The Power of Web Scraping For Market Research

The fastest, most efficient and unpredictable XXI century has brought massive changes to the business world. With the biggest technological leaps since the Industrial Revolution, IT and the continuous development of Artificial Intelligence (AI), companies today have new tools for calculation, data storage, communication and marketing.

In a market where everything is much faster, bigger and easily accessible, for us humans, the natural biological tools can barely keep up with godlike technology in 2022. Sophisticated gadgets and automated software continue to expand the internet and encourage technological advancements, but the systems and digital bodies, created through the cooperation of our brightest minds, need the assistance of the same technologies to understand them and extract every drop of comfort and convenience.

Market research today depends on the technical proficiency of employees or business-minded individuals who can employ automated tools to extract public information on the web. Most successful companies focus on pre-built websites and their positions on search engines as the most valuable systems for client outreach and marketing.

In this article, our goal is to educate readers about market research on the web. We will address different approaches to researching competitors or other valuable targets and discuss the role of automated web scraping. For example, we can focus on the use of eBay stealth accounts and why companies need them to better understand the market. While most businesses see pages of similar retailers as valuable targets, with an eBay stealth account, you can get a thorough analysis of similar products, their pricing and their sensitivity to their changes. For now, let’s discuss the importance of market research and techniques to approach it.

What is Web Scraping?

Web scraping is the process of automatically collecting data from websites using software or scripts. It involves making HTTP requests to a web server, parsing the HTML content of web pages and extracting specific data elements. This data can then be stored in a structured format like a spreadsheet or database for further analysis.

Web scraping enables the collection of large amounts of data quickly and efficiently, which would be impractical or impossible to do manually. It’s a powerful technique used by businesses, researchers and data analysts to gather valuable insights from the vast amount of information available on the internet.

Why is Web Scraping so Popular?

Most of the time, digital market research is based on snooping on what is on the other side of the fence and storing that information. Without powerful computers, the changes in business strategy and pricing were far less frequent, but keeping an eye on all competitors required the collective effort of assigned employees. Today, consumers look for most products on the internet, either by visiting websites of known brands or searching for desired goods on search engines.

Now we have more moving parts than ever before. Just like clients, competitors can go in and out of your website and extract presented information to get a better understanding of your strategy and how to counter it. With search engines as the main access point for online shops, companies have Search Engine Results Pages (SERP) and their rankings as another block of parameters to look out for.

Even with a dedicated team of employees, keeping track of everything can be daunting. Thankfully, the advancements in IT that gave birth to the digital business environment also have the tools to manage and control massive amounts of data.

Web scraping uses automated bots to extract the HTML code in targeted pages and filter out the most important information. It can be new products added by the competitor or changes in pricing for similar goods.

Price sensitivity is a great example that encompasses the need for web scraping. Amazon, the biggest online retailer, changes it’s pricing approximately every 10 minutes, which amounts to more than 2 million price changes for all products a day. Businesses utilize price intelligence and gather data from competitors to undercut or make other favorable adjustments.

Automated data extraction helps us extract a lifetime’s worth of knowledge and filter out the most important bits that help us make precise decisions. One web scraper is much faster than a real user and there are still opportunities for safe and effective scaling.

Benefits of Web Scraping for Market Research

Market research is crucial for businesses to understand their target audience, competitors and industry trends. Web scraping offers several benefits for conducting effective market research:

1. Cost-effective: Web scraping eliminates the need for manual data collection, saving time and resources. It allows businesses to gather large volumes of data at a fraction of the cost compared to traditional market research methods.
2. Real-time data: Web scraping enables the collection of up-to-date information, providing businesses with real-time insights into market trends, consumer behaviors and competitor activities.
3. Competitive intelligence: By scraping competitor websites, businesses can gather valuable information about their products, pricing strategies, customer reviews and marketing campaigns. This data helps in benchmarking and identifying areas for improvement.
4. Identifying market opportunities: Web scraping can uncover untapped market segments, emerging trends and potential growth areas. By analyzing data from various sources, businesses can identify new opportunities and adapt their strategies accordingly.

Improved decision-making: With access to comprehensive and accurate data, businesses can make data-driven decisions. Web scraping provides the necessary inputs for market segmentation, demand forecasting, pricing optimization and other strategic decisions.

The Difficulties of Data Extraction

In 2022, everyone is scraping and no one wants their website to be scrapped. Not only can your competitor use multiple bots at the same time, scrapers also send much more connection requests that can crash the server.

As everyone continues extracting information, businesses set up barriers for bot detection to stop the negative effects of being scraped. When scrapping for market research, you will encounter numerous websites with different tools for bot detection.

