Assortment Optimization with Customer Choice Modeling in a Crowdfunding Setting

TL;DR

This paper models customer choice behavior in crowdfunding platforms using a multinomial logit model, and applies machine learning techniques to optimize platform assortments for revenue maximization.

Contribution

It introduces a novel combination of choice modeling and machine learning for assortment optimization in crowdfunding settings, addressing platform-specific features.

Findings

Machine learning methods can effectively predict optimal assortments.

Comparison shows varying performance of regression and classification approaches.

Assortment optimization can significantly increase platform revenue.

Abstract

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.