Lead Generation by Extracting Data with Python

A McKinsey study observed that companies that utilized data, analysis, and technology to empower and complement their sales and marketing witnessed a 15-25% increase in their EBITDA (earnings before interest, taxes, depreciation, and amortization) as well as above-market growth. The technologies in question include data mining and analytical tools. The study notes that data mining solutions have increased the opportunities to identify new leads by helping companies find hundreds or even thousands of potential customers. Simply put, data extraction and mining tools aid in lead generation.

What is Lead Generation?

Often contracted as lead gen, it refers to the process of identifying, attracting, closing, and converting new potential customers, known as leads, into loyal customers. Lead gen is vital for businesses as it increases brand awareness and, eventually, the number of customers and purchases, ultimately translating to increased revenue. Additionally, lead generation offers credibility to marketing efforts, with the leads acting as tangible results of the allocated budget. No wonder then that 85% of B2B companies regard lead gen as the most important marketing goal.

There’re several ways to generate leads, including:

• Email marketing
• Social media
• Blog content and search engine optimization (SEO)
• Landing pages
• Videos
• TV and radio
• Podcasts
• Paid search ads
• Organic search
• Physical events and tradeshows
• SMS

Each of these promotional channels targets potential customers in its own unique way. However, they all have a similar goal: to actively engage with the lead and have them sign up for a communication service, such as email, or fill in a form that prompts for their contact information. But some of the approaches above can be costly, yet they may not generate many leads. For instance, TV and radio advertisements may not influence the viewers or listeners to provide their contact information. Similarly, some users may avoid paid search ads in favor of organic search.

For this reason, marketers need to find a balance by using an approach that’s as effective as it’s cost-efficient. And web scraping bears all the hallmarks of an efficient and cost-effective lead-generation system.

What is Web Scraping?

Web scraping is the process of collecting publicly available data from third-party websites. Although the term can refer to either manual or automated data collection methods, it’s mostly used to denote automated data harvesting. Mainly, that’s because manual web scraping is slow, prone to errors, and costs considerably high due to the time and human resources needed. In contrast, automated web scraping, performed by bots known as web scrapers, is fast, accurate, reliable, and cost-efficient.

It’s noteworthy that web scrapers can be purchased or created from scratch. To create a custom web scraper, you must use a programming language that has an HTTP requests library, especially because the first step of any web scraping project entails sending HTTP requests. Python is one such language.

Web scrapers allow you to extract contact information from reliable sources such as online directories or social media pages. Thus, the first step always entails identifying such sources. Next comes the data extraction process using Python, in what is known as Python web scraping.

Python Web Scraping

Python is a high-level, general-purpose programming language that’s easy to learn and understand as it uses an English-like syntax. In addition, Python is known for its libraries, which simplify the process of creating software and applications. When it comes to web data harvesting, for instance, there are some pretty helpful Python web scraping libraries, namely Requests, Selenium, Beautiful Soup, and lxml.

The Requests library enables the scraper to send HTTP requests. Beautiful Soup and lxml are parsing libraries that enable the scraper to convert the unstructured data stored in the HTML and XML files into a structured format. Lastly, Selenium facilitates JavaScript rendering.

You can use a Python web scraping tool to extract contact information from websites and emails and store it in a file. You can also use Python to automatically fill online forms using the data collected by the scraper. This means that you can use Python not only to get a hold of the data automatically but also to send out marketing messages to potential customers whose contact information has been collected.

Conclusion

The process of generating leads can be complicated, no doubt. It generally entails identifying potential customers with the intention of converting them into loyal customers. But the complication arises from the fact that there are multiple approaches businesses can use to generate leads. Fortunately, you can use web scraping, which is a cost-efficient and one of the most effective ways to generate leads. A Python web scraping tool can extract contact information from websites and emails and fill forms automatically, thus providing you with a hassle-free data processing method.