There has been a steady rise in the number of ecommerce websites. And if you guessed that they are an excellent data source- you guessed right!

Since data has become instrumental in businesses, it has become essential to discuss ecommerce data scraping. Ecommerce data refers to any data found on ecommerce websites and marketplaces. 

Most information on ecommerce websites is available to the public. Some information available via e-commerce data scraper includes:

  • Customer data such as demographics, purchasing habits and queries. 
  • Product data, including price, sales, reviews, availability, and vendors, are also available on the websites. 
  • In addition, you can find information on transactions like taxes, shipping costs, and payment methods. 

Many organizations use ecommerce data scraping as a source of financial data. This guide will explore how ecommerce data scraping works, its use cases, best practices, and ethical issues, as well as optimizing it with NetNut Proxies. 

How Does Ecommerce Data Scraping Work?

How Does Ecommerce Data Scraping Work?

Ecommerce data scraping is the automated process of identifying, collecting, and storing data from ecommerce websites. Subsequently, ecommerce data scraping involves the use of scrapers. Here is an overview of how to use them:

Project setup

Python is one of the best languages for web scraping. For the sake of this guide, let us imagine we are working with Python to build an ecommerce data scraper.

The first step is to download and install Python. Remember to create a folder to store the codes and other necessary tools to build an ecommerce data scraper.

Install libraries

Python’s vast libraries make it a top programming language for ecommerce data scraper. These libraries are critical because they play significant roles in extracting and parsing the HTML content of the website. You may require three common Python libraries: Requests, Beautiful Soup, and CSV.

The Request library is essential because it makes HTTP requests simpler and more human-like. Any web scraping code written in Python requires Requests to function. The request function is a direct line of communication between your scraper and the ecommerce website.

BeautifulSoup is another popular Python library often used in web scraping activities. Its unique role is parsing HTML and XML documents for efficient data scraping. 

On the other hand, the CSV library helps to read and write tabular data in Excel or CSV formats. This choice depends on where you want to save your extracted data. For the sake of this guide, we are using the CSV library because we want to store the extracted data in a CSV file.

Extract ecommerce data with the script

This is the stage where you use the ecommerce data scraper bot. Ecommerce data scraping involves parsing the HTML content with a library such as BeautifulSoup. Parsing involves analyzing the HTML structure of a website to understand its elements. 

Additionally, you can scrape data from multiple pages by setting a queuing command. To do this, identify anchor tags (<a>) that contain links to additional pages. Then, add these links to a queue before you begin the ecommerce data scraping process on the website. The process continues until the queue is empty and all the pages have been scrapped. 

Save the scraped data

The stage of data storage is one of the less confusing aspects of ecommerce data scraping. However, you need to create a list for storing the data. Here are some of the Python codes. Remember that the retrieved data can be transferred into Excel or saved in JSON or CSV format.

Use Cases of Ecommerce Data Scraping 

You are probably wondering, how can my business benefit from ecommerce data scraping? The good news is that ecommerce data scraping can take your business to the next level. Here are some of the most popular use cases:

Price monitoring

One of the applications of ecommerce data scrapers is price monitoring. The goal of every ecommerce business is to make a profit, which is impossible without sales. Since the ecommerce sector is highly competitive, customers can quickly check two or more sites and compare their prices before settling on one. 

Therefore, it becomes necessary for brands to offer competitive prices that will optimize conversion and sales. Ecommerce data scraping allows you to monitor the average prices of a particular product within a period as well as the customer’s buying reactions to them. This prevents you from selling a product at a very high price, which makes potential customers patronize your competitors.

In addition, price monitoring can provide useful insight on where to invest your money. For example, suppose a particular product category keeps experiencing an aggressive price decline. In that case, you may need to focus on another product so your net profit is unaffected.

Trend monitoring and demand forecasting

 Ecommerce data scraping makes trend monitoring and demand forecasting possible. It is not about collecting the data but predictive analysis to identify patterns and user attitudes. 

 The use of targeted ecommerce data scrapers makes trend monitoring more effective. It includes using specific keywords to collect data on new categories, customer comments, or best sellers. This information provides useful insight into trends and market movement. 

For example, if you want to launch a new product, you must first research to determine if it will address people’s pain points.

Since ecommerce data scraper brings a huge amount of data to your fingerprints, it is useful for demand forecasting. Therefore, with ecommerce data scraping, you can determine the best period to launch the new product.

Remember that even if a concept or product is not currently trending, it does not cancel its operations. Certain products tend to be in higher demand during some seasons. Therefore, if a product manages to be relevant throughout the various seasons of the year, it may be a good place to venture as demand is great.

