Guide to Web Scraping eCommerce Websites Without Getting Blocked

In the era of digitization, having an online store presence becomes essential for every business. Consumers prefer to see, compare, and shop on the go, which saves them time and money.

With many online stores launching, this sector is becoming increasingly competitive, specifically for the eCommerce industry. Imagine you are thinking of opening an online store to sell clothes and accessories. You will find thousands of competitors selling the same product in the marketplace. You need to know what your competitors are doing to be competitive and gain more business. Getting insights regarding product prices, market trends, consumer behavior, competitor marketing strategies, and more is essential. The big question here is, how to get that information? The answer relies on eCommerce data scraping. Today, in this detailed post, we will learn how to gather data from eCommerce platforms, scrape eCommerce data, useful tools, best practices to follow, and use cases.

What is E-Commerce Web Scraping?

E-commerce web scraping is finding, crawling, and extracting helpful product data insights for analysis and making data-driven business decisions. eCommerce web data scraping has become very important for brands to stay competitive and enhance business growth in the digital marketplace sector. Automated data scraping allows brands to quickly gather precise and real-time information regarding competitor product pricing, offers, business strategies, consumer reviews, latest market trends, customer purchase behavior, and more.

eCommerce web scraping also helps to obtain information that is impossible to acquire manually, such as dynamic and historical data. Analyzing this data helps businesses build effective pricing and marketing strategies and stay one step ahead by making informed business decisions.

Cost Saving and Efficiency

Adopting automated data scraping and analysis methods for eCommerce data collection saves ample time compared to manual processes. Businesses can seamlessly manage large chunks of data on multiple eCommerce platforms. When it comes to reporting, brands can easily generate customized reports for specific business requirements.

Revenue Optimization

Track and monitor market competitors to identify their dynamic pricing strategies. Utilizing an ecommerce web scraper to collect vital information related to products and its everchanging prices helps brands modify product prices and maintain stock levels per the market trends and demands.

This will lead to effective pricing optimization, which will result in increased revenue by delivering better services and offers to consumers.

Strategic Business Partnerships

With effective web scraping eCommerce data, find out and analyze the sector's top-performing suppliers. Having reliable supplier data provides opportunities to tie up with new retailers to improve the overall product range, increase the consumer base, and enhance market penetration.

Improve Brand Reputation

eCommerce product review data scraping and analysis helps businesses to know consumer demands, product reviews, and suggestions on their own and competitor products. This data helps to identify loopholes in the products, consumer opinions, and upcoming trends to improve product sales and brand reputation.

Product Content Enhancement

Having complete product data handy helps ecommerce brands to compare and find missing information in their product details. By knowing what is missing, companies can fill the content gap and introduce new material to provide better information than top market performers.

Better Decision Making

Business intelligence tools can analyse scraped eCommerce data to better predict future trends and consumer requirements. Combining business intelligence and real-time data helps benchmark business performance, mitigate risks, and make profitable business decisions.

Product Performance Analysis

eCommerce web scraping collects various product details from multiple listing platforms. This data helps recognize how well your products perform in the marketplace. Find out the least performing products and take the necessary steps to make them perform better.

Profitable Marketing Strategies

Use scraped eCommerce data to build effective marketing campaigns for your in-demand products, improve cross-selling, and promote offers. With the help of user data, brands can create targeted promotional activities. Analyzing competitor data helps improve SEO optimization. Brands can also focus on ad cost optimization to make spending effective.

Continuous Improvement

In the ever-changing eCommerce landscape, accurate data helps brands update listing details continuously. Keeping the product details up-to-date helps deliver the latest information to consumers, stay competitive in the market, and improve product performance, which results in enhanced profit.

eCommerce Web Scraping Use Cases

Price Tracking and Monitoring

Track, monitor, and get real-time product pricing data with effective eCommerce web data scraping across multiple platforms. Companies can find competitor product prices, offers, discounts, and more with reliable pricing data. Real-time pricing alerts help to optimize prices quickly to stay competitive in the market. With pricing intelligence services, brands can know historical prices and predict future trends to provide better deals to their consumers.

Product Intelligence

Product intelligence assists businesses to monitor and optimise various important product details. These details include product description, title, meta, features images, specification, and more. With product intelligence, brands can find out exact or similar match product in the marketplace to identify core competitor of their products. Product optimization helps to improve the product performance, better ranking, and beat the competition.

Sentiment Analysis

eCommerce web scraping provides reliable data of user rating and reviews to run the sentiment analysis. These data help brands to know what customers are saying regarding their and competitor products, their requirements, tips to improve product, positive and negative points, and more. With the accurate sentiment analysis, companies can make changes to refine the product, introduce new product, find out new business opportunities, optimize product performance, and enhance shopper’s experience.

