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This study explores the role of customers' social network in their defection from a service provider. The authors use data on communication among one million customers of a cellular company to create a large-scale social system composed of customers' individual social networks. The study's results indicate that exposure to a defecting neighbor is associated with an increase of 80% in the defection hazard, after controlling for a host of social, personal, and purchase-related variables. This effect is comparable in both magnitude and nature to social effects observed in the highly researched case of product adoption: The extent of social influence on retention decays exponentially over time, and the likelihood of defection is affected by tie strength and homophily with defecting neighbors and by these neighbors' average number of connections. Highly connected customers are more affected, and loyal customers are less affected by defections that occur in their social networks. These results carry important implications for the theoretical understanding of the drivers of customer retention and should be considered by firms that aim to predict and affect customer retention.

Many service providers offer supplementary products related to their ongoing services (e.g., fitness centers offer fitness smartwatches). In seven studies, the authors show that the payment method for such supplementary products (multiple payments vs. a single lump sum) affects customers’ tendency to defect from the provider’s core service over time. Specifically, when customers pay for add-ons in multiple payments—provided that (1) they perceive the add-on as being bundled with the core service and (2) the payment period has an end point—they are initially less likely to defect from the service provider than when they pay in a single payment. Over time, however, as payments are made, this gap closes, such that defection intentions under the two payment methods eventually become similar. The authors propose that this phenomenon reflects “commitment projection,” wherein a decrease in customers’ commitment to the add-on product over time is projected onto their commitment to the service provider. These findings carry important managerial implications, given that many service providers offer add-on products in multiple-payment plans and that customers’ defection decisions substantially affect firms’ profitability.

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The adoption of new behaviors or new products is often influenced by those closest to us, but customer churn is influenced by social factors as well. An analysis of 1 million customers of a cellular company showed that customers were 79.7 % more likely to defect for each time one of their social neighbors defected. The more a customer communicated with a neighbor and the more characteristics they shared, such as age, gender or status, the more likely the customer was to follow the neighbor in canceling the service. The effect of a neighbor’s defection on a focal customer’s hazard of defection was strongest within the first month and decreased over time. The study shows that companies should take customers’ social networks into account when attempting to predict and manage customer churn. Network-related information can substantially improve analysis of new product adoption, and the same seems true for the field of customer defection.