Now-a-days it seems like everyone wants to be an entrepreneur. While there are hundreds of options to choose from, it’s becoming increasingly difficult for investors to find ventures that will give them 10x returns on their investment. Enter Preseries, an automated platform that helps VC’s discover, evaluate and monitor potential investments by leveraging machine learning. “Preseries works by analyzing data about startups to predict which companies have the most promise from an investor’s standpoint. It uses data provided by the companies and gleaned from public information available in databases like Crunchbase.” 
Preseries use machine learning as a process improvement tool to revolutionize the way VC’s assess new ventures. Many investment decisions are based on who you know and how well you present and less on how likely you are to succeed in the future. Biases in the investment process can stunt the success of a venture and inhibit the investor from making the most informed decision. Machine learning can solve that problem by collecting the available data about a startup from the internet and creating machine learning models that predict future success.
Vance Fried and Robert Hisrich explored the decision-making process in venture capital and found that “Many proposals pass through the firm-specific screen only to be rejected without extensive review … Most deals that pass through the firm-specific screen are rejected at the generic screen based upon a reading of the business plan coupled with any existing knowledge the venture capitalist may have relevant to the proposal.”  Preseries addresses the two most notable problems outlined in the current decision-making process, 1) the lack of time and 2) a VC’s lack of familiarity with certain industries. By leveraging data from across the internet, Preseries is able to analyze 400+ variables to make the evaluation process more efficient and less dependent on investors understanding of the space. 
In the short-term, Preseries is investing it’s energy into refining it’s algorithms so they can more accurately make successful predictions. Their biggest challenge is teaching the algorithm to evaluate a startup’s incremental growth and development. Kelly Nguyen, an Associate at the consulting firm BFA, noted that her portfolio company, Destcame, “has served over 500,000 customers, received over US$2M in funding, grew their team from 15 to 24 employees, and recently expanded to Mexico”  since they first began using Preseries in their investment analysis, but “These achievements are not reflected in the current PreSeries score and yet they are critical pieces of information for an investor ”. This missing information is an important indicator of Destcame’s progress and without it, new investors may not have a full understanding of Destcame’s potential.
In the next 2-10 years, as more VC firms continue to adopt this technology, it’ll be important for Preseries to provide predictive analytics on how well a company aligns with a VC’s investment theory and how their internal data is able to predict future success.
Preseries should consider integrating human feedback into its assessment as a way to provide qualitative input on a venture’s potential success. As we’ve seen with disruptive startups like Uber and Airbnb, data doesn’t always do a great job of predicting how a startup may change consumer behavior, but qualitative feedback on consumer studies can give important insights. The human feedback can come into two main forms: 1) survey and interview data about a product or a service and 2) informed input from a network of investors.
Consumer feedback data can provide insight on why a customer was willing to pay for the service or good and how the consumer believes this product stacks up against competitors. This information becomes increasingly important as startups look to scale. Likewise, this information will be important for investors as they work to understand how successful a startup may be. If Preseries was able to assist in this capacity and turn those qualitative insights into data points, they’d be become a one-stop-shop due diligence tool for VC’s.
Similarly, input from other investors is extremely important in the decision-making process for most VC’s. It’s common practice for VC’s to co-invest in deals with other firms in their network. While this helps build confidence in the success of a new venture, it may also feed into their bias even further as people in their network may share the same views or investment theories. If VC’s instead crowd sourced feedback on startups from a variety of investors with diverse experience it could help round out their opinion on a startup.
Preseries’ success won’t be realized for at least another 5 years when potential exits and IPO’s take place. In the interim, should VC’s trust the analytical judgement of a machine? Will there ever come a point in time where VC’s will be completely automated? (786)
- Kara Baskin, “Preseries wants to make it easier for startups to get funded,” MIT Sloan, October 26, 2017, [ http://mitsloan.mit.edu/ideas-made-to-matter/preseries-wants-to-make-it-easier-startups-to-get-funded], accessed November 2018.
- Fried, V., & Hisrich, R. (1994). Toward a Model of Venture Capital Investment Decision Making. Financial Management,23(3), 28-37. Retrieved from http://www.jstor.org/stable/3665619
- Atakan Cetinsoy, “Machine-Learning Software That Aims to Predict Successful Startups,” Wall Street Journal, March 3, 2017, [https://www.wsj.com/video/machine-learning-software-that-aims-to-predict-successful-startups/C27E3DB5-760F-4537-89B0-242C0B79F990.html], accessed November 2018.
- Kelly Nguyen, “How can investors user Machine Learning to Pick the Right Startups,” MEDIUM | BFA, December 6, 2017, [ https://medium.com/f4life/how-can-investors-use-machine-learning-to-pick-the-right-startups-aef9d370829b], accessed November 2018.