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Key E-commerce Business Metrics Overview
Metric Name | Definition / Formula | Explanation |
Total Revenue | Total money earned from all sales | Measures overall business size and income; used to compare different time periods or campaign performance |
Total Quantity Sold | Total number of units sold | Indicates sales volume; used to assess product demand and inventory planning |
Profit | Revenue - Cost of Goods Sold (COGS) | Measures actual income; critical for evaluating business sustainability |
Gross Margin (%) | (Profit ÷ Revenue) × 100% | Reflects profitability; higher margin indicates better cost control or premium pricing |
Number of Orders | Total number of sales transactions | Reflects customer activity; helps analyze conversion rate and marketing effectiveness |
Average Order Value (AOV) | Revenue ÷ Number of Orders | Shows customer purchasing behavior; useful for upselling strategies |
Average Selling Price (ASP) | Revenue ÷ Quantity Sold | Reveals pricing trends; used for product pricing analysis |
- Low Gross Margin: A gross margin of 12.47% indicates limited profit per sale. It is recommended to evaluate opportunities to optimize procurement, logistics, or pricing strategies to improve margins.
- High Order Volume but Moderate AOV: While the number of orders is high, the Average Order Value (AOV) is only 458.61, suggesting that customers are spending a moderate amount per transaction. Consider implementing bundle offers or upselling strategies to increase AOV.
- Profitability vs. Revenue: Despite a high total revenue of 2.29 million, the total profit is only around 286K. This suggests that operating costs may be significant. Focus should be placed on improving operational efficiency or reducing costs to enhance overall profitability.
Time-Based Sales Analysis Framework
Category | Description |
1. Time Series Sales Trend Analysis | Analyze sales by week, month, quarter, and year to identify patterns and seasonality. |
2. Year-over-Year (YoY) / Month-over-Month (MoM) Growth | Compare current period to the same period last year or the previous period to evaluate growth trends. |
3. Time Series Forecasting | Use models to predict future sales trends to support planning and inventory management. |
4. Calendar Heatmap Visualization | Display daily or weekly sales using a calendar heatmap to quickly spot high and low seasons. |
5. Moving Average Analysis | Apply 7-day, 30-day moving averages to smooth out short-term fluctuations. |
6. Seasonal Decomposition | Use methods like seasonal_decompose to separate trend, seasonality, and noise components. |
- Author:Entropyobserver
- URL:https://tangly1024.com/article/1f4d698f-3512-8035-a2fb-f5c94c198515
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