E-Commerce Case Study

Client

Online Retail Company

Problem Statement

The company is struggling to understand its customers and sales performance.
They want insights into:

• Customer behavior → retention, churn, and segmentation.
• Sales performance → top products, repeat purchases, and seasonality.
• Financial impact → revenue growth, refunds, and profitability.

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Step 1: Data Understanding

Datasets

• customers.csv → demographics (customer_id, name, gender, signup_date, etc.)
• transactions.csv → (transaction_id, customer_id, product_id, date, quantity, price, status [purchase/refund])

First Tasks

• Explore number of customers, transactions, and unique products
• Check missing values, duplicates, invalid entries
• Inspect relationships: customer_id links the two files

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Step 2: Data Cleaning

• Standardize date formats → YYYY-MM-DD
• Remove/flag duplicate rows
• Convert numeric fields (quantity, price) into correct data types
• Handle refunds/returns (negative values or status column)

New Calculated Columns

• Revenue = price × quantity
• Net Revenue = Revenue – Refunds

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Step 3: Define KPIs

Customer KPIs

• Total Customers
• New vs Returning Customers
• Customer Retention Rate
• Churn Rate
• Average Revenue per Customer

Sales & Financial KPIs

• Total Revenue (Σ price × quantity)
• Revenue Growth (monthly/quarterly)
• Refund/Return Rate
• Average Order Value (AOV)
• Top 10 Customers by Lifetime Value (LTV)
• Top 10 Products by Sales

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Step 4: Exploratory Data Analysis (EDA)

Customer Analysis

• Gender distribution
• Signup trends
• Repeat purchase rate

Transaction Analysis

• Revenue over time (daily, monthly, yearly)
• Distribution of order values
• High- and low-frequency customers

Refunds/Returns

• % of transactions refunded
• Which customers request the most refunds?
• Net impact on revenue

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Step 5: Key Business Questions

• Who are the most valuable customers?
• What is the customer churn rate?
• Which products generate the most revenue?
• What is the revenue trend over time?
• How much do refunds impact net revenue?

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Step 6: Insights & Recommendations

Possible examples (based on analysis):

• 60% of customers purchase only once → need loyalty program
• Refunds account for 15% of revenue loss → investigate product quality
• Top 5% of customers bring in 40% of sales → launch VIP rewards

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Step 7: Deliverables

• Python Jupyter Notebook → cleaning, analysis, visualization
• Charts (Matplotlib/Seaborn) → sales trends, churn, top customers
• Short Report/Presentation → key findings + recommendations

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This case study will provide a clear view of customer behavior, sales performance, and financial impact to help the business make data-driven decisions.