Classification of risk attitudes from customer behavior with machine learning

Abstract

Every​ product​ and service in​ the market​ has​ its​ characteristic​ which has an impact on a consumer's decision to buy or use them. The risk is a special characteristic of financial products, so in financial product and service design must use risk as a critical factor. On the other hand, the consumer has different attitudes to the risk which can distinguish in 3 categories viz risk aversion, risk neutral and risk seeking. Therefore, knowing risk attitudes of consumer who is the target market is an important key to define marketing strategy such as designing service and product, campaign, and promotion which going to be offered to them. There are two ways to know the consumer’s risk attitudes. The first way, is via a questionnaire which consumer do it by themselves and the second way, is via their behaviors which reflect their risk attitude from their activities in everyday life. With the second way, machine learning takes a vital role to classify risk attitudes of each consumer and some machine learning such as Ensemble can specify the features or consumer’s behaviors which dominant to their risk attitudes. In this paper, we study and experiment to classify consumer’s risk attitudes from their behaviors and specify importance features with Ensemble method.