Leveraging machine learning to deepen customer insight

Zain Abbas, Roland Merbis and Artur Motruk
Additional contact information
Zain Abbas: Senior data scientist at Scotiabank, Canada
Roland Merbis: Director of Customer Insights and Analytics at Scotiabank, Canada
Artur Motruk: Senior manager of customer insights and analytics at Scotiabank, Canada

Applied Marketing Analytics: The Peer-Reviewed Journal, 2020, vol. 5, issue 4, 304-311

Abstract: Banks have been sitting on troves of customer behavioural data for decades; only recently, however, have they had the computing power to find meaningful patterns in all these data. This article discusses how Scotiabank’s analytics team is using machine learning to better understand its customer base and identify meaningful events in its customers’ lives, like first job/employment, income change, starting or graduating college/ university, and family growth, thus improving the bank’s ability to provide contextual, personalised service and support. Understanding when customers are going through these life events makes it possible to deliver more meaningful contextual communication, and improve customer-centric service and support. This approach improves the customer experience and brand loyalty, translating into better marketing results and increased revenue for the bank.

Keywords: analytics; machine learning; artificial intelligence (AI); contextual marketing; customer insight; marketing; segmentation and behavioural modelling (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2020:v:5:i:4:p:304-311

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