  Crowdfunding, which is the act of raising funds from a large number of
people's contributions, is among the most popular research topics in economic
theory. Due to the fact that crowdfunding platforms (CFPs) have facilitated the
process of raising funds by offering several features, we should take their
existence and survival in the marketplace into account. In this study, we
investigated the significant role of platform features in a customer behavioral
choice model. In particular, we proposed a multinomial logit model to describe
the customers' (backers') behavior in a crowdfunding setting. We proceed by
discussing the revenue-sharing model in these platforms. For this purpose, we
conclude that an assortment optimization problem could be of major importance
in order to maximize the platforms' revenue. We were able to derive a
reasonable amount of data in some cases and implement two well-known machine
learning methods such as multivariate regression and classification problems to
predict the best assortments the platform could offer to every arriving
customer. We compared the results of these two methods and investigated how
well they perform in all cases.
