Referral Timing and Fundraising Success in Crowdfunding

Gordon Burtch, Diwakar Gupta () and Paola Martin ()
Additional contact information
Diwakar Gupta: McCombs School of Business, University of Texas at Austin, Austin, Texas 78712
Paola Martin: McCombs School of Business, University of Texas at Austin, Austin, Texas 78712

Manufacturing & Service Operations Management, 2021, vol. 23, issue 3, 676-694

Abstract: Problem definition : Crowdfunding, a relatively new approach for raising capital for early-stage ventures, has grown by leaps and bounds in the past few years. Entrepreneurs launch a campaign on a web platform and solicit contributions from many potential backers. A primary way that entrepreneurs affect fundraising is by leveraging their social networks to drive traffic to their campaign. We address the following question: When should an entrepreneur send out referral links to impel traffic to the campaign web page? Academic/practical relevance : Prior capital accumulation serves as social proof of the project’s “quality,” which can result in herding. However, prior capital accumulation can also lead to crowding out and bystander effects. Entrepreneurs’ social networks strongly affect their chances of success, but they often do not know when to solicit contacts’ involvement. We investigate this question via a combination of empirical and analytical methods, providing guidance for platform owners and entrepreneurs. Methodology : The social proof/herding mechanism leads to a convex-shaped effect of current accumulation on future contributions, the crowding out scenario leads to a concave-shaped effect, and the initial dominance of herding giving way to the later dominance of crowding out leads to a sigmoidal effect (S-shaped curve). We use a Markov decision process model to derive three alternative optimal referral policies, which we fit to proprietary data from a large crowdfunding platform. We explore heterogeneity in relative model fit across different subsamples of our data, demonstrating that our conclusion is stable over a range of scenarios. Results : Using mathematical models, we identify optimal referral strategies under the concave, convex, and S-curve assumptions. Estimating these models on the proprietary data, we show that the S-curve model exhibits the best fit. Based on estimated model parameters, our simulations show that a nonoptimal (e.g., myopic) expenditure of referrals can lead to a substantially smaller accumulation of funds. Managerial implications : The results of this paper help inform both platform owners and entrepreneurs. Platform owners can perform this sort of analysis to provide guidance to entrepreneurs about referral strategy. The entrepreneurs, in turn, learn that in an environment similar to that represented in our data, they will benefit from concentrating their referrals earlier in the fundraising process, while retaining some portion for the final stages of fundraising. The mix of this early versus late referral allocation within the campaign duration may vary depending on the entrepreneurs’ social capital and referral cost.

Keywords: crowdfunding; referral-based marketing; social proof; crowding out; entrepreneurship (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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