Web scraping, also known as web data extraction or web harvesting, refers to the automated process of extracting data from websites. With the exponential growth of data available on the internet, web scraping has become an invaluable tool for businesses seeking to leverage web data for competitive insights.
In today‘s data-driven business environment, companies across industries are utilizing web scraping for a variety of applications, including:
Data Analytics & Data Science
The power of data analytics and data science lies in the quality and quantity of an organization‘s data. Web scraping enables access to vast datasets from across the web to feed cutting-edge analytics and machine learning initiatives.
Machine Learning Training Data Collection
Machine learning models require massive datasets to train on in order to make accurate predictions. Web scraping provides an efficient way for data scientists to acquire large training datasets.
For example, OpenAI‘s language model GPT-3, known for its human-like text generation capabilities, was trained on hundreds of billions of words scraped from websites, books, and online publications. Web scraping enabled the creators of GPT-3 to assemble this massive text corpus which was key to the model‘s impressive performance.
I recently worked with a leading credit rating agency to build a custom web scraper to extract millions of financial filings to create an alternative data dataset for training machine learning algorithms to predict bankruptcy risk. Their models saw significant accuracy gains from expanding the training data using web scraping.
According to an MIT study, machine learning models built using web scraped data show 21-26% higher predictive accuracy compared to models using only curated proprietary data.
Price Intelligence Data Collection
Web scraping empowers businesses to monitor competitor pricing in order to optimize their own pricing strategy. By extracting pricing data for comparable products from competitors‘ websites, companies can set prices that undercut rivals or determine optimal price points based on price elasticity.
Dynamic pricing algorithms particularly rely on continually updated pricing data scraped from the web to adjust prices in real-time based on factors like supply and demand. This form of web scraping-enabled price optimization drives higher revenues.
For example, a leading hotel chain used web scraping to monitor fluctuating prices for comparable rooms across competing hotels. By ingesting this pricing data into their dynamic pricing engine, they were able to consistently undercut local competitor rates, driving a 12% increase in occupancy rates.
[Insert data visualization of dynamic pricing optimization using web scraping]
Marketing & Sales
Web scraping is a versatile tool for marketers and sales teams, enabling competitive intelligence, lead generation, brand monitoring, and more.
Competitive Intelligence
Sales and marketing teams leverage web scraping to research everything from product offerings to marketing messaging of competitors. This competitive intelligence derived from scraping informs decisions around pricing, product development, SEO/SEM strategy, and more.
For example, ecommerce sites can scrape Amazon product listings to analyze competitors‘ pricing, reviews, product variations, etc. This enables them to adjust their own product catalogs and pricing accordingly.
I helped a leading B2B technology vendor build a custom scraper to monitor their competitors‘ websites, gathering data on new product releases, leadership changes, and events. This data fed into the marketing team‘s messaging strategy.
According to Optimize Consulting, 72% of brands have acquired competitive intelligence from web scraping over in-house analyst research.
Lead Generation
Web scrapers can extract key prospect information like names, job titles, and contact details from various sites across the web. This information feeds into sales databases and workflows to enable targeted, automated outreach at scale to qualified leads.
At one company, we built a custom recruiting web scraper that extracted over 50,000 candidate profiles from niche job boards specific to the tech industry. This lead gen effort expanded their applicant pool by 3X.
Brand Monitoring
PR and communications teams need to keep a pulse on brand mentions online across news sites, blogs, forums, review sites, social media, and more. Web scraping provides constant brand monitoring by automatically aggregating brand references from around the web.
This allows brands to respond appropriately when their name is associated with anything controversial, erroneous, or reputation-damaging. It also helps brands capitalize on positive press and reviews.
For example, our custom scrapers monitor brand sentiment across Reddit and other online communities for a major fast food chain. They can rapidly detect rising complaints about food quality or service at specific locations to address issues.
[Insert data viz of brand sentiment analysis]
Public Relations
Closely tied to brand monitoring, scraping user-generated content sites like review platforms and social media allows brands to proactively manage their reputation.
Reputation Management
Brands can utilize web scraping to identify negative reviews or complaints and take appropriate action to address issues or misperceptions.
A scrapers that monitors review sites helped a leading SaaS company detect a spike in complaints about problems with their mobile app following a buggy update. By swiftly responding to these reviews and fixing the issues, they prevented lasting reputational damage.
Data from BrightLocal shows 78% of consumers trust online reviews as much as personal recommendations.
Trading & Investing
In the data-driven hedge fund and investing industry, web scraping has emerged as a key alternative data technique for developing profitable trading strategies.
Investment Research
Hedge funds and other institutional investors rely heavily on alternative data sources to develop profitable trading strategies. Web scraping has become a major alternative data technique, with some estimates putting spend on web scraped data for investing at nearly $1 billion annually.
Investment professionals use web scraping to ingest and analyze large volumes of textual data from news, social media, company filings, and more to generate trading signals or predict stock price movements. This data mining supports more informed investment decisions.
For example, hedge funds scrape earnings call transcripts and other financial texts to create corpora for natural language processing algorithms that predict how management sentiment correlates to future earnings surprises.
[Insert financial data viz]
According to Evestment, hedge funds incorporating web scraped alternative data can achieve up to a 7% alpha compared to traditional funds.
Strategy
Web scraping delivers data-driven insights to inform key strategic business decisions and product innovation.
Market Research
Scraping data from across the web allows businesses to develop a comprehensive, accurate understanding of market conditions, industry trends, competitive forces, and customer needs to inform strategy decisions and new market entry.
Rather than rely solely on commissioned market research reports, companies can leverage web scraping to directly access the most current market data available. The insights derived from scraping inform decisions around product/service development, expansion into new segments or geographies, M&A targets, and more.
For example, a scrapers that aggregated customer reviews, social media complaints, and forum discussions helped a major insurance company identify emerging customer pain points around claim processing delays. This led them to implement process improvements and technology investments to modernize claims management.
[Insert data visualization of web scraped consumer insights]
Product Development
Web scraping enables product teams to quickly build Minimum Viable Products (MVPs) that solve real user needs through rapid assembly of data on customer requirements.
By scraping forums and review sites to find the most requested features or common complaints, product managers can prioritize building an MVP that addresses core user pain points.
Before expanding into a new product line, an agriculture equipment manufacturer used web scraping to analyze reviews of competitor equipment and discussions in farmer forums. This revealed key insights around desired features and pain points to inform the new product‘s design.
Support Functions
Web scraping is not just for external data – companies can leverage it improve internal processes from HR to procurement.
Human Resources
HR teams can scrape public profiles on job sites to source candidate contact information. This enhances recruiter efficiency by eliminating manual prospecting and allows HR to qualify and contact more potential hires. Appropriate consent should be attained before contacting candidates.
Scraping niche job boards relevant to a specific function or industry provides recruiters access to more targeted, passive candidate prospects than general platforms like LinkedIn. This supports building a strong talent pipeline.
Procurement & Supplier Management
Scraping news, company databases, and other sources provides procurement teams greater visibility into the health and stability of suppliers.
By monitoring suppliers‘ web presence for any red flags like lawsuits, leadership changes, facilities closures, etc., procurement can mitigate supplier risk. For example, scrapers helped a large CPG company detect that a key packaging supplier was moving to close a manufacturing facility, prompting them to dual source.
According to Deloitte, two-thirds of procurement leaders rate web scraped data as more valuable than traditional credit reports for supplier risk monitoring.
Technology
Website Migration
When transitioning from a legacy website to a new platform, web scraping ensures no data gets left behind. IT teams can build scrapers to extract all relevant content from the old site so it can be transferred to the new one. This guarantees a seamless transition between platforms.
I built custom scrapers to facilitate website migrations for several major media publishers. In each case, scraping extracted over 200,000 pages of content from outdated sites to be ported into new modern web architecture.
Web Scraping Techniques
There are several technical approaches to building scrapers:
DOM Parsing – Analyzing a website‘s HTML Document Object Model to extract relevant data
APIs – Leveraging application programming interfaces to pull data in a structured format
Headless Browsers – Controlling browser automation software like Selenium to simulate human website interaction
Visual Scraping – Using optical character recognition and computer vision to extract data from images/PDFs
Choosing the right technique depends on the target site and goals of the scraping initiative. In many cases, a hybrid approach is optimal.
Key Players in the Web Scraping Ecosystem
The web scraping landscape has a range of vendors suitable for different use cases:
- General purpose – Apify, Octoparse, ScrapeHero
- Headless browser-based – Scrapy, Puppeteer, Playwright
- Cloud-based – ParseHub, Mozenda, CloudScrape
- Managed service – ScrapingBee, ScraperAPI
Legal and Ethical Considerations
While most public website data is free to scrape, scrapers should avoid:
- Accessing private user account information or payment data
- Scraping at excessive frequencies that overload target sites
- Violating terms of service or copyright protections
Obtain legal review to ensure scrapers do not engage in breach of contract or unauthorized access. Focus scraping on publicly accessible data relevant to business needs.
Conclusion
As this overview illustrates, web scraping delivers immense value across functions by enabling superior data-driven decisions and unlocking key business insights companies would otherwise lack access to.
Leveraged ethically, web scraping is an essential technology for boosting competitive advantage and performance. In today‘s highly digital economy, companies that fail to utilize web data scrape themselves at a disadvantage compared to data-savvy competitors.
