ |
| LTV (12mo)         | $312| $439 | +41%↑ |
==================================================
    

Data validation rules alerted managers when KPIs fell outside expected ranges (e.g., repurchase rate < 18%).

Conclusion

By transforming spreadsheets into a centralized analytics engine, Tangbuy achieved a 22% YoY increase in repeat purchases with minimal tech overhead. Future enhancements will integrate Python scripts for predictive modeling while retaining spreadsheets' operational simplicity. This case demonstrates how methodical data organization—paired with executable insights—can drive measurable commerce growth.