Getting caught can bring you many hardships: the operation will abruptly stop, the IP address gets banned and competitors can identify your identity behind it. That is why most modern companies find a proxy provider to protect their web scrapers. You can choose from premium proxies where everything is bigger and better, for a higher price, as their suppliers focus primarily on businesses or find the most affordable residential proxy provider with a decent IP pool. Some bot detection systems can identify addresses from data centers and ToR (The Onion Router) exit nodes. Residential IPs provide the highest level of protection, which makes them a perfect choice for web scraping and research. Reach out to proxy service providers that suit your needs to start analyzing your market and it’s competitors.

Web Scraping Techniques

There are various techniques and approaches to web scraping, depending on the complexity of the websites and the specific data requirements. Here are some common web scraping techniques:

1. Parsing HTML: This involves using programming languages like Python or libraries like Beautiful Soup to parse the HTML structure of web pages and extract specific data elements based on tags, classes or IDs.
2. XPath and CSS selectors: XPath and CSS selectors are query languages used to navigate and select nodes in an HTML or XML document. They provide a more precise way to locate and extract data elements from web pages.
3. Web scraping tools and frameworks: There are numerous web scraping tools and frameworks available that simplify the process of data extraction. Some popular options include Scrapy, Puppeteer and Selenium. These tools handle tasks like making HTTP requests, handling JavaScript rendering and managing concurrent requests.
4. API scraping: Some websites provide APIs (Application Programming Interfaces) that allow developers to access data in a structured format. API scraping involves making requests to these APIs and extracting the desired data from the JSON or XML responses.

Technique	Description	Advantages	Disadvantages
Parsing HTML	Using programming languages or libraries to parse HTML and extract data	Simple and straightforward approach	Limited to static websites, may require frequent updates
XPath/CSS selectors	Using query languages to navigate and select data elements	Precise data extraction, handles dynamic websites	Requires knowledge of XPath/CSS syntax, can be complex
Web scraping tools	Using pre-built tools and frameworks for data extraction	Simplified process, handles JavaScript rendering	Learning curve, may have limitations or costs
API scraping	Extracting data from APIs provided by websites	Structured data format, efficient data retrieval	Requires API access, limited to available endpoints

Best Practices for Web Scraping

To ensure successful and ethical web scraping, consider the following best practices:

1. Respect website terms of service: Review the website’s terms of service, robots.txt file and other guidelines to understand their scraping policies. Some websites explicitly prohibit scraping, while others may have specific requirements or limitations.
2. Be gentle with server resources: Implement appropriate delays between requests to avoid overloading the website’s server. Scraping too aggressively can strain server resources and potentially lead to IP blocking or legal consequences.
3. Use caching and save data locally: Implement caching mechanisms to avoid repeatedly scraping the same data. Store scraped data locally to minimize the need for repeated requests and improve efficiency.
4. Handle dynamic content and pagination: Many websites use JavaScript to load content dynamically or implement pagination. Ensure your scraping techniques can handle these scenarios by using tools like Puppeteer or Selenium that can execute JavaScript and navigate through pages.
5. Monitor and adapt to website changes: Websites often undergo changes in their structure or layout. Regularly monitor your scraping scripts and adapt them as necessary to handle any changes and ensure the continued extraction of accurate data.

Web Scraping Tools and Libraries

There are numerous web scraping tools and libraries available across different programming languages. Here are a few popular options:

• Python:
  • Beautiful Soup: A library for parsing HTML and XML documents and extracting data.
  • Scrapy: A powerful and extensible web scraping framework.
  • Requests: A simple library for making HTTP requests.
• JavaScript:
  • Puppeteer: A Node.js library for controlling a headless Chrome browser and scraping dynamic websites.
  • Cheerio: A library for parsing and manipulating HTML using a jQuery-like syntax.
• Ruby:
  • Nokogiri: A library for parsing HTML and XML documents.
  • Mechanize: A library for automating interactions with websites.
• Java:
  • JSoup: A library for parsing HTML documents and extracting data using CSS selectors.
  • Apache HttpClient: A library for making HTTP requests and handling responses.

These are just a few examples and there are many more tools and libraries available for different programming languages and specific scraping requirements.

Conclusion

Web scraping is a powerful technique for gathering valuable data for market research. By leveraging the vast amount of information available on the internet, businesses can gain insights into their target audience, competitors and industry trends. However, it’s crucial to approach web scraping ethically and legally, respecting website terms of service and intellectual property rights.

By understanding the benefits, techniques, best practices and tools available for web scraping, businesses can harness the power of data to inform their strategies and drive growth. As with any data-driven approach, the key lies in extracting meaningful insights from the collected data and translating them into actionable decisions.