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

D. van Thiel, W.F. van Raaij

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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
Original languageEnglish
Pages (from-to)88-101
JournalJournal of Financial Transformation
Volume2017
Issue number46
Publication statusPublished - 2017

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Robot
Psychographics
Segmentation
Targeting
Personalization
Factors
Millennials
Factor analysis
Consumer finance
Wealth
Cluster analysis
Key words
Education
Income
Advisors
Economics
Activation
Demographics
Financial literacy
Service delivery

Cite this

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title = "Targeting the robo-advice customer: the development of a psychographic segmentation model for financial advice robots",
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Targeting the robo-advice customer: the development of a psychographic segmentation model for financial advice robots. / van Thiel, D.; van Raaij, W.F.

In: Journal of Financial Transformation, Vol. 2017, No. 46, 2017, p. 88-101.

Research output: Contribution to journalArticleScientificpeer-review

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