Table of Contents
- E-commerce Web Scraping
- Our experience
- The Benefits of E-commerce Web Scraping
- Why is Web Scraping Ideal for Product Information Extraction from E-Commerce Platforms?
- What Kind of Data Can You Scrape?
- E-commerce Sites Data Extraction Challenges
- Building a correct crawling path and collecting the necessary URLs
- Crafting an efficient scraper
- Creating a scalable architecture
- Taking anti-bot countermeasures
- Data Quality
- What’s Important to Consider When Scraping At Scale?
- Final thoughts
Introduction to Chinese Web ScrapingIn our modern, progressively digitized reality, every industry tends to become more and more data-driven, and the analysis of large amounts of data is especially crucial for the eCommerce sphere, which constantly grows and has no boundaries. To get insights from one of the largest markets in the world, you might need to scrape Chinese web, i.e. Alibaba, TaoBao, and other websites and apps.
How to Scrape Chinese WebWhen you gather data from various sources, you receive a crucial piece of competitive intelligence and the potential to win over the other market players in your field. Today, arming yourself with the necessary information is simple. Ecommerce data scraping completes this task quite effectively, and when it comes to fetching publicly available information from e-commerce giants like Amazon, large-scale web scraping is the best approach. Scraping websites on a large scale involves running multiple scrapers in parallel against one or more websites and extracting massive amounts of data. Speaking about web scraping at a large scale, we have plenty of cases to share, including a really huge project for a customer from Shanghai in product review scraping.
Chinese Web Scraping Real Case
- Tmall formerly Taobao Mall;
- Make sentiment analysis;
- Improve the products;
- Increase sales;
- Improve brand awareness;
- Enhance customer satisfaction.
Login requirementsTo login into the target sites, we needed a Chinese mobile phone number, so we had to get one and log in on the websites under a Chinese IP. PDD is a platform with only a mobile version, so we found a Chinese provider to enter the site under a Chinese IP as well.
Mobile app scrapingSince PDD is mobile-based only, we had to create a turnaround and scraped the platform with the help of a mobile app developed for this purpose.
CaptchasAlmost all of the sites we scraped had various captcha types for each page, most of which were quite sophisticated and in Chinese. As you know, the majority of the DataOx team is located in Ukraine, but we found a specialist who knows the language, and the most sophisticated captchas were solved manually by our Chinese-speaking colleague.
PaginationDepending on site scope and specifics, pagination may be used, but the great number of pages caused problems for our work. On Tmall, for instance, the pagination runs into a cyclic path after the 10th page. Thus, we had to scrape details in small groups, going from one product to another. On JD, we faced trouble with sorting after the 10th page. We only needed fresh reviews to scrape, but due to this issue, we scraped all the reviews and then sorted them out to take 100-200 fresh comments.
Data scopeAs we mentioned above, the scope of comments scraped in a session was numbered in the tens of millions. To manage all this data, we needed a dedicated system. The DataOx development team created a Kubernetes-based cluster using the Rancher system. The combination of those two technologies resulted in a quick and efficient data management system.
Design changesEven though we develop universal scrapers for our projects and only significant redesigns can interfere with its work, different coding of the pages became a challenge for us. Depending on the situation, we either used a smart parser or an instrument dealing with a specific page structure.
Data qualityData quality maintenance is always a challenge for extensive projects; but when you scrape information in Chinese, everything gets even more complicated. However, for our team, it was yet one more interesting task to complete, and we did it: we integrated a translator into our UI tech system.
Benefits of Ecommerce Web ScrapingWeb data scraping allows entrepreneurs to gather business intelligence quickly and efficiently while providing them with a bird’s-eye view of the market they operate in, including up-to-date business conditions, prevailing trends, customer preferences, competitor strategies, and challenges of lead generation. Through e-commerce websites, scraping businesses most often pursue the following aims:
Brand/reputation monitoringHuge e-commerce platforms are a perfect source for researching the consumer attitude toward a chosen brand, whether it’s your company or a product you are going to sell. Through the web scraping of eCommerce websites, you can literally be all ears to what your target and real customers say and complain about, thus detecting their pain points and addressing them in a timely manner.
