The Role of Social Feedback in Financing of Technology Ventures

TL;DR

This study investigates how social feedback, like search trends and website traffic, influences venture capital investment decisions and company exit outcomes in the Internet technology sector.

Contribution

It introduces a novel dataset linking VC investments with social feedback metrics and demonstrates their impact on future financing and exit likelihoods.

Findings

Positive social feedback increases likelihood of future financing

Social feedback shortens time between funding rounds

Social feedback influences IPO prospects but not M&A exits

Abstract

This research examines relationship between staging of Venture Capital (VC) investments and social feedback visible in publicly available data on the Web. We address the question of Venture Capital investment sensitivity to performance and prospects of new venture, given as likelihood of obtaining future financing, available exit options and duration between investment rounds. We argue that in the case of Internet companies, publicly available social feedback data, such as search trends and website traffic information, can be used as a proxy for some of company's internal metrics such as user base growth and product adoption. In order to answer questions of interest, we compile unique dataset consisting of detailed information about Venture Capital investments in the Internet Technology sector over the period from 2004 to 2012 and associated longitudinal search trend and website traffic data. By applying methods of survival analysis, we find that positive trends in search and website traffic volumes can lead to increased likelihood of future financing and shortening of duration between subsequent financing rounds. We also find evidence that social feedback only impacts company's ability to attract next round of financing or exit via IPO, while M&A exits seem relatively independent of such performance metrics and can occur at any stage of company development. Such findings provide strong evidence in support of learning hypothesis and suggest VC's ability to identify prospects of new venture early in it's development and allocate funding accordingly. Given research also provides methodological contributions to the problem of evaluating the prospects of new startup companies using only publicly available data, and as such should be of interest in applications such as new investment screening and industry-level assessments by analysts or policy makers.