In the fast-paced world of e-commerce, data is your most powerful asset. For platform managers and quality control teams on KAKOBUY, transforming raw data on product quality and seller performance into clear, actionable insights is crucial. This guide explores how to leverage simple tools like chartspivot tables
Why Visualization is Key
Scrolling through endless spreadsheets of defect codes, return reasons, and seller IDs is inefficient. Visualization helps you:
- Spot Trends Instantly:
- Compare Performance:
- Pinpoint Root Causes:
- Communicate Clearly:
- Compare Performance:
Part 1: Mastering the Pivot Table for Data Aggregation
Before you chart, you need to organize your data. A pivot table is your first and most powerful step.
Typical Data Setup:
Your raw data table should include columns such as: Order ID, Seller Name, Product Category, QC Defect Code, Refund Reason, Date, Refund Amount.
Key Pivot Table Configurations:
| Objective | Rows Area | Values Area | Filters Area (Optional) |
|---|---|---|---|
| Seller Refund Ratio | Seller Name | Count of Refunded Orders, Count of Total Orders (calculate ratio) | Date Range |
| Top QC Defects by Category | Product Category, QC Defect Code | Count of Orders | Seller Name |
| Monthly Refund Trend | Year-Month (grouped from Date) | Sum of Refund Amount, Count of Refunds | Refund Reason |
Tip: Use the "Refresh" function when your underlying data updates to keep your pivot tables current.
Part 2: Choosing the Right Chart for Insight
Once your pivot table summarizes the data, select a chart that matches your analytical goal.
1. For Tracking Trends Over Time: Line Chart
Use Case:
How:
Insight:
2. For Comparing Sellers or Defects: Bar/Column Chart
Use Case:
How:
Insight:
3. For Understanding Composition: Pie or Donut Chart
Use Case:
How:
Insight:
Practical Workflow: From Data to Action
- Extract Data:
- Create Pivot:Rows: Seller Name, Values: Count of Refunded Orders, Count of Total Orders.
- Add Calculated Field:
- Visualize:
- Spot & Drill Down:
- Act: