Distributed monitoring of cluster quality for car insurance customer segmentation

Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this paper, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: different mobility needs, driving habits and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and provide a clustering-oriented instantiation of the segmentation problem, based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up-to-date. We tackle a major scalability issue that emerges in this con- text when the number of customers is large, namely the communication bottleneck, by proposing and implementing a sophisticated distributed monitoring solution, which reduces the communications between vehicles and company servers