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The Role of Monitoring Effect in Risk Classification for Personalization Strategies: Evidence from Telematics Adoption

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Abstract

The adoption of the Internet of Things (IoT) empowers firms to devise personalization strategies such as customized pricing or targeted advertising through collecting and analyzing consumer activity data. While behavioral tracking has become more prevalent in practice, its effectiveness in discerning customer types to assist personalization strategies has been hardly discussed. We utilize the detailed individual-level data from a field experiment conducted by an insurance company and its car-rental partner to show that tracking using IoT devices such as telematics induces a monitoring effect that sways consumers to behave differently than usual when they are monitored. More importantly, the magnitude of the monitoring effect is correlated with the unobserved inherent behavior of the drivers that the tracking devices target to uncover. The interrelation between the inherent behavior and the monitoring effect significantly undermines the informativeness of the tracked behavioral data in discerning individual types for personalization purposes, even if firms are aware of the existence of the monitoring effect. Our exercise demonstrates that even if recognizing the existence of the monitoring effect, overlooking this correlation can double the misclassification rate when utilizing observed risks for personalization purposes. Furthermore, we show that the monitoring effect on individual behavior spills over into the post-monitoring period and manifests through both habit formation and crowd-out effects. The direction and the magnitude of the post-monitoring effect vary across individuals. These findings have important practical implications for utilizing IoT-tracked data in the design of personalization strategies.

Email us at digitalinitiative@hbs.edu for information on attending this seminar.

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