Web Scraping Myntra for Apparel and Footwear Market Research

May 05, 2025

Introduction

The most rapidly developing sectors in e-commerce today are online didacticism regions such as apparel and footwear. Myntra is one of the largest online retailers of fashion items in India. Myntra has become a huge market research opportunity, having a collection of apparel, footwear, and accessories. With over 20 million active users, the brand selection ranges across Myntra data, which will imply insights relating to trends, customer preferences, and competitor pricing.

Web scraping gathers vast amounts of data quickly and efficiently. It would allow market researchers, retailers, and data enthusiasts access to timely trends and create a better understanding of the overall competitive landscape. Web scraping automatically helps businesses in extracting product information, reviews, discounts, etc.

This article will discuss everything to do with web scraping Myntra for apparel and shoe market research—from the simplest scraping techniques through the legal and ethical considerations associated with this work. This article will also help you understand how to collect and analyze Myntra data to make wiser business decisions.

Why Scrape Myntra?

Myntra is a powerful player in the online fashion and lifestyle market of India. Speculative reasons for scraping Myntra are countless with regard to bringing market insights.

Massive Inventory: Myntra is the creation, however, product selection extends to hundreds of brands; hence, scraping their product listings, pricing, and details will essentially provide information about the present scenario in the fashion market.
Customer Ratings and Reviews: Myntra provides a platform for customers to leave substantive feedback for products in the form of ratings and reviews, and this can be effectively interpreted when seeking to identify customer sentiment, pain points, and various popular trends related to apparel and footwear.
Price Tracking: Myntra is a frequent site of sales and discounts, thus making a good opportunity to collect data for comparison and tracking promotional strategies across the different categories of products.
Trend Analysis: By scraping Myntra's most popular items, sales, and seasonal trends, businesses can gauge what types of apparel and footwear are trending at any given time.
Competitor Analysis: With detailed product listings from Myntra, you can monitor pricing, discounts, and sales strategies of competitors to understand the market landscape.
Stock Availability: Scraping stock levels for different products allows you to track demand and product availability in real time.

Legal Considerations in Web Scraping Myntra

1. Myntra’s Terms of Service:

Myntra's terms of service prohibit their unauthorized access as well as the automated scraping process, meaning that you should always check their robots.txt file to determine which pages are allowed for crawling or scraping. You should abide by their instructions, and you should never scrape any pages that are explicitly forbidden.

2. Ethical Scraping:

Avoid Overloading the Server: Scrape responsibly by limiting the number of requests per second to avoid putting too much load on Myntra’s servers.
Respect Data Privacy: Do not scrape any personal or sensitive customer data (e.g., addresses, payment information).
Use Publicly Available Data: Stick to scraping data that is publicly accessible, such as product listings, reviews, and prices.

3. Compliance:

Ensure that you comply with data protection laws (e.g., GDPR if scraping for clients in the European Union) and Myntra’s terms of service. If unsure, consult with legal professionals to avoid any legal issues.

Tools and Technologies for Scraping Myntra

Python: Widely used due to its extensive libraries and ease of use.
JavaScript (Node.js): Ideal for scraping dynamic content generated by JavaScript.
BeautifulSoup: Python library to parse HTML and extract useful data.
Scrapy: A full Python framework for web scraping.
Selenium: For scraping JavaScript-heavy pages using browser automation.
Playwright: Modern tool for fast and stable scraping of dynamic sites.
Requests: Simple HTTP library for fetching web pages.
Proxies/IP Rotation: To avoid IP bans and access throttling.
Captcha Solvers: Tools like 2Captcha or Anti-Captcha may be used cautiously.

Step-by-Step Guide to Scraping Myntra

Step 1: Inspect the Myntra Website

Use Chrome DevTools to inspect tags such as <h1>, <span>, or <div> for product details.

<div class="product">
  <span class="product-name">Nike Running Shoes</span>
  <span class="price">₹2,999</span>
  <span class="rating">4.5/5</span>
</div>

Step 2: Installing Required Libraries

pip install requests beautifulsoup4 pandas

Step 3: Writing the Scraper (Static Pages)

import requests
from bs4 import BeautifulSoup
import pandas as pd

url = "https://www.myntra.com/shoes"
headers = {"User-Agent": "Mozilla/5.0 ..."}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, 'html.parser')
products = soup.find_all('div', class_='product')
product_data = []

for product in products:
    name = product.find('span', class_='product-name').text
    price = product.find('span', class_='price').text
    rating = product.find('span', class_='rating').text if product.find('span', class_='rating') else "No rating"
    product_data.append({'Product Name': name, 'Price': price, 'Rating': rating})

pd.DataFrame(product_data).to_csv('myntra_products.csv', index=False)

Step 4: Handling Pagination (Dynamic Pages)

from selenium import webdriver
from selenium.webdriver.common.by import By
import time

driver = webdriver.Chrome()
driver.get('https://www.myntra.com/shoes')
time.sleep(5)
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(3)
products = driver.find_elements(By.CSS_SELECTOR, '.product')

for product in products:
    name = product.find_element(By.CSS_SELECTOR, '.product-name').text
    price = product.find_element(By.CSS_SELECTOR, '.price').text
    rating = product.find_element(By.CSS_SELECTOR, '.rating').text
    print(name, price, rating)

driver.quit()

Analyzing the Scraped Data

1. Price Analysis

● Compare prices for different brands, categories, and sellers.
● Identify discounts and promotions.

2. Trend Identification

● Look for patterns in ratings, reviews, and sales performance.
● Detect seasonal trends and popular products

3. Competitor Monitoring

● Track the product offerings of competitors.
● Analyze competitor pricing strategies and product variations.

4. Customer Sentiment

● Analyze customer reviews and ratings to gauge product satisfaction.
● Use text mining or sentiment analysis techniques on reviews..

Conclusion

Myntra web scraping, at its best, is a boon for research in the apparel and footwear market. It helps in the automated collection of product data, reviews, prices, etc., for trends and competitor analysis, per data-driven business decisions. But always remember ethical guidelines, follow the law, and use the data appropriately.

Myntra web scraping is a strong weapon to have in the arsenal of your market research if you want to scoop out a competitive edge over others of the same ilk in the race of fashion e-commerce.