Competitor Monitoring

Scrape eCommerce data to collect vital data insights from your competitors. Real-time data updates help to reveal competitor marketing strategies, product performance, price changes, reviews and ratings, stock availability, and more. Track and monitor competitor activities to stay one step ahead in the sector.

Brand monitoring

Enhance brand reputation in the market by performing various analyses on the data gathered via eCommerce data scraping. Data insights will showcase areas for improvement, present opportunities to enhance the business, help in reputation monitoring, counterfeit detection, overall market position, and more. Enhancing the brand value of a business or product will lead to more online footfalls, business growth, and increased profit.

Digital Shelf Analysis

eCommerce data has proven helpful in optimizing online product shelfs to enhance business and fulfil consumer needs. With comprehensive data, business can monitor the shelf performance in real-time along with prices, track promotion effects, optimize product arrangement for better cross selling, better inventory management, perform the assortment, and more.

What are the Steps to Scrape E-Commerce Data using Python?

Setup the Project

Need to download and install Python. You can download it from here

Once Python is installed, setup Beautiful Soup, pandas, and request with below command.

mkdir scraper 
pip install beautifulsoup4 
pip install requests
pip install pandas

Import Libraries

Import libraries with the following code.

import requests
from bs4 import BeautifulSoup
import pandas as pd

Set the Base URL of the Main Page

This is important to avoid Python blockage by declaring a variable to save user-agent.

baseurl = "https://www.thewhiskyexchange.com"
                        headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'}

Investigate the Page & Make HTTP Call

By investigating the page, we can find where the product links are on the page to get them. Open Chrome dev tool to inspect.

Create script to access each one and generate URL. Make HTTP call first to scrape li element with Beautiful Soup

k = requests.get('https://www.thewhiskyexchange.com/c/35/japanese-whisky').text
soup=BeautifulSoup(k,'html.parser')
productlist = soup.find_all("li",{"class":"product-grid__item"})
print(productlist)

Get HTML for Products and Scrape from Productlist

productlinks = []
for product in productlist:
        link = product.find("a",{"class":"product-card"}).get('href')                 productlinks.append(baseurl + link)

Create Loop for All the Pages

productlinks = []
for x in range(1,6):  
 k = requests.get('https://www.thewhiskyexchange.com/c/35/japanese-whisky?pg={}&psize=24&sort=pasc'.format(x)).text  
 soup=BeautifulSoup(k,'html.parser')  
 productlist = soup.find_all("li",{"class":"product-grid__item"})

    for product in productlist:
        link = product.find("a",{"class":"product-card"}).get('href')
        productlinks.append(baseurl + link)

This code will help to get all the links present on the platform.

Extract Product Information from Pages

Here we are going to scrape the date related to product name, price, about, and ratings.

data=[]
for link in productlinks:
    f = requests.get(link,headers=headers).text
    hun=BeautifulSoup(f,'html.parser')

    try:
        price=hun.find("p",{"class":"product-action__price"}).text.replace('\n',"")
    except:
        price = None

    try:
        about=hun.find("div",{"class":"product-main__description"}).text.replace('\n',"")
    except:
        about=None

    try:
        rating = hun.find("div",{"class":"review-overview"}).text.replace('\n',"")
    except:
        rating=None

    try:
        name=hun.find("h1",{"class":"product-main__name"}).text.replace('\n',"")
    except:
        name=None

    whisky = {"name":name,"price":price,"rating":rating,"about":about}

    data.append(whisky)

This code will help to collect information…

Product Name – Under H1 Tag

Product Details – Under div Tag

Product Price – Under p Tag

Product Rating – Under span Tag

Make Data Presentable

Use pandas to make the extracted and stored data visible in proper format.

df = pd.DataFrame(data)

print(df)

Complete Code

import requests
from bs4 import BeautifulSoup
import pandas as pd

baseurl = "https://www.thewhiskyexchange.com"

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'}
productlinks = []
t={}
data=[]
c=0
for x in range(1,6):
    k = requests.get('https://www.thewhiskyexchange.com/c/35/japanese-whisky?pg={}&psize=24&sort=pasc'.format(x)).text
    soup=BeautifulSoup(k,'html.parser')
    productlist = soup.find_all("li",{"class":"product-grid__item"})


    for product in productlist:
        link = product.find("a",{"class":"product-card"}).get('href')
        productlinks.append(baseurl + link)


for link in productlinks:
    f = requests.get(link,headers=headers).text
    hun=BeautifulSoup(f,'html.parser')

    try:
        price=hun.find("p",{"class":"product-action__price"}).text.replace('\n',"")
    except:
        price = None

    try:
        about=hun.find("div",{"class":"product-main__description"}).text.replace('\n',"")
    except:
        about=None

    try:
        rating = hun.find("div",{"class":"review-overview"}).text.replace('\n',"")
    except:
        rating=None

    try:
        name=hun.find("h1",{"class":"product-main__name"}).text.replace('\n',"")
    except:
        name=None

    whisky = {"name":name,"price":price,"rating":rating,"about":about}

    data.append(whisky)
    c=c+1
    print("completed",c)

df = pd.DataFrame(data)

print(df)

Which are the Tools used to Scrape E-Commerce Website?

