Myntra Sales Analysis

A comprehensive exploratory data analysis (EDA) and customer segmentation of Myntra Gifts Ltd.'s online retail sales data, uncovering actionable insights into sales trends, customer behavior, and product performance.

Table of Contents

• Introduction
• Features
• Dataset Overview
• Installation
• Usage
• Key Insights
• Power BI Insights
• Recommendations
• Power BI Dashboard
• Contributing
• License
• Contact

Introduction

This project analyzes sales data from Myntra Gifts Ltd., a UK-based division of Myntra specializing in unique all-occasion giftware, covering transactions from December 1, 2009, to December 9, 2011. The goal is to extract valuable insights to enhance business strategies by identifying purchasing trends, evaluating product performance, and understanding customer behavior. The dataset includes 328,610 records across 20 columns, providing a detailed snapshot of international online retail activities.

Author: Harddik Singh
Published: June 30, 2025

Features

• Sales & Revenue Trends: Analyzes monthly and quarterly revenue, average order value (AOV), and price trends.
• Customer Behavior Analysis: Examines purchase patterns across weekdays/weekends, peak hours, and months.
• Product Performance: Identifies top-performing products by revenue and quantity, and analyzes unit price distributions.
• Country-Level Insights: Evaluates revenue contributions and pricing variations across countries.
• Time & Purchase Patterns: Highlights peak invoice hours and day-of-week trends.
• RFM Analysis: Segments customers based on Recency, Frequency, and Monetary metrics.
• K-Means Clustering: Groups customers into actionable segments using machine learning.
• Statistical Significance Testing: Provides confidence intervals and hypothesis testing for key metrics.

Dataset Overview

The dataset (myntra_sales.db) contains 328,610 records with 20 columns, including:

• Key Variables: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country, Total_Revenue, Items_Per_Invoice, etc.
• Data Quality: No duplicates or missing values, ensuring robust analysis.
• Time Frame: December 1, 2009, to December 9, 2011.
• Scope: International online retail transactions for Myntra Gifts Ltd.

Installation

To run the analysis, follow these steps:

1. Clone the Repository:

   git clone https://github.com/username/myntra-sales-analysis.git
   cd myntra-sales-analysis

2. Install Dependencies: Ensure Python 3.8+ is installed. Install required libraries:

   pip install pandas numpy matplotlib seaborn cufflinks plotly scikit-learn scipy

3. Set Up Power BI (Optional): Install Power BI Desktop for interactive dashboard exploration.

4. Set Up the Database: Place the myntra_sales.db SQLite database in the project root directory.

5. Environment Setup: Create a .env file for sensitive configurations (e.g., database paths), though not required for this project.

Usage

1. Load the Dataset: Run the provided Jupyter notebook or Python script to connect to the SQLite database:

   import sqlite3
   import pandas as pd
   conn = sqlite3.connect('myntra_sales.db')
   df = pd.read_sql_query("SELECT * FROM myntra_sales_summary", conn)

2. Run the Analysis: Execute the analysis scripts to generate visualizations and insights. Key analyses include:

   • Sales trends (monthly/quarterly revenue, AOV).
   • Customer segmentation (RFM and K-Means clustering).
   • Statistical tests (confidence intervals, t-tests).

3. Explore the Power BI Dashboard: Import the provided .pbix file into Power BI Desktop to interact with visualizations (see Power BI Dashboard).

4. View Results: Results are displayed as plots (e.g., bar charts, histograms, 3D scatter plots) and printed outputs. Refer to the Myntra_report.html for detailed visualizations.

Key Insights

Sales & Revenue Trends

• Monthly Revenue: Peaks in November ($600K), lowest in February (~$200K).
• Quarterly Revenue: Q4 2025 is the strongest ($1.4M), with steady growth from Q1.
• Average Order Value (AOV): Highest in October ($303), with peak revenue per order at 6 AM ($30 AOV).
• Price Trends: Unit prices increase from July 2025 to February 2026, stabilizing quarterly at $12-$12.50.

