How To Scrape Pricing Data Successfully?

How To Scrape Pricing Data Successfully

Over 80% of consumers consider the price of an item before making a purchase decision. Today most businesses are selling online. Consequently, it’s become easy for consumers to compare different offers and pick the best deal. This process has become even easier thanks to price comparison platforms such as Google shopping.

Customers no longer need to walk from one store to another to check prices. They do not need to browse different eCommerce sites either. They can check offers by different retailers and compare prices of products on Google shopping from the comfort of their homes.

This tells us that even though it’s easy for any retailer to open an online store, the competition is cutthroat. The challenge to online sellers now is to set an attractive pricing point to win consumers and beat their competitors without sacrificing profits.

How do you devise a competitive price that gives you an edge over competitors? The answer lies in pricing data scraping.


The Importance of Pricing Data Scraping

The Importance of Pricing Data Scraping

For once, Google shopping carries a lot of product data from different online retailers. Sellers list their products alongside the market prices, offer prices, descriptions, and customer reviews. It will be helpful for any business seeking to monitor what their competitors are doing to get their hands on this data. Thankfully, it’s easy to obtain this data using a robust third-party Google shopping API.

Consider Google Shopping API by SERPMaster, for instance. This tool integrates with your browser, enabling you to extract real-time pricing data from Google shopping. Real-time price monitoring is crucial since retailers change their prices often and you need up-to-date data to make meaningful decisions.

Price scraping enables companies to collect high volumes of custom data sets fast and at a pocket-friendly budget. They can now analyse this information and use the insights to advise their pricing strategy. Besides, the business will also come across other important information when collecting pricing data. They can identify new competition in the market, competitor product categories, how competitors design their campaigns, and other pricing intelligence.

Google scraping for pricing data is easy to automate especially when using Google shopping API. There’s no need for manual copy-pasting and your team can concentrate on other business tasks. Also, automation reduces errors and it’s easy to collect huge amounts of data for analysis with minimal effort.


Challenges associated with price scraping

No business wants its competitors to learn about their secrets easily. That’s why most platforms have made it hard to collect pricing data from their websites. Here are some challenges you’ll come across when trying to extract pricing data.

1. Anti-scraping technology

Some websites invest in anti-scraping bots and other measures to bar online users from collecting data on their pages. Such techniques include IP blocking, using captcha, and bot detection. You may request the websites to allow you to collect the data, but that depends on them. Use the following ideas to work around anti-scraping techniques:

  • IP Blocking – consider using proxies and IP rotation to access such websites.
  • Captcha –  you’ve seen websites that ask a user to solve logical problems before accessing a page. This is how a captcha distinguishes between a bot and a human. Consider a captcha-bypassing open-source tool or use web scrapers with an inbuilt capacity to bypass this challenge.

2. Data Quality

Data quality and accuracy can be a challenge for most web scraping projects. Remember, it’s hard to achieve effective decisions if the data collected wasn’t accurate. It thus becomes necessary to fill text fields properly and make sure the harvested data matches the pre-defined template. To avoid errors, some teams go over the fields manually to confirm the data. However, it could help to use a data extraction tool that can automatically do quality tests before saving the info.

3. Changes in website structure

E-commerce site owners modify their platforms frequently to give a better user experience. However, these changes catch web scrapers off-guard, especially since most tools are accustomed to a certain structure. They'll need to set up the web scraper to match the current web page structure. You’ll either need to use a versatile tool or ensure constant monitoring to adjust the tool with the prevailing website setup.

4. Unique HTML Codes

It’s necessary to extract data from thousands of pages to make a meaningful analysis. Unfortunately, HTML coding on different pages can be different. As a result, the parsers have difficulties making sense of information on some pages. As a solution, you either scan the entire website and find the different codes and customise your tool to match.


Incorporate Artificial Intelligence (AI) into eCommerce price scraping to overcome data mining challenges

AI into eCommerce

Websites are becoming savvy at protecting their data. It means most measures we’ve suggested above may not work efficiently to overcome data scraping issues. Fortunately, you can enhance your data collection with AI-based web-scraping. Artificial intelligence is a sure way to successfully bypass anti-scraping technologies and boost the efficacy of your proxies.

AI is the secret behind third-party tools that simulate human behaviour to bypass anti-scraping technologies. With AI, it’s possible to make traffic coming from a scraper appear as though it’s 100% human. This way, you avoid both IP blocking technologies, captcha, and bot detection thus enabling hassle-free data mining.


Wrapping up

Coming up with a competitive pricing strategy isn’t a walk in the park. As many businesses are now selling online, you want to design your offers and prices in a way to attract a fair share of the market. Use online data to your advantage and create a pricing strategy that works for your business.

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