LitBuy: Analyzing Refund Timelines and Seller Accuracy
In the world of e-commerce, understanding refund processing speeds and seller reliability is crucial for both buyers and platform administrators. The LitBuy Spreadsheet provides a systematic approach to analyze these key performance indicators using simple yet powerful table structures and formulas.
Setting Up Your Analysis Spreadsheet
Create a comprehensive spreadsheet with the following columns to track and analyze refund data:
| Column | Description | Data Type |
|---|---|---|
| Order ID | Unique identifier for each order | Text |
| Seller Name | Name of the seller/store | Text |
| Refund Request Date | Date when refund was requested | Date |
| Refund Completion Date | Date when refund was processed | Date |
| Refund Processing Days | Calculated field: Completion Date - Request Date | Number |
| Refund Status | Completed, Pending, or Denied | Text |
| Refund Reason | Reason for refund request | Text |
| Seller Response Time (Days) | Days taken by seller to respond | Number |
Key Formulas for Analysis
1. Calculating Refund Processing Speed
To calculate the average refund processing time for each seller:
=AVERAGEIF(Seller_Name_Range, "Specific_Seller", Processing_Days_Range)
Example:
Calculate the percentage of completed refunds versus total refund requests: Example:
Create an automated performance rating using nested IF statements: Use your analyzed data to create: Identify common refund reasons by seller: By implementing this LitBuy spreadsheet analysis system, you can transform raw refund data into actionable insights, ultimately improving buyer satisfaction and seller performance across your e-commerce platform.=AVERAGEIF(B2:B100, "TechGadgets", E2:E100)
2. Seller Reliability Rate
=(COUNTIFS(Seller_Range, "Specific_Seller", Status_Range, "Completed") / COUNTIF(Seller_Range, "Specific_Seller")) * 100
=(COUNTIFS(B2:B100, "HomeEssentials", F2:F100, "Completed") / COUNTIF(B2:B100, "HomeEssentials")) * 100
3. Average Response Time by Seller
=AVERAGEIF(Seller_Range, "Specific_Seller", Response_Time_Range)
Sample Analysis Table
Seller Name
Total Refunds
Completed Refunds
Reliability Rate
Avg Processing Days
Avg Response Days
Performance Rating
TechGadgets
15
14
93.3%
3.2
1.5
Excellent
HomeEssentials
25
20
80.0%
5.8
3.2
Good
FashionHub
30
22
73.3%
7.5
4.1
Fair
BookWorld
10
8
80.0%
4.2
2.0
Good
Automated Performance Rating Formula
=IF(AND(Reliability_Rate>90, Avg_Processing_Days<4), "Excellent",
IF(AND(Reliability_Rate>75, Avg_Processing_Days<7), "Good",
IF(Reliability_Rate>60, "Fair", "Poor")))
Creating Visual Dashboards
Advanced Analysis Techniques
1. Monthly Performance Trends
=SUMIFS(Completed_Refunds_Range, Seller_Range, "Specific_Seller", Date_Range, ">="&START_DATE, Date_Range, "<="&END_DATE)
2. Refund Reason Analysis
=COUNTIFS(Seller_Range, "Specific_Seller", Reason_Range, "Specific_Reason")
Benefits of Systematic Analysis
Best Practices