Student Project

Using Data to Identify VC Appropriate Companies before VCs

The state of VC, particularly in the case of consumer technology investing, is ripe for a process overhaul and disruption. VC investors that invest in companies before they've gained significant traction (usually demonstrated by user growth and engagement)  have significantly more leverage than those that are investing after a company has gained traction. As a result, VCs are highly interested in spotting high-growth potential companies early. Yet the process for spotting good companies early in their life cycle is messy. Beyond the background of the team (i.e. do they have a CS degree, did they go to one of 5 schools, have they launched something before, etc.), investors making seed-stage investments rarely have the data to systematically make good decisions and often rely on serendipity (Snapchat investor relied on daughter’s app usage) or result to later stage investing (the only Whats App investor invests after company no longer needed VC). Yet data does exist early stage. Think disparate market signals such as # of mobile app installs, pace of installs, social media mentions, propensity of adopting audience to share, early adoption engagement. What are the hidden or surprising early market signals that matter?  How do we discover the measurements/ levels that are interesting? What else do we think matters?

Last updated:

October 7, 2016