Effective data analysis is key to optimizing your sourcing strategy. This guide will show you how to transform raw PinguBuy order data into actionable insights using charts for refund ratios, inspection pass rates, and average shipping costs over time.
Ensure your export from PinguBuy includes at least the following columns for each order:
Create a separate summary table on a new sheet to aggregate data by MonthWeek.
Calculation:(Number of Refunded Orders / Total Orders) * 100.
Visualization:Line Chart. The X-axis shows the timeline (months), and the Y-axis shows the refund percentage. This chart quickly highlights periods with quality or supplier issues.
Calculation:(Number of QC Passes / Total Inspections) * 100.
Visualization:Column ChartCombo Chart
[Column Chart: QC Pass Rate % by Month]
Calculation:Average(Total Shipping Cost)
Visualization:Line Chart
[Line Chart: Average Shipping Cost (USD) Over Time]
Place all three charts on a single dashboard sheet. Add:
Analyze the charts together to inform business decisions:
| Observation | Potential Action |
|---|---|
| Refund ratio spikes simultaneously with a drop in QC pass rate. | Investigate specific supplier performance; reinforce QC standards or renegotiate terms. |
| Steady increase in average shipping costs over several months. | Evaluate alternative shipping agents or consolidate shipments to reduce costs. |
| High QC pass rate but persistently moderate refund rate. | Review reasons for refunds—they may be due to shipping damage or inaccurate product description, not quality. |