Customer Behavior

• Items Per Invoice: Top 10% of invoices average 888.8 items, while the bottom 50% average 108.8, indicating large orders drive sales.
• Weekday vs. Weekend: Weekdays have higher AOV ($256) than weekends ($240). Monday has the highest AOV ($270).
• Peak Hours: Purchases peak between 6 AM and 12 PM, especially at 6 AM (2840 invoices).

Product Performance

• Top Products: StockCode 47566 generates the highest revenue (>30,000), while "pack_of_72_retrospot_cake_cases" leads in quantity (24.42% of total).
• Unit Price Distribution: Most products are priced ≤$10, with 31,338 items < $0.50 and top 1% > $6.89.

Country-Level Insights

• Revenue Contribution: The UK dominates (90.2%), followed by Germany (3.4%) and France (3.0%).
• Pricing Variations: Norway and Belgium have higher median unit prices ($4-$6), while Switzerland and EIRE are lower ($2-$4).

Customer Segmentation

• RFM Analysis: Segments customers into "Best Customers" (12% of total), "Loyal Customers" (22%), and others, with balanced score distribution.
• K-Means Clustering: Identifies 4 clusters (Loyal, Potential, Lost, New/Inactive), with a silhouette score of 0.591 indicating good separation.

Statistical Tests

• AOV Confidence Intervals: Top-selling products have a 95% CI of $206.10-$212.38 (mean: $209.24), vs. low-selling at $10.17-$11.72 (mean: $10.94).
• T-Test: Significant difference in AOV between top and low-performing vendors (t=120.169, p<0.05).
• Purchase Frequency: High-value customers (top 20%) average 10.74 purchases (CI: 9.84-11.63), vs. low-value at 2.34 (CI: 2.27-2.40).

Power BI Insights

Sales & Revenue Trends

• Monthly Revenue: Peaks with ~$48K YoY growth in November 2011, with the lowest monthly revenue not specified in dashboard data.
• Quarterly Revenue: No specific quarterly data provided, but total sales reached $339.76K across top countries.
• Average Order Value (AOV): Peak revenue per order at 6 AM is $30, with no specific monthly peak identified beyond dashboard hours.
• Profit Margin: Overall profit margin is 28.15%, with a total profit of $95.64K.
• Returns: December shows the highest returns with 142 orders, and a return rate spike of 35.3% in Spain.

Customer Behavior

• Order Frequency: Customer ID 14911 has the highest order frequency (2159 orders), followed by 12748 (850 orders).
• Customer Spending: Top 5 customers by total spend range from $66K to $149K.
• Sales Growth: Highest YoY sales growth achieved in November 2011 ($48K).

Product Performance

• Top Products: "Hot Water Bottle Keep Calm" leads sales at 15.51%, with top 5 StockCodes by average order value ranging from $25.92 to $85.37 (e.g., "Jumbo Bag Red Retrospot" at $85.37, "Regency Teapot" at $70.69).
• Profit by StockCode: StockCode 47566 tops profit at 1332 units, followed by 79321 (1105 units).
• Order Frequency by Product: "White Hanging Heart T-Light" (120 orders) and "Party Bunting" (102 orders) show high order frequency.

Country-Level Insights

• Revenue Contribution: Total sales across top countries is $339.76K, with the UK as the dominant market.
• Profit by Country: EIRE leads with $7.97K profit and 28.64% margin, France with $6.1K profit and 21.2% margin, while Portugal shows a loss of -$0.5K, and Channel Islands a loss of -$11K.
• Returns and Profit Margin: Spain has the highest return rate at 35.3%, while Saudi Arabia has the lowest profit margin at -57.80%.

Customer Segmentation

• Customer Profit Contribution: Top 5 customers contribute $66K to $149K in overall profit.

