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Business
E-commerce Data Analysis Framework
Words 436Read Time 2 min
May 15, 2020
May 18, 2025
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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.
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