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E-commerce Sales Analysis

Project Overview

In today’s fast-paced e-commerce landscape, businesses need clear insights into purchasing behavior and sales trends to stay competitive and maximize revenue.
This project presents a simulated analysis of sales performance for an online store, highlighting top-selling products, high-performing categories, and key customer segments.

With these insights, e-commerce teams can:

Make data-driven decisions about inventory
Tailor offerings to the right customers
Identify opportunities to boost sales and profitability

Dashboard

E-commerce Dashboard Screenshot

Interactive Google Sheets:

View the live Google Sheet dashboard here:
View-only Dashboard in Google Sheets

Make a copy to interact with filters

Features:

Total Sales, Total Orders, Average Order Value (AOV), Return Rate
Sales breakdown by quarter, region, category, customer demographics
Trends across time, product categories, age brackets, and shipping status
Interactive filters for Region, Period (Quarter), Category, and Delivery Status

Data Preparation

A cleaned and transformed dataset was created from raw e-commerce transactions.

Missing values handled:
- Blank Region and Shipping Status → replaced with "Unknown"
- Blank Age → retained for separate analysis
Derived fields created:
- Age Bracket
- Day of Week
- Month
- Quarter

Key Metrics Tracked

Customer ID – unique identifier for purchase tracking
Gender – customer segmentation
Region – geographical insights
Age / Age Bracket – demographic segmentation
Product Name & Category – performance comparisons
Unit Price & Quantity – revenue calculations
Total Price – sales revenue per transaction
Shipping Fee – logistics costs
Shipping Status – delivery tracking
Order Date – trend analysis

Insights

Some key findings from the dashboard:

Q4 generated the highest sales, driven by holiday shopping patterns.
Electronics dominated sales, accounting for nearly 90% of revenue.
Return Rate reached ~50%, highlighting an opportunity for quality control and better fulfillment processes.
Customers aged 35–44 and 45–54 contributed significantly to sales volume.
Regional distribution showed a balanced split across East, West, and South, with the North slightly lower.

Tools Used

Google Sheets – data cleaning, pivot tables, calculated fields
Google Sheets Charts – dashboard visualizations
Formulas – data transformations (e.g., IF, FILTER, DATE functions)

Source Data

E-commerce Sales and Customer Insights Dataset

Next Steps

Add advanced analytics such as Customer Lifetime Value (CLV) and Cohort Retention.
Automate reporting in Looker Studio for dynamic dashboards.
Integrate with SQL for scalable analysis.

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