Product Offer and Pricing Personalization in Retail Banking

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Author(s): Wei Ke (Columbia Business School, USA), Rogan Johannes Vleming (Simon Kucher & Partners, Canada)and Rohan Shah (Simon Kucher & Partners, Canada)
Copyright: 2023
Pages: 12
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch116

Keywords: Data Analysis and Statistics / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The rise of parametric and non-parametric modeling capabilities has opened up new possibilities for banks to shift from a fragmented, siloed, and product-centric approach when it comes to pricing and marketing towards orchestrated one-to-one marketing where the bank is able to automate and tailor communications, pricing, and products offers to speak directly to their customers in context-appropriate, relevant ways. Developed using internal and external data, these models can consider a customer's wider relationship with the bank to personalize next-best offers, adapt messages based on reactions and feedback, and nudge customers towards desired actions with high-levels of accuracy and relevance. To optimize the application of machine-learning (ML)-powered personalization in the retail bank setting, a number of critical elements are required including data reliability, supporting response models, behavior-based customer segmentation grids, and customer lifetime value models for commercial effectiveness. A level of organizational maturity is also needed.

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Product Offer and Pricing Personalization in Retail Banking

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