Recommendations

Based on the analysis, the following strategies can optimize Myntra Gifts Ltd.'s business performance:

• Target High-Value Customers: Focus retention efforts on "Best Customers" (e.g., Customer ID 14911 with 2159 orders, top 5 with $66K-$149K spend) and "Loyal Customers" (22%) with personalized promotions or loyalty programs.
• Boost Off-Peak Sales: Increase marketing during low-performing months (e.g., February) and evening hours (post-6 PM) with flash sales or discounts, leveraging the morning revenue peak (6 AM, ~$30 AOV).
• Optimize Product Portfolio: Promote top-performing products (e.g., "Hot Water Bottle Keep Calm" at 15.51%, "Jumbo Bag Red Retrospot" at $85.37 AOV) through bundling. Review low-profit items (e.g., Saudi Arabia’s -57.80% margin) and high-return products (Spain, 35.3%) for profitability adjustments.
• Expand International Markets: Leverage the UK’s dominance ($339.76K total sales) while targeting high-profit countries like EIRE (28.64% margin) and France (21.2% margin), and address low-profit markets (e.g., Portugal -$0.5K, Saudi Arabia) with pricing strategies.
• Enhance Midweek Engagement: Capitalize on midweek peaks (Wednesday-Friday) by scheduling promotions, aligning with November’s $48K YoY growth.
• Reduce Returns: Investigate Spain’s 35.3% return rate and December’s 142 returns, implementing quality checks or customer education to minimize returns.
• Refine Customer Acquisition: Convert "Potential Customers" into "Loyal Customers" with targeted marketing, using insights from top customers (e.g., 14911, 12748) and their profit contribution ($66K-$149K).

Power BI Dashboard

The Power BI dashboard provides an interactive interface to explore the Myntra Sales Analysis insights. It is available as myntra_sales_dashboard.pbix in the repository.

Features

• Interactive Visualizations: Includes bar charts for monthly sales ($48K YoY growth in November 2011), line graphs for AOV by hour (peak at 6 AM, ~$30), and pie charts for top products ("Hot Water Bottle Keep Calm" at 15.51%, "Jumbo Bag Red Retrospot" at $85.37 AOV).
• Filters and Slicers: Filter by country (e.g., UK, EIRE), product (e.g., StockCode 47566), or time period (e.g., November 2011, Q4) to drill down into trends.
• Key Metrics: Displays total sales ($339.76K), profit ($95.64K, 28.15% margin), top customer order frequency (14911 with 2159 orders), and top 5 customer spend ($66K-$149K).
• Customer Segmentation: Visualizes top customers by quantity sold and profit contribution, and countries with lowest profit margins (e.g., Saudi Arabia at -57.80%).
• Time-Based Analysis: Shows peak revenue hours (6-12 AM), monthly returns (December peak at 142), and YoY growth (0.1% overall, 48K in November 2011).

Usage

1. Open the Dashboard:
   • Download myntra_sales_dashboard.pbix from the repository.
   • Open in Power BI Desktop.
2. Connect to Data:
   • Link to myntra_sales.db via MYSQL connector or import the CSV equivalent.
3. Interact with Visuals:
   • Use slicers to filter by country (e.g., Spain for returns), product (e.g., 47566 for profit), or month (e.g., November for growth).
   • Hover over charts for details, such as France’s $6.1K profit or Spain’s 35.3% return rate.
4. Export Insights:
   • Export visuals to PowerPoint or PDF for presentations using Power BI's export functionality.

Dashboard Screenshot

1️⃣ Overview Dashboard – Sales, Orders & Product Insights

Figure 1: This dashboard provides a holistic view of total sales (₹339.76K), profit margin (28.15%), quantity growth (+1230%), and total orders. It also highlights top-selling months, top 5 products by sales, and most profitable stock codes.

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2️⃣ Market Dashboard – Time & Regional Performance

Figure 2: This dashboard showcases hourly revenue patterns, monthly returns, and country-wise YoY performance. December marks the highest return volume (201), while Finland and Sweden show strong YoY growth.

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3️⃣ Segmentation & Categories Dashboard – Country, Customer & SKU Analysis

Figure 3: This view focuses on return rates by country (Spain: 35.3%), high-frequency customers (Customer 14911), and product reorder trends. It also highlights stock codes with the highest average order value (AOV), such as 23284 (₹85.37).

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Note: Ensure the images/ folder contains the dashboard screenshot for proper rendering in the README.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature-name.
3. Commit changes: git commit -m "Add feature-name".
4. Push to the branch: git push origin feature-name.
5. Open a pull request.

License

This project is licensed under the MIT License. See LICENSE for details.

Contact

• Author: Harddik Singh
• Email: [email protected]