Another usefulness of ecommerce data scraping is that it provides insights into how many products you need to keep in store for optimized availability in various locations.

Monitoring competition 

Another significant application of ecommerce data scraping is monitoring competition. Before making concrete digital marketing strategies, you must understand how the industry works and what your competitors are doing. 

Therefore, ecommerce data scrapers allow you to analyze the important aspects of your competitors to understand what areas you need to work on. With ecommerce data scraping, you can identify the latest marketing strategies used by your competitors. In addition, you can analyze this data to identify how competitors promote their brands as well as the public reaction.

Furthermore, ecommerce data scraper allows you to keep track of competitors’ prices and any changes to their pricing model. This is possible because ecommerce data scraping can deliver real-time price updates.

Another aspect of using ecommerce data scraping for monitoring competition is to find out the customer’s response to certain offers, promotions, and discounts. Therefore, all the information from ecommerce data scrapers arms you with enough fuel to launch your ecommerce business to the next level.  

Sentiment analysis 

 Ecommerce data scraping allows you to grasp the concepts of Sentiment analysis. This involves getting in-depth information on how the public reacts to your brand. It involves using ecommerce data scraper to collect customer reviews from various parts of the world. Subsequently, this allows you to respond quickly and tackle any negative issues.

Ecommerce data scraper saves you the time to manually search, read, and organize thousands of reviews. Instead, the data is delivered to you in a readable format, and you can use various data analysis methods to represent the overall sentiment toward your ecommerce business.

Brand monitoring 

Brand monitoring is another significant use case of ecommerce data scraping. With the increase in ecommerce activities, malicious individuals are also constantly producing counterfeit items. A counterfeit, according to the IACC, is an item that uses someone else’s trademark without their permission with the aim to profit from the owner’s reputation. 

Counterfeit products are often of inferior quality, which damages the brand’s reputation. In addition, it significantly affects sales as they are often cheaper to lure more customers. Solving counterfeiting issues may cost your business thousands of dollars.

 Therefore, ecommerce data scraping becomes paramount to monitor the brand’s reputation. Identifying these counterfeits helps you stop them before they permanently damage your brand. 

Lead generation 

Every ecommerce business thrives on getting new leads. These leads are then nurtured into potential customers. However, the traditional channels for generating leads are often time-consuming and expensive, depending on your needs.

Ecommerce data scraping is an efficient way to collect contact information of potential customers, create an email list and launch an outreach campaign. 

In addition, ecommerce data scraper exposes you to techniques that other companies in the industry use to generate leads. An example of a strategy that can generate leads is offering free shipping. This increases the chances of sales as people generally don’t like to spend unnecessary money.

Challenges Associated with Ecommerce Data Scraping 

Challenges Associated with Ecommerce Data Scraping 

 As important as ecommerce data scraping, you may encounter some challenges. They include:


CAPTCHAs are one of the popular challenges for ecommerce data scrapers. Some websites require you to complete a CAPTCHA challenge before accessing them. On the other hand, CAPTCHA may serve as an anti-scraping measure to verify that visitors are humans. This technique is used to systematically reduce the amount of bot crawling a website. 

CAPTCHAs are a big challenge because they require humans to solve them. However, some ecommerce data scrapers integrate CAPTCHA solvers to bypass this limitation.


Cloaking is another measure that can limit ecommerce data scraping. It is an anti-scraping measure and data protection technique. Cloaking, in simple terms, describes hiding something. Therefore, some websites may employ this technique to feed scraper bots, once identified, false data. 

In addition, cloaking as a limitation to ecommerce data scrapers may redirect the bot to irrelevant links. The use of cloaking measures to limit ecommerce data scraping is surrounded by some controversies. It is considered a violation of Google’s Webmaster Guidelines, so it is illegal. Subsequently, suppose a website uses cloaking to limit ecommerce data scraping. In that case, it may be banned from a Google Search Engine Result Page.

Interface changes

One of the challenges to ecommerce data scrapers is interface changes. Many ecommerce websites alter their fonts and formats to optimize customer experience. However, this can pose a significant challenge to ecommerce data scraping activities. 

Since they contain dynamic content, your scraper may not be able to interact with the page. Subsequently, you would need to write a new script that matches the new format so it can effectively Parse the HTML content on the ecommerce website.

IP bans

One of the primary challenges of ecommerce data scrapers is IP bans. This can happen when the target website identifies you as an unwanted bot. They then block your IP address to ensure optimal data privacy. 

You may experience IP bans during ecommerce data scraping when you send many parallel requests – the same request to a website multiple times per second. 

Another reason for IP blocks during ecommerce data scraping is when you make too many requests from the same IP address. The implication of having your IP address blocked is you can no longer do ecommerce data scraping on that website. However, ecommerce data scraping tools can use measures like delaying requests to avoid IP bans.


