How can Web Scraping help Analysts in Venture Capital Firms

An analyst in a typical VC firm spends most of their time trying to find deals for their firm by scanning through a pile of blogs, news websites, start-up listings, and portals manually for identifying new opportunities, researching about those startups and then emailing or calling them.

This research would seem impressive in the early days of an analyst’s career, but it would soon lead to repeats like Uber for X and Tinder for Y, making the research seem uninteresting. 

Scouting for Opportunities

Web Scraping can help you automate most of these “boring” parts.

You can find new opportunities and potential startups by collecting and analyzing data from thousands of media websites, patent databases, job boards, etc. You can then use machine learning to correlate these together giving you most of the info you need, along with some new correlated insights. (Data-as-a-Service providers like ScrapeHero can help you get just the required data and the insights without worrying about writing and maintaining scrapers)

Web Scrapers can also help you get more information about the attendees of events you would be attending, to meet and interact with potential start-ups. You can connect this data to the other sources that pull news and funding info on start-ups and get a clean report in few minutes. This should give you enough information to wow those CEOs, other investors and your firm and show them your firms “worth”.

Evaluating Potential Start-ups

It’s a well-known fact that VCs don’t just throw money at every startup that knocks on their door. Vetting or “Due Diligence” is critical to the success of every VC firm.  The research for evaluating a potential deal requires you to sort through various markets, companies, technologies, regulatory issues, financial data, equity information, funding information, news articles, legal research data, and individuals, swallowing up a lot of time for data janitorial work.

Some of this data would readily available to download public/private databases. The rest of the publicly available data has to be collected using crawlers or scrapers.

Web Scrapers can help you pull data from resources to get answers to the questions you get every day, like “Tell me how big this market is.” or “Tell me as much about this company as you can.”.

You can also do background checks, news runs, patent searches, trading comparables, acquisition comparables, demographic information, funding data, growth projections, etc. by using scripts to gather this info and run simple or complex lookups.

Soon, you will be able to do a single search with a start-ups name, and get back statistics, news mentions, funding details, detailed information about the management, patents, job posts, reviews about the company by employees in few minutes. We can add some Artificial Intelligence capabilities to tell you if a start-up clears your screening criteria, from your previous screenings to refine this even further.

You could probably start your day by saying

“Alexa, find me some promising startups in FinTech from UK”

and be greeted back with a list of startups and their statistics

( We’ll write more on this soon, once we get a working prototype that can do this ).

If you are planning to build and run scrapers yourself, read more here Scalable do-it-yourself scraping – How to build and run scrapers on a large scale. 

Or if you would like to skip all the hard work and use Data as A Service providers like ScrapeHero, contact us by filling the form below –

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Please DO NOT contact us for any help with our Tutorials and Code using this form or by calling us, instead please add a comment to the bottom of the tutorial page for help

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