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Working Paper

Social Effects on Customer Retention

Irit Nitzan and Barak Libai, 2010 [10-107]

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Researchers recognize the role that social interaction has in the adoption of new products or services: word-of-mouth interactions can lessen the risk and uncertainty associated with new products. But what about quitting behavior? If social connections can induce a person to adopt a product or service, can they also lead a person to abandon a product or service?

Until recently, it has been difficult to study this question because of the extensive data needed to do so, but in the past few years, researchers have used telecommunications databases to explore social network behavior. In the current research, Irit Nitzan and Barak Libai of Tel Aviv University use a cellular phone company’s database of more than one million customers to examine the influence that customers who leave the company (defectors) have on their first-degree contacts (direct contacts, or “neighbors”) in their social network.

Hypotheses
The researchers hypothesize that exposure to a defecting neighbor will increase a customer’s likelihood of defecting and that factors of closeness and distance will moderate that effect. That is, the more closely the customer and the defecting neighbor are linked—and the more similar they are to each other—the greater the likelihood of defection becomes. The researchers also expect temporal distance to play a role: they predict that as time passes, the effect of the neighbor’s defection will lessen. Finally, they expect that the more loyal the focal customer is to the company, the weaker the influence of his or her neighbor’s defection will be.

Results
The researchers use a proportional-hazard model (commonly used to model the duration of customer-provider relationships) to examine the variables of exposure to defection, tie strength, similarity, and economic incentives. As they predict, the defection of a network neighbor increases a customer’s hazard of defecting—in fact, it greatly increases it: exposure to a defecting neighbor is associated with an increase in the focal customer’s hazard of defecting as much as 150%, or by 80% when tie strength and similarity (homophily) are controlled for.

The analysis shows that every 1% increase in a customer’s tie strength with defecting neighbors is associated with a 2% increase in that customer’s hazard of defection. The average tie strength of the customers in the data set is around 8%, which means that exposure to defecting neighbors increases their risk of defection by 16%. Similarity (homophily) among neighbors also increases the risk: a 1% increase in similarity between a customer and a defecting neighbor is associated with a 1.1% increase in the customer’s hazard of defection.

As predicted, the influence of a neighbor’s defection decreases markedly with the passage of time, and a customer’s loyalty “immunizes” him or her against the effects of a defecting neighbor.

Implications
Given that mere exposure to a defecting neighbor is associated with an increase in a customer’s hazard of defection by 80%, both managers and researchers have a strong incentive to understand the role of social effects in customer retention. This is especially true given that previous research has noted that negative events can have a more powerful effect on people than positive ones.

The researchers recommend that managers include customers’ social networks in their attempts to predict and manage customer churn. Companies that are interested in understanding the behavioral drivers of retention should probably also take social networks into consideration. For example, customer satisfaction surveys might be modified to include questions regarding customers’ friends who also use or have used the product or service in question.

The results also reinforce the importance of customer loyalty, as those customers who feel loyal to the company are less likely to be swayed by the defections of others. The researchers urge further study in other industries to confirm the generalizability of the findings. The current research examines the influence of first-degree contacts, but the researchers also recommend investigating the influence of defections of network members that are second-degree or even more greatly removed from the focal customer.

Irit Nitzan is a doctoral student and Barak Libai is a Professor in the Faculty of Management, both at the Leon Recanati Graduate School of Business Administration, Tel Aviv University, Israel.

Acknowledgments
The authors would like to thank Vardit Landsman, Eitan Muller and Gal Oestreicher-Singer for helpful comments and discussion. This research was partially supported by the Institute for Business Research at Tel Aviv University, and the Israel Science Foundation.

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