Customer preferences researchDirectly listening to your consumers through reviews and feedback allows you to determine the crucial factors that drive sales in your market segment. By extracting and analyzing reviews with the right goals, your business can address its target audience’s needs, contribute to their satisfaction, garner more customers, and enhance sales.
Competitor analysisChecking your brand reputation and listening to the customer’s voice is not enough. By monitoring your competitors, you can spot the hanging fruits you failed to see earlier. Scraping competitor product reviews can help you detect customer demand for a particular feature and become a pioneer in incorporating it into your product or service.
Fraud detectionCounterfeit goods are a threat to brands, influencing not only sales but also damaging brand reputation when a customer does not realize he’s got a fake. By scraping e-commerce sites for reviews, you can spot hints of ongoing fraud or identify partners/competitors who do not stick to their agreements. Web data scraping is an ideal solution to access a massive amount of product information and reviews all at once. Let’s find out why.
Why is Web Scraping Ideal for Product Information Extraction from Ecommerce Platforms?When you need information about the product you are going to market, it’s impossible to manually extract all the details and reviews due to the enormous scope of data available. Plus, such work makes information prone to human errors, while automated data extraction is much faster, more efficient, and works at a large scale. Check out how to take data from a website below. A software tool is able to browse thousands of product listings and capture the necessary details – pricing, a number of variants, reviews, or something else – in a matter of hours. What’s more, scraping technology allows extracting details, which are invisible to a user’s eye or protected from common copy-pasting. Another benefit of a technology solution is saving data into readable and meaningful formats convenient for processing and analysis.
What Kind of Data Can You Scrape?The type of data you scrape is predetermined by your aims, so to scrape data from an e-commerce website and benefit from it, you need to understand the web data and the goals you set. Let’s take a common e-commerce platform like Amazon. From it, we can scrape:
- Product URL
- Name of a product
- Item description
- Stock details
- Image URL
- Average rating
- Product reviews
Chinese Ecommerce Sites Data Extraction ChallengesAs we’ve mentioned above, sites don’t like being parsed; their development teams and website admins do their best to prevent information from being extracted. However, a good web scraping specialist always knows what to do. Awareness of common data scraping challenges allows you to automate and improve certain parts of the process using various digital solutions powered by machine learning technology or artificial intelligence. Common, well-known obstacles to smooth scraping include:
- Webpage design and layout changes;
- Unique elements usage;
- Anti-scraping technologies utilized;
- HoneyPot traps;
Building a correct crawling path and collecting the necessary URLsWhen dealing with multiple products from an e-commerce site, you need to accurately build a crawling path and a URL library for data extraction. All the necessary URLs should be considered, and identified as important for your case to be parsed, and scraped later.
Crafting an efficient scraperThe tip of the iceberg here is choosing the right language and API along with the framework and another technology stack. Then, infrastructure management and maintenance should be considered, as well as anti-measures for fingerprinting and site protection. Though you may be tempted to develop separate spiders for each site, our best practice and advice is to have one bot developed with all the rules, schemes, page layouts, and nuances of target sites in mind. The more configurable your tool is, the better, although it may be complex, it will be easier to adjust and maintain in the future.
Creating a scalable architectureWhen it comes to e-commerce, there is no doubt that the number of requests will increase as soon as you scale your project further on. Your crawling infrastructure will require scaling, as well. Thus, you need to develop the architecture in such a way that it can scrape millions of requests a day without a decrease in performance.
How to scrape Chinese web well?First of all, you need to make sure that your tool can detect and scrape all the necessary product pages in the time set (often one day) and to it, you should:
Most Common ErrorsThe most common data errors that we encounter in our projects are:
- Data validation errors;
- Coverage inconsistency;
- Product details errors.