Targeting the robo-advice customer: the development of a psychographic segmentation model for financial advice robots

Abstract

The purpose of this study is to develop the world’s first psychographic market segmentation model that supports personalization, customer education, customer activation, and customer engagement strategies with financial advice robots. As traditional segmentation models in consumer finance primarily focus on externally observed demographics or economic criteria such as profession, age, income, or wealth, post-hoc psychographic segmentation further supports personalization in the digital advisor’s service delivery. It might also provide insight into how to include the 4.5 billion underserved people financially and support inexperienced millennials in securing their future financially. To develop the psychographic segmentation, a survey (N= 2,232) has been conducted across the U.K. and the Netherlands. Factor analysis has been performed to define the following psychographic factors: “convenience,” “financial illiteracy,” and “rigid personality.” Based on these factors, a Ward cluster analysis has been performed to define the psychographic segments across the two markets. Keywords: financial advice, digital advice, psychographic segmentation, financial literacy, risk tolerance