Honeypot is another challenge regarding ecommerce data scraping. They describe traps implemented by the website administrator to identify web scrapers. Your IP address is captured and blocked once the ecommerce data scraper is discovered. 

A typical example of a honeypot is a hidden URL on a website that is not visible to humans. However, bots can identify and follow the link as part of ecommerce data scraping activities. Unfortunately, this will reveal your IP address to the website.

Best Practices for Ecommerce Data Scraping

Read the web page robots.txt

Before you begin ecommerce data scraping, ensure you read the robot.txt file. This helps you familiarize yourself with specific data that you can scrape and those you should avoid. Subsequently, this information helps guide you in writing the code for the web data extraction activity. 

Terms and conditions/ web page policies

Another great tip for optimizing ecommerce data scraping is reviewing the website policy or terms and conditions. Many people overlook the policy pages because they often align with the robot.txt file. However, there may be additional information that can be relevant to your web data extraction activities. 

Avoid sending too many requests

There are two primary dangers of sending too many requests to a website. First, the site may become slow, malfunction, or even crash. Secondly, the website’s anti-scraping measures are triggered, blocking your IP address. 

Use proxy servers

One of the best tips for effective ecommerce data scraping is using proxy servers. One of the primary challenges of ecommerce data scrapers is blocked IP. However, you can avoid this limitation via IP rotation.

Enhancing The Effectiveness of Ecommerce Data Scraping with NetNut

NetNut is an industry-leading proxy provider you can partner with for your ecommerce data scraping needs. Using NetNut proxies ensures that every ecommerce data scraping request is with a different IP address for efficient IP block bypass. In addition, you don’t have to worry about CAPTCHAs or incidental aggressive scraping.

NetNut has an extensive network of over 52 million rotating residential proxies in 200 countries and over 250,000 mobile IPS in over 100 countries, which helps them provide exceptional data collection services.

NetNut rotating residential proxies are your automated proxy solution that ensures you can access websites despite geographic restrictions. Therefore, you get access to real-time data from all over the world that optimizes decision-making.

Alternatively, you can use our in-house solution- NetNut Scraper API, to access websites and collect data. Moreover, you can use NetNut’s Mobile Proxy  if you need customized web scraping solutions. 


Congratulations on reading to this point. You are well on your way to effective and successful ecommerce data scraping. Ecommerce data scraping has several use cases, including lead generation, price monitoring, sentiment analysis, and more. 

Ecommerce data scraping is legal and ethical, provided you only scrape publicly available data. However, ecommerce websites often implement anti-scraping techniques like honeypots, CAPTCHA, and IP bans.

However, NetNut offers an inhouse scraping solution that is flexible and customizable. Always use proxies for ecommerce data scraping activities to ensure uninterrupted real-time data updates.

Kindly contact us if you have any further questions! 

Frequently Asked Questions

Is it ethical to conduct ecommerce data scraping?

Ecommerce data scraping is ethical if you follow good practices that respect the rules on the website. Avoid excessive scraping that may cause overloading of the ecommerce website such that it may malfunction. 

In addition, be sure to read the website’s Terms of Service and the robot.txt file to avoid breaching any laws. It is very important to use the data appropriately to avoid plagiarism or breach of data privacy.

What are the factors to consider when choosing an ecommerce data scraping tool?

Choosing the right tool ensures effective ecommerce data scraping, and factors to consider include the following:

  • Select an ecommerce data scraping tool that is easy to use
  • The tool should offer flexibility to allow for customization to target specific elements.
  • Choose an ecommerce data scraping tool that is scalable- it must handle huge amounts of data effectively.
  • Pricing is another factor to consider for ecommerce data scraping.
  • When choosing an ecommerce data scraping tool, opt for providers with responsive customer support and comprehensive documentation.

What is the legal status of ecommerce data scraping?

The legal status of ecommerce data scraping falls back to the kind of data you are collecting. Extracting publicly available data is legal, and most e-commerce websites display product categories, reviews, and pricing information.

However, ecommerce data scraping becomes illegal when you scrape content behind login walls. In addition, plagiarizing data derived from ecommerce data scraping can lead to legal consequences. Instead of displaying them as yours, you can use them for optimized decision-making.

Ecommerce Data Scraping For Your Business- NetNut
Full Stack Developer
Ivan Kolinovski is a highly skilled Full Stack Developer currently based in Tel Aviv, Israel. He has over three years of experience working with cutting-edge technology stacks, including MEAN/MERN/LEMP stacks. Ivan's expertise includes Git version control, making him a valuable asset to NetNut's development team.