Scraping e-commerce websites involves extracting data such as product details, prices, and reviews, often using open-source tools that provide robust functionality. Popular tools include BeautifulSoup and Scrapy, both written in Python. Other notable tools are Selenium, which automates browsers and is useful for dynamic content rendering, and Puppeteer, a Node.js library for headless Chrome automation.

Selenium

Selenium is a browser automation tool created to test web applications. It automates all the activities that need to be performed on the application for the QC process. This tool has been utilised widely for web data scraping and automated browsing activities.

Selenium is open-source tool which works with most of browsers. Just install the package and WebDriver will work for the desired actions to automate any browser. Selenium is able to extract data from multiple dynamic platforms.

Scrapy

Scrapy is a Python-supported open-source data crawling and scraping tool capable of fetching data from APIs. Data experts utilize Scrapy for data mining, monitoring, and test automation on extracted data.

If you want to scrape structured data from platforms with steady structure, Scrapy is the best choice. This tool also helps extract large chunks of data from more than one platform at a time. To use Scrapy, you need a Python 3.8+ version.

If you want to scrape content from platforms that showcase data through JavaScript rendering, Scrapy might not work as expected.

80legs

With 80legs, users can build and run web data crawlers via its SaaS platform to scrape data efficiently. As it relies on a grid-based computer network (50k individual systems across the world), there is no issue related to bandwidth cap while scraping the data.

This tool is suitable for small businesses or individuals whose data requirements are not huge as it scrapes data in a limited manner. You can select multiple payment options according to your service and data needs.

Octoparse

Octoparse is free data scraping tool comes with easy-to-use interface. This tool can be used on both Windows and Mac to scrape ecommerce data from any platform. The best thing about octoparse is that a non-technical person can also easily perform the data extraction process. Huge amounts of data can be scraped in quick time with the help of it’s auto-detect algorithm.

There are many more tools available that can be used according to your data requirements. If you have a long-term data requirement, it is advisable to look for eCommerce Web Scraping Companies that can provide effective and professional data extraction services from multiple platforms quickly. These tools provide flexibility, community support, and integration capabilities, making them favorites among developers for e-commerce scraping projects.

What are the Best Practices for Scraping an e-commerce Website?

While extracting data from eCommerce platforms, follow ethical guidelines and adopt best practices. It is very important to stay in line with legal guidelines for data scraping. Let's see what best practices need to be followed while scraping data.

Read the Platform’s Terms of Services

It is important to read the website’s terms to determine if there are any restrictions regarding data scraping.

Robots.txt

It is essential to check robots.txt to know if there are any certain restrictions for data extraction.

Apply Rate Limits

Always remember that while scraping the data don’t overload the server of targeted platform. Limit the data scraping requests by applying rate limits.

Usage of Proxies

To save yourself from IP blocking, use proxies to change IP addresses frequently.

Manage Dynamic Content

eCommerce platforms extensively use AJAX or JS for better performance. Select a tool that can work with the dynamic content loading mechanism.

Is it Legal to Scrape E-commerce Website Data?

Yes, it is legal to scrape data from an eCommerce platform if it is publicly available and extracted by keeping the platform’s data usage policy in mind. As stated above, it is mandatory to follow ethical methods to grab data from any platform.

Not following the guidelines or breaking the rules may result in legal action against the business. It is essential to check the following laws before initiating the data scraping process on any online marketplace.

Copyright Laws

Data can be protected with the copyright. Scraping and using protected data without consent can create the legal trouble for your business.

GDPR Laws

GDPR is the General Data Protection Regulation, which protects users private data worldwide. Make sure you are not extracting or using any personal information without the permission of that entity.

CFAA Laws

The Computer Fraud and Abuse Act applies if an unauthorized attempt is made to access the data present on the web platform. This could lead to serious legal procedures regarding data breaches.

Conclusion

In this eCommerce web scraping guide, we became familiar with how to scrape eCommerce data, tools, benefits, use cases, and more. eCommerce web scraping is becoming popular daily as it provides significant benefits to brands. Select the best tool or adopt professional services for effective eCommerce web scraping. We at Scraping Intelligence provide effective and customized eCommerce data scraping services to fulfill your data requirements by providing accurate data insights. Connect with our expert to know more.