Lead generation is a top priority for businesses of all stripes – and for good reason. A study by Marketo found that companies with mature lead generation practices achieve 133% greater revenue versus their plan than average companies. But lead generation is also a top challenge, with 61% of B2B marketers citing generating high-quality leads as their number one obstacle (Source: HubSpot).
The problem isn‘t a lack of potential leads. There are over 4 billion internet users worldwide, and they‘re leaving digital footprints all over the web that savvy businesses can follow. The real challenge is efficiently collecting, organizing, and acting on all that dispersed lead data. That‘s where web scraping comes in.
What is Web Scraping?
Web scraping is the practice of using software to automatically extract data from websites. Rather than manually copying and pasting information from a webpage, a web scraper can grab all the relevant data and transfer it to a spreadsheet or database in seconds.
At a technical level, web scrapers work by sending HTTP requests to a target webpage, downloading the HTML content, and then parsing that content to locate and extract specific data elements. More advanced scrapers can handle JavaScript-heavy single page applications, navigate authentication barriers, and even mimic human behavior to bypass bot detection mechanisms.
While web scraping has been around for decades, it has exploded in usage in recent years thanks to the proliferation of open source tools and cloud platforms that make it easy for businesses to build and deploy scrapers with minimal coding required. One study found that 55% of companies are now using web scraping on a regular basis.
Why Use Web Scraping for Lead Generation?
Traditional lead generation tactics like cold calling, bulk email blasts, and paid ads can still be effective, but they come with significant downsides. Purchased lead lists and broad targeting parameters tend to result in low conversion rates. Cold outreach is increasingly difficult in an era of spam filters, ad blockers, and regulations like GDPR. And most channels have become thoroughly saturated with marketing noise.
Web scraping offers a more targeted and efficient approach. By collecting lead data directly from the websites where your ideal customers congregate, you can build highly specific lists of leads who have already shown intent or fit key criteria. You get complete control over the accuracy and freshness of your lead data. And you can automate the whole prospecting process from end-to-end.
The results speak for themselves. A McKinsey study found that using automated lead generation techniques like web scraping can increase sales productivity by 14% and cut sales costs by 12%. Another analysis showed that companies leveraging web scraped lead data had 35% higher close rates and 34% larger deal sizes.
How Companies Are Using Web Scraping for Lead Generation
To make this more concrete, let‘s look at a few real-world examples of how companies are putting web scraping into practice:
Technology reseller – This B2B tech provider scraped a massive database of company profiles to identify businesses using specific software products. They then cross-referenced this with firmographic data like industry, size, and growth rate to surface the highest-potential accounts for their sales team to pursue.
Executive recruiting firm – Recruiters used web scraping to aggregate data from LinkedIn, GitHub, and Stack Overflow profiles to build a pipeline of passive tech talent. By analyzing profile data like skills, experience, and engagement metrics, they were able to proactively reach out to professionals who were more likely to be open to new opportunities.
Commercial real estate broker – A CRE firm scraped public property records, permit filings, and business registrations to identify companies in high-growth mode who would likely need new office space in the near future. The brokerage then used this data to reach out and pitch their tenant representation services.
Wealth management practice – This financial advisory used web scraping to collect data points on an ultra-high-net-worth individuals from SEC filings, news articles, property records, and charitable giving databases. By building detailed profiles of potential clients‘ financial status, investment holdings, professional networks, and personal interests, the firm was able to craft highly personalized outreach to win their business.
As you can see, the applications for web scraping in lead generation are virtually limitless. Any business can leverage this technology to gain an edge in an increasingly data-driven sales and marketing landscape.
Different Types of Data to Scrape for Lead Generation
So what exactly should you be scraping to generate leads? The specifics will depend on your business and audience, but here are some of the most common types of data to collect:
Contact information – Names, job titles, email addresses, phone numbers, social media profiles. Scraped from company websites, public directories, social platforms, press releases, etc.
Firmographic data – Industry, location, size, revenue, growth rate, technology stack, web traffic. Pulled from company databases like Crunchbase, public registries, and website analysis tools.
Behavioral data – Content viewed, whitepapers downloaded, webinars attended, social media engagement, support inquiries. Gathered by scraping your own website and marketing platforms.
Intent data – Job postings, RFPs, search activity, review site traffic. Aggregated from various websites to detect buying signals and sales readiness.
Technographic data – CRM usage, marketing automation, payment processing, web hosting. Extracted from website source code or by analyzing third-party integrations.
The key is to get creative and consider all the different places online that your target audience is leaving behind valuable data trails. The more specific and comprehensive a picture you can paint of your ideal leads, the more effective your outreach will be.
Web Scraping Tools & Techniques
Once you know what data you want to collect, the next step is actually building your web scrapers. There are a number of different tools and approaches to choose from depending on your technical capabilities and project requirements:
Visual web scraping tools – These are browser extensions and point-and-click desktop apps that allow you to build scrapers without writing any code. Simply navigate to the webpage you want to scrape, highlight the desired data points, and the tool will automatically extract them. Recommended for simpler, small-scale scraping jobs.
Cloud-based web scraping services – These platforms provide pre-built scrapers for common sites and data types that you can customize with a few clicks. They handle all the heavy lifting of rendering JavaScript, rotating proxy IPs, and scaling up your scraper, making them a good choice for less technical users with more demanding scraping needs.
Open source web scraping frameworks – For maximum flexibility and control, developers can build their own scrapers from scratch using open source libraries like Puppeteer, Scrapy, and BeautifulSoup. This requires more coding expertise but allows for fully customized scrapers that can handle even the most complex websites and large-scale scraping jobs.
Headless browsers – Tools like Puppeteer and Selenium automate full web browsers like Chrome in a "headless" environment without a visible UI. This allows scrapers to interact with JavaScript-enabled pages and more closely mimic human behavior. Essential for scraping modern web apps and avoiding bot detection.
Proxies and CAPTCHA solving services – To scrape a large number of pages without getting blocked, you‘ll likely need to distribute your requests across many different IP addresses using proxies. CAPTCHA solving services can also help automate bypassing those pesky "prove you‘re not a robot" challenges.
The web scraping process can be broken down into a few key steps:
- Identify the target website(s) and specific URL(s) to scrape
- Inspect the page source to locate the desired data points
- Fetch the page HTML with an HTTP GET request
- Parse the HTML to extract and structure the relevant data
- Store the extracted data in a database or export it to a file
- Schedule the scraper to run automatically on a set frequency
- Monitor and maintain the scraper to handle any website changes or errors
There are pre-built scraper templates and plugins that can simplify this process for popular sites like LinkedIn, Yelp, Crunchbase, and TechCrunch. But most serious scraping projects will require some custom configuration and ongoing tweaking to keep the data flowing smoothly.
Best Practices for Lead Generation with Web Scraping
As you embark on your web scraping journey, there are a few key best practices to keep in mind to ensure success:
Use rotating proxies and IP addresses to avoid rate limiting – Most websites will block any IP that makes too many requests in a short period of time. Proxies allow you to distribute those requests across a wide range of IPs to stay under the radar.
Respect robots.txt – This file specifies which pages on a site should not be accessed by scrapers. While it‘s not legally binding, it‘s a good starting point for determining whether a site wants to be scraped. Tools like Scrapy have built-in support for parsing robots.txt.
Set a reasonable request rate – Even with proxies, making hundreds of requests per second is liable to get you blocked. Start slow and then gradually ramp up to a sustainable pace.
Identify yourself with a custom user agent – A user agent is a string that identifies the client making a request. Many sites will block the default user agents associated with popular scraping tools, so it‘s a good idea to set a custom one that looks like a normal browser.
Handle errors gracefully – Websites change all the time and unexpected issues can crop up, so your scraper needs to be able to identify and either work around or report any problems rather than failing entirely.
Cache frequently-scraped pages – If you find yourself scraping the same pages over and over, consider caching the results to reduce the load on both your scraper and the target site. Tools like Scrapy have built-in caching functionality.
Avoid scraping sensitive or copyrighted data – Steer clear of any personally identifiable information (PII) like social security numbers, financial details, or health records. And respect copyrights by only scraping facts and data, not full articles or images. When in doubt, get permission first.
The Future of Web Scraping for Lead Generation
As the volume of data on the web continues to explode and sales and marketing becomes ever-more data-driven, web scraping will only become more essential for businesses looking to stay competitive. Here are some of the key trends and predictions we see shaping the future of this space:
Increased adoption of machine learning and natural language processing – Advancements in AI will allow scrapers to automatically identify and extract entities, understand the sentiment and intent behind text, and predictively score leads based on behavioral patterns. We‘re already seeing early examples of this with tools like Clearbit Enrichment and Node.io.
Shift towards real-time data streams – Rather than periodic batch collections, scrapers will increasingly be used to capture data in real-time as it‘s published to the web. This will enable marketers and salespeople to instantly react to intent signals and engage with leads at the optimal moment.
Growth of vertical-specific data marketplaces – As more companies build sophisticated lead generation scrapers, we expect to see the rise of niche data marketplaces where businesses can buy and sell highly-targeted lead lists in specific verticals like recruiting, real estate, finance, and more.
Tighter integration with CRM and marketing automation platforms – To make web scraped lead data immediately actionable, scrapers will need to sync directly with the other tools in a company‘s sales and marketing stack. Startups like Lusha and LeadFuze are already moving in this direction.
More focus on data accuracy and compliance – With GDPR and other data privacy regulations cracking down on unauthorized scraping and use of personal information, businesses will need to be much more rigorous in verifying and protecting the data they collect. Expect to see scraping tools build in more safeguards and auditing capabilities to help with compliance.
The common thread here is that web scraping is becoming more essential and more integrated into core business processes. It‘s no longer just a hacky tactic for early-stage startups – it‘s a mainstream data collection strategy being adopted by the largest enterprises.
As Gartner puts it: "The future of web scraping is less about the mechanics of how to extract data from sites and more about how to consume, integrate, and act on that data at scale across the organization".
Getting Started with Web Scraping for Lead Generation
If you‘re feeling inspired to start experimenting with web scraping for lead generation, the best way to begin is by clearly defining your target audience and ideal customer profile (ICP). What specific data points would help you identify and evaluate potential leads? Where are those leads most likely to congregate online?
Once you have a data wish list, start by identifying a handful of websites that might contain that information. Manually browse through them and note the URL patterns and data structures you see. Then try out a few different scraping tools and methods to see what works best for your use case and technical comfort level.
Start small and gradually scale up your scraping operation as you figure out what data is most valuable and how to act on it. And make sure to share your results internally to get buy-in and support from sales and marketing leadership.
Web scraping can seem daunting at first, but the payoff in terms of generating high-quality leads at scale is well worth the learning curve. With the right tools and approach, any business can unlock this powerful strategy for growth.