Abstract

This paper uses a new dataset from a major European credit card issuer to fill an important gap in the study of coalition loyalty program effectiveness by analyzing how complex spatial relationships influence card spend and redemption activity. A coalition program offers incentives to customers at multiple businesses; the primary challenge facing coalition managers is determining the value of each store in the network. We use detailed, individual-level transaction data to analyze how the spatial evolution of a coalition loyalty program network influences card usage and redemption activity. By augmenting traditional spatial models with advances in machine learning methods we are able to properly account for the influence of complex spatial interactions and provide actionable analyses to managers. Our results suggest that if the coalition reduces the size of their network to only include key branch locations estimated credit card revenue increases substantially.

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