Now-a-days it seems like everyone wants to be an entrepreneur. While there are hundreds of options to choose from, it\u2019s 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\u2019s discover, evaluate and monitor potential investments by leveraging machine learning. \u201cPreseries works by analyzing data about startups to predict which companies have the most promise from an investor\u2019s standpoint. It uses data provided by the companies and gleaned from public information available in databases like Crunchbase.\u201d [1]<\/p>\n
Preseries use machine learning as a process improvement tool to revolutionize the way VC\u2019s 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.<\/p>\n
Vance Fried and Robert Hisrich explored the decision-making process in venture capital and found that \u201cMany 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.\u201d [2] Preseries addresses the two most notable problems outlined in the current decision-making process, 1) the lack of time and 2) a VC\u2019s 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. [3]<\/p>\n
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, \u201chas served over 500,000 customers, received over US$2M in funding, grew their team from 15 to 24 employees, and recently expanded to Mexico\u201d [3] since they first began using \u00a0Preseries in their investment analysis, but \u201cThese achievements are not reflected in the current PreSeries score and yet they are critical pieces of information for an investor \u201d. \u00a0This missing information is an important indicator of Destcame\u2019s progress and without it, new investors may not have a full understanding of Destcame\u2019s potential.<\/p>\n
In the next 2-10 years, as more VC firms continue to adopt this technology, it\u2019ll be important for Preseries to provide predictive analytics on how well a company aligns with a VC\u2019s investment theory and how their internal data is able to predict future success.<\/p>\n
Preseries should consider integrating human feedback into its assessment as a way to provide qualitative input on a venture\u2019s potential success. As we\u2019ve seen with disruptive startups like Uber and Airbnb, data doesn\u2019t 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.<\/p>\n
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\u2019d be become a one-stop-shop due diligence tool for VC\u2019s.<\/p>\n
Similarly, input from other investors is extremely important in the decision-making process for most VC\u2019s. It\u2019s common practice for VC\u2019s 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\u2019s instead crowd sourced feedback on startups from a variety of investors with diverse experience it could help round out their opinion on a startup.<\/p>\n
Preseries\u2019 success won\u2019t be realized for at least another 5 years when potential exits and IPO\u2019s take place. In the interim, should VC\u2019s trust the analytical judgement of a machine? Will there ever come a point in time where VC\u2019s will be completely automated? (786)<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
This post explores that evolution of predictive analytics and how VC's can leverage machine learning to invest in the next big thing. <\/p>\n","protected":false},"author":11552,"featured_media":34491,"comment_status":"open","ping_status":"closed","template":"","categories":[4928],"class_list":["post-34316","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-venture","hck-taxonomy-organization-preseries","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n