Leveraging fine-grained transaction data for customer life event predictions
De Caigny, Coussement and De Bock work with a large European financial services provider. They combine aggregated customer data on demographics, behaviour and contact with the firm with fine-grained debit transaction data to predict four life events: moving, the birth of a child, a new relationship and the end of a relationship. The transaction data, covering around a hundred and thirty thousand customers and a very large number of transactions with counterparties, reveal a pseudo-social network that supports measures of behavioural similarity. The study validates the prediction model in a real-world setting.
De Caigny, Coussement and De Bock work with a large European financial services provider. They combine aggregated customer data on demographics, behaviour and contact with the firm with fine-grained debit transaction data to predict four life events: moving, the birth of a child, a new relationship and the end of a relationship. The transaction data, covering around a hundred and thirty thousand customers and a very large number of transactions with counterparties, reveal a pseudo-social network that supports measures of behavioural similarity. The study validates the prediction model in a real-world setting.
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De Caigny, A., Coussement, K., & De Bock, K. W. (2020). Leveraging fine-grained transaction data for customer life event predictions. Decision Support Systems, 130, 113232. doi:10.1016/j.dss.2019.113232