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The success of drop-shipping businesses like Lovbuy heavily relies on efficient after-sales services to maintain customer satisfaction. This document presents a methodology for analyzing Lovbuy's after-sales service data (including returns/exchanges, repair records, and complaint resolutions) through spreadsheet-based statistics, followed by targeted quality improvement actions.
| Columns (Dimensions) | Description |
|---|---|
| Order ID | Unique identifier for tracking product origin |
| Service Type | Return/Exchange/Repair/Complaint categorization |
| Resolution Time | Days required to close the case |
| Customer Rating | Satisfaction score (1-5 stars) |
Additional metrics tracked: Monthly case volume trends, product category failure patterns, and regional service demand variations.
Implement bi-weekly training sessions targeting:
→ Foundational improvements measured by pre-/post-training test scores (Tracked in separate 'Training_Results' sheet)
Restructure workflows based on bottleneck identification: Implemented streamlined authorization process reducing approval steps from 5 to 2
Connect spreadsheet system with CRM software via =IMPORTDATA()
| KPI | Baseline | 30-day Target | 60-day Target |
|---|---|---|---|
| First-Contact Resolution % | 52% | 65% | 75% |
| Avg. Processing Time | 3.2 days | 2.5 days | 1.8 days |
Theoretical improvement curve demonstrating compounding quality gains over time
Biannual refresher protocols ensure sustained progress:
1. Comparative analysis between old vs. new case handling methods in dedicated 'Method_Compare' sheet
2. Feedback loop integration from social media sentiment tracking
3. ROI calculations on improvement investments (conditional formatting highlights high-impact areas)
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