1. Introduction
Effectively managing after-sales service data is crucial for maintaining customer satisfaction in cross-border e-commerce platforms like Lovbuy. This article outlines a structured approach to analyze service metrics (returns, repairs, complaints) using spreadsheet tools and showcasing how data-driven insights lead to measurable service improvements.
2. Data Collection Framework
| Metric | Data Source | Tracking Frequency |
|---|---|---|
| Return requests | Order management system | Weekly |
| Exchange cases | CRM tickets | Daily |
| Repair records | Service partner logs | Monthly |
| Complaint resolution time | Customer service dashboard | Real-time |
Recommendation: Centralize data in Google Sheets with IMPORTRANGE() for automatic updates across departments.
3. Key Analysis Methods
3.1 Pivot Table Insights
- Product category vs. return reasons (e.g., 23% electronics returned due to shipping damage)
- Geographical distribution of complaints (noting 40% flagged delivery delays to Oceania)
3.2 Time-Trend Formulas
=QUERY(ServiceLogs!A:E,"select month(A), count(B) where C='Repair' group by month(A)")
3.3 Quality Scoring
Creating composite indices weighing:
Resolution speed (30%) | Customer satisfaction (40%) | Cost impact (30%)
4. Improvement Initiatives
A. Staff Training (Implemented Q3)
- Product knowledge certification
- Role-play scenarios for common complaints
Result: 17% reduction in escalations
B. Process Automation
- Automated RMA authorization for low-value items
- Zapier integration between spreadsheets and CS tools
Result: 2.5 days faster turnaround
5. Performance Dashboard
Live Tracking Metrics:
▸ First-response SLA: 91% (▲3%)
▸ Refund processing: 4.2 days (▼0.8)
▸ Repeat complaint rate: 6.1% (━)
6. Continuous Improvement
Quarterly review recommendations:
- Expand root-cause analysis columns in tracking sheets
- Implement conditional formatting for SLA breaches
- Add supplier performance tabs for upstream accountability