How to Scrape Rakuten Product Data Easily: A Complete Guide for 2026
Rakuten is one of the leading eCommerce platforms in Japan and across Asia, offering a wide range of products from electronics, fashion, beauty, home appliances, and more. With millions of products listed and a vast user base, Rakuten has become a valuable source of product data for sellers, marketers, and researchers looking to analyze market trends, pricing strategies, and consumer preferences.
In this blog, we will discuss how you can scrape Rakuten product lists easily and extract them in a structured format like Excel, CSV, or JSON to gain actionable insights for your eCommerce business.
Why Scraping Rakuten Product Data is Useful
Rakuten has millions of monthly users and thousands of sellers, making it a goldmine of eCommerce data. By scraping product data from Rakuten, you can:
- Analyze product trends: Understand which products are popular among buyers and which categories are gaining traction.
- Monitor competitor pricing: Stay competitive by tracking the prices of similar products sold by other sellers.
- Optimize your product listings: Discover keywords, product titles, and descriptions that attract more buyers.
- Make data-driven decisions: Use insights from Rakuten product data to plan your inventory, marketing, and sales strategies.
For sellers, this data can directly impact your revenue by helping you list in-demand products and set competitive prices. For researchers or marketers, it provides a detailed look into market trends and consumer preferences.
Does Rakuten Allow Web Scraping?
One common question that comes up is whether Rakuten data extraction is legal. While Rakuten does not have a publicly stated restriction against scraping, it is essential to scrape only publicly available data. Avoid scraping sensitive user information or restricted content, as this could violate Rakuten’s terms of service.
You can scrape all publicly visible product information, such as:
- Product name and description
- Brand and seller information
- Category and subcategory
- Product price
- Product reviews and ratings
- Images and videos
- Product URL
- Shipping details and availability
By collecting this data, you can analyze the market, identify top-selling products, and understand customer preferences. If you are a seller, this information is invaluable for planning inventory, identifying trending products, and improving your listings to attract more buyers.
Methods to Scrape Rakuten data
There are several ways to scrape Rakuten, depending on your technical skills. Whether you prefer professional services like TagX, no-code tools, or custom Python scripts, you can extract product data efficiently and in a structured format.
1. Scrape Rakuten Without Coding
If you are not familiar with programming, you can use professional services like TagX or web scraping tools such as Octoparse, ParseHub, and Data Miner. These options allow you to scrape Rakuten product data quickly and efficiently, without writing any code.
- TagX: Offers professional Rakuten scraping services, delivering structured, accurate, and ready-to-use product data in your preferred format, saving time and effort.
- Octoparse:t User-friendly tools for scraping product data. It provides an auto-detection feature to identify product listings and allows you to export the data in Excel, CSV, or JSON.
- ParseHub: A popular tool that supports complex scraping tasks, including handling dynamic content on Rakuten.
- Data Miner: A Chrome extension that can scrape data directly from web pages into spreadsheets.
These tools and services are perfect for sellers, marketers, or researchers who want structured Rakuten product data quickly and efficiently, without the need for coding
2. Scrape Rakuten Using Python
For those comfortable with coding, Python provides a flexible and efficient way to scrape Rakuten. Popular libraries include:
- BeautifulSoup: Helps parse HTML content and extract product data easily.
- Requests: Allows you to send HTTP requests and get the HTML response from Rakuten pages.
- Selenium: Useful for scraping dynamic content loaded with JavaScript.
With Python, you can create a custom scraper to extract product names, prices, reviews, images, and URLs. This method provides more control over data extraction, automation, and storage.
Tips to Scrape Rakuten Product List Efficiently
Here are some tips to make your Rakuten scraping experience smoother:
- Avoid overloading Rakuten servers: Use reasonable delays between requests to avoid being blocked.
- Scrape only necessary data: Focus on product information that provides actionable insights.
- Use cloud scraping for large datasets: Tools like Octoparse allow you to scrape millions of product listings without overloading your local machine.
- Keep your data organized: Use structured formats like Excel or CSV for easy analysis.
- Regularly update your scraper: Rakuten may change its website structure, so updating your workflow ensures uninterrupted scraping.
Benefits of Using Scraped Rakuten Data
By scraping Rakuten, you can gain several benefits for your business or research:
- Market insights: Identify trending products and seasonal demand patterns.
- Competitive advantage: Track pricing and product strategies of top sellers.
- Improved listings: Optimize your product titles, descriptions, and images based on popular listings.
- Data-driven marketing: Use product and pricing data to design effective promotions and campaigns.
- Inventory planning: Forecast demand based on popular products and top-selling categories.
For sellers on Rakuten, these benefits translate directly into higher sales and better visibility on the platform.
Common Challenges and How to Overcome Them
While scraping Rakuten is straightforward, there are some common challenges:
- Dynamic content: Some product details may load via JavaScript, requiring tools like Selenium or cloud-based scraping solutions.
- Captcha & blocking: Frequent scraping may trigger Captchas or temporary bans. Always use delays and rotate IPs if necessary.
- Website changes: Rakuten occasionally updates its website layout. Regularly check your scraper workflow to ensure it continues working.
- Data accuracy: Always validate scraped data to avoid missing or incorrect information.
Using advanced scraping tools like TagX’s scraping services can help overcome these challenges efficiently.
How TagX Can Help
At TagX, we provide professional web scraping services, including Rakuten data extraction. Our services are designed for businesses that want accurate, reliable, and structured data without investing time in learning coding or managing scrapers.
With TagX, you can:
- Extract product names, descriptions, images, prices, and reviews
- Access data for market research and competitive analysis
- Receive data in Excel, CSV, or JSON formats ready for analysis
- Save time and resources with automated scraping workflows
Whether you are a small seller or a large enterprise, TagX ensures you have the right Rakuten data to make smarter business decisions.
Conclusion
Scraping Rakuten product lists is a highly effective way to gain insights into eCommerce trends, competitor strategies, and customer preferences.By following the steps outlined above, you can collect Rakuten product data in a well-organized format and use it to optimize your listings, analyze trends, and boost your sales.
For businesses looking to save time and access high-quality data, TagX’s professional scraping services provide a reliable and efficient solution. All you need to do is define your data requirements, and TagX handles the rest, delivering clean, actionable data ready for analysis.
FAQs
1. What kind of Rakuten product data can I scrape?
You can scrape publicly available Rakuten data such as product names, prices, descriptions, seller details, categories, images, ratings, and product URLs. This information is useful for analyzing market trends and improving product listings.
2. Is it legal to scrape data from Rakuten?
Scraping publicly available data from Rakuten is generally allowed as long as you don’t access private user information or violate the platform’s terms of service. Always ensure your scraping activities comply with ethical and legal standards.
3. Can I scrape Rakuten using Python?
Yes. You can use Python libraries such as BeautifulSoup, Requests, and Selenium to scrape Rakuten. These libraries allow you to extract structured product data, automate scraping workflows, and handle dynamic web content.
4. Which Rakuten data extraction service is the best?
TagX is one of the best Rakuten data extraction service providers, offering accurate, clean, and structured datasets ready for analysis. With TagX, you can easily perform market research, monitor competitors, and optimize product listings all without the hassle of manual scraping or coding.
5. How accurate is the data collected from Rakuten scraping?
Data accuracy depends on the scraping method. Manual scraping can lead to missing fields, while professional services like TagX ensure high accuracy by validating, cleaning, and structuring the data before delivery.
