Manually calculating seller performance scores is tedious and error-prone. For CNFANS users managing multiple suppliers, automating this process is key. This guide shows you how to create a self-updating performance dashboard using basic spreadsheet formulas, focusing on reliability and refund metrics.
The Core Concept: The Performance Score Formula
We will calculate a unified Performance Score (out of 100)
The logic is simple: reward high reliability and penalize high refund rates. A basic formula is: This gives a score from 0 to 100. We can then enhance it for better insights.
Performance Score = Reliability Ratio - Refund Ratio
Building the Automated Spreadsheet
Set up columns with the following headers and formulas. Assume data starts on Row 2.
| Column | Description | Sample Formula (Google Sheets/Excel) |
|---|---|---|
| A: Seller Name | Manual entry | Text Input |
| B: Total Orders | Manual entry or import | Number Input |
| C: Completed Orders | Manual entry or import | Number Input |
| D: Refunded Orders | Manual entry or import | Number Input |
| E: Reliability Ratio (%) | Auto-calculated | =(C2/B2)*100 |
| F: Refund Ratio (%) | Auto-calculated | =(D2/B2)*100 |
| G: Raw Performance Score | Reliability - Refunds | =E2 - F2 |
| H: Adjusted Performance Score | Scaled & penalized for low volume | =IF(B2<10, G2*0.8, G2) |
| I: Rating Tier | Auto-assigned category | =IF(H2>=90, "A: Excellent", IF(H2>=75, "B: Good", IF(H2>=60, "C: Fair", "D: Review"))) |
Advanced Automation & Tips
- Weighted Scoring:=(E2*0.7) - (F2*0.3).
- Minimum Order Threshold:IF
- Color-Coding Tiers:Rating TierScore
- Dynamic Sorting:descending
- Color-Coding Tiers:Rating TierScore
Conclusion
By implementing these simple spreadsheet formulas, CNFANS users can transform raw order data into a dynamic, auto-updating seller ranking system. This eliminates manual calculations, ensures consistency, and allows you to instantly identify your most reliable suppliers based on tangible performance data. Your sourcing decisions become faster, more objective, and data-driven.
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