Can Your Toothpaste Shopping Predict Mutual Funds Purchasing? Transferring Knowledge from Consumer Goods to Financial Products Via Machine Learning

Abstract

With the rapid growth of e-commerce, financial products are being brought onto online platforms. However, due to the scarcity of data in this new product domain, online platforms face challenges in predicting users' purchase behavior. In this paper, we study whether we can "transfer'' knowledge learned from the existing consumer goods domain to benefit the prediction in the domain of the financial products. With data provided by one of the largest online shopping platforms in China, we develop machine learning solutions to enable knowledge transfer. We show that users' prior browsing and shopping history in consumer goods can significantly improve the prediction accuracy of users' purchases of mutual funds for both the existing-user and the new-user scenarios.

In addition, we study the heterogeneous prediction performance lifts on users with different socioeconomic statuses and investment risk preferences. Results show that information from the consumer goods domain has a higher prediction performance lift on users in the high socioeconomic group. Finally, we compare the effect of different sources of information on predicting users' purchases of mutual funds. We find that users' browsing and shopping history for consumer goods are more predictive than their profile features. Our findings and methods will be valuable to both the financial industry and online platforms that seek to expand their product domains.

Keywords: cross-domain recommendation, cross-domain consumer behavior, e-commerce, transfer learning