In this article we will discuss how Hedge Funds, accelerators, and angel investors better predict startup success rates by:
- Scraping Linkedin for ‘target company information’
- Performing LinkedIn scraping that sheds light on ‘people/team data’
- Scrape Linkedin for industry, and competitive landscape data
Scraping LinkedIn for ‘target company information’
Which companies are on the up, and up?
First, and foremostly investors are trying to gain an informational advantage, and get in at the ground level with fledgling startups before competing investors add them to their portfolios. By scraping LinkedIn for publicly available web data they can identify new startups filtering for specific fields such as ‘biotechnology’ as well as geolocation e.g. Israel.
What startups are showing healthy signs of initial growth?
Funds may want to collect data on companies that have 3-5 employees that are currently posting newly open positions. Or companies that have seen employee numbers double, triple, or even quadruple over the previous year indicating hyper-growth.
Where is investor interest currently focused?
This can be discerned from a variety of data points, from quantitative mentions of a young company in organic posts, to qualitative mentions by an investment authority/influencer. Meaning hundreds of posts that mention a company within the investment professional community or one or two posts by an ‘investment guru’ can both be indicators of a worthwhile pursuit.
How are target audiences engaging with relevant content?
When companies come up with new proprietary technology that is aimed at ‘revolutionizing their industry’ it usually gets a lot of hype, and news coverage. Take Elon Musk’s Tesla cars for example or SpaceX. When they were just starting out both of these companies had massive coverage via print/digital news outlets, that content got shared over LinkedIn, and target audiences engaged with it. Taking the form of ‘likes’, ‘shares’, and ‘comments’. All of which can be algorithmically analyzed to point to consumer/investor sentiment.
How are people reacting to the product itself?
When software is adding value, professionals tend to share this information on LinkedIn. Take Monday.com, for example. It got disproportionate LinkedIn coverage from managers who were excited about workflow optimization, and employees who felt the effects of operational optimization. ‘Social proof’ can manifest itself in many shapes, and forms – it can be a post in which someone tags the company or the uploading of a video or screenshot that demonstrates the product value. It can be an organic message calling like-minded professionals to use ‘such, and such’ software. All of which are great indicators of present/future value creation.
Performing LinkedIn scraping that sheds light on publicly available ‘people/team data’
Who are the individuals behind the product/software?
More often than not, it is the people that make a successful company. By understanding which companies are being driven by champions, investors can better identify corporations that will outperform in their industry. Data points may include:
- How many successful startups have top-brass members previously worked with?
- What unique skillsets do key players possess?
For example, have they published an eBook on LinkedIn that serves as a playbook for Chief Marketing Officers (CMOs) in preparation for an imminent Initial Public Offering (IPO)? Indicating a clear advantage over competitors who employ CMOs that do not have this type of ‘street cred’.
- Where have their careers previously taken them?
Are we talking about a company that develops military-grade cybersecurity solutions, with a Chief Technology Officer (CTO) who served in a key position in the U.S. Army Cyber Command? That kind of information can serve to increase investor confidence in positive technological outcomes as well as the probability of capabilities that exceed current industry standards.
Scrape Linkedin for industry, and competitive landscape public data
What other companies are currently operating in this field?
When looking to invest in a company, no matter the size or stage, investors want to understand the context in which they are operating. Coca-Cola, Pepsi, and Royal Crown (RC) Cola are all working in the soft drink space, and they each have their own individual customer base, quality, and taste bud appeal in varying markets. By collecting information regarding paid content and ads, for example, one can piece together a picture of target audiences, geographies of operation, and potential gaps or vacuums that may be in dire need of filling.
What makes a startup, a company is looking to invest in strategically unique?
Companies are answering this by collecting datasets that shed light on:
What is a company’s Unique Selling Proposition (USP)? Is their solution actually unique or are there 12 other startups in Brazil, China, and India doing something so similar that the company in question will be eclipsed when they finally roll out their software?
By simply collecting and analyzing LinkedIn company descriptions, for example, investors can start narrowing down their direct competitors to a smaller pool of candidates, who can then be designated for a more manual review before an investment is actually made. Not all companies have the time or resources to collect the data themselves, and sometimes they may need the data immediately. For those cases, buying an up-to-date LinkedIn dataset is the best solution.
The bottom line
Whether a company is looking for startups to add to their venture capital portfolio or an accelerator is looking for the next most promising early-stage enterprises. Scraping LinkedIn for publicly available data points is helping organizations gain real-time insights into target business’s:
- Team
- Product
- Growth prospects
- Competitive landscape
- Target audience engagement
So that they can make better-informed investment decisions that strongly impact outcomes and their bottom line.
