Effective data analysis is key to improving your shopping agent experience. This guide explains how to use a simple spreadsheet to track and visualize crucial LoveGoBuy performance metrics like Quality Control (QC) approval rates, delivery timelines, and refund percentages for clear, actionable insights.
Monitoring this data helps you make informed decisions. You can identify consistently reliable sellers, understand shipping method performance, and track issues that lead to refunds. Visualizing this data transforms raw numbers into a clear performance dashboard.
Create a spreadsheet with the following columns for each order:
| Order ID | Seller/Store | QC Result (Pass/Fail) | QC Notes | Ship Method | Posted Date | Delivered Date | Total Days | Refund Issued? (Y/N) | Refund Reason |
|---|---|---|---|---|---|---|---|---|---|
| #00123 | Store_A | Pass | No flaws | EMS | 2023-10-01 | 2023-10-12 | 11 | N | N/A |
Consistently log every order to build a robust dataset for analysis.
Purpose:
How to Visualize:bar chart or pie chart. Group data by 'Seller/Store' and calculate the percentage of 'Pass' results versus total orders from that seller. This quickly highlights the most and least reliable sources.
[Visual: Bar chart showing "Seller A: 95% Pass", "Seller B: 78% Pass", "Seller C: 60% Pass"]
Purpose:
How to Visualize:average line chart or box plot. Group data by 'Ship Method' and calculate the average 'Total Days' for delivery. Plotting this over time can also show consistency or seasonal delays.
[Visual: Line chart comparing average delivery days: DHL (8 days), EMS (14 days), SAL (32 days)]
Purpose:
How to Visualize:stacked bar chart or donut chart
[Visual: Donut chart showing refund reasons: 45% Quality Issues, 30% Wrong Size, 25% Not Received]
