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Robo Advisory Customer Groups: Who Requires Advice?

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  • Blaschke, Justus
  • Kriebel, Johannes

Abstract

Prior literature has often investigated how robo advisors can broaden their customer base. This study is based on the observation that some customers value the risk elicitation of robo advisors (guidance customers), whereas others value other aspects such as the simplicity and convenience of these services. Based on empirical robo advisory data, we build machine learning models to identify guidance customers. The models make predictions based on the financial knowledge of customers to a large extent. The age of a customer, the amount invested, income, and available assets are further important determinants.

Suggested Citation

  • Blaschke, Justus & Kriebel, Johannes, 2021. "Robo Advisory Customer Groups: Who Requires Advice?," Die Unternehmung - Swiss Journal of Business Research and Practice, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 75(3), pages 397-410.
  • Handle: RePEc:nms:untern:10.5771/0042-059x-2021-3-397
    DOI: 10.5771/0042-059X-2021-3-397
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    References listed on IDEAS

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    4. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
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    Cited by:

    1. Nicole Maria Namyslo & Dominik Jung & Timo Sturm, 2025. "The state of robo-advisory design: A systematic consolidation of design requirements and recommendations," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-29, December.

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