Product Performance Analysis with Python for Retail E-commerce Analytics
Learn how to perform product performance analysis for retail and e-commerce using real transaction data. This video covers essential steps to identify top-selling items, analyze revenue contributions, and uncover trends across products, time, and markets. You will gain practical skills to drive better marketing, sales, and inventory decisions by leveraging accurate sales metrics and avoiding common data pitfalls.
Follow along as we demonstrate how to clean and validate retail data, calculate key metrics like total revenue, average order value, and product rankings, and interpret results for actionable business recommendations. Whether you manage an online store or analyze sales data, these techniques will help you optimize product assortment and maximize business impact.
00:00 Introduction to product performance analysis
01:07 Key retail data concepts
01:46 Loading and previewing transaction data
02:34 Building a product catalog
03:12 Calculating revenue per transaction
04:11 Measuring total store revenue
05:11 Analyzing product sales volume
06:05 Identifying top revenue products
07:02 Counting unique products sold
07:54 Finding highest average price items
08:54 Tracking monthly revenue trends
09:55 Revenue by country analysis
10:47 Calculating average order value
11:48 Products included in most orders
12:41 Product revenue share breakdown
13:50 Top products per country
14:48 Applying the Pareto principle
15:56 Identifying unsold inventory
16:54 Checking for missing data
17:59 Handling returns and negative transactions
18:56 Common errors in revenue calculation
19:07 Ranking product performance
20:06 Comparing top and bottom performers
20:57 Business recommendations and summary
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