Index Policies for Campaign Promotion Strategies in Reward-Based Crowdfunding

Abstract

Reward-based crowdfunding plays a crucial role in fundraising for start-up entrepreneurs. Many crowdfunding platforms follow an all-or-nothing scheme whereby they collect commissions only from successful campaigns. This study considers platforms' decision of selecting projects to highlight on the homepage to boost their chance of success, and investigates optimal promotion strategies that maximize platforms' revenue over a fixed period. We characterize backers' investment decisions by a Multinomial Logit model and formulate the problem as a stochastic dynamic program, which is however computationally intractable. To address this issue, we follow the Whittle's Restless Bandit approach to decompose the problem into a collection of single-project problems and prove indexability for each project under some mild condition. We show that the index values can be directly derived from the value-to-go of each project under a non-promotion policy, which has a linear-time complexity. To the best of our knowledge, this work is the first in the literature to develop index policies for campaign promotions in reward-based crowdfunding. It is also the first to provide indexability analysis of bi-dimensional restless bandits coupled by not only resource but also demand. Extensive numerical experiments show that the proposed index policy outperforms the other benchmark heuristics in most scenarios considered.

Keywords: reward-based crowdfunding, multinomial logit model, restless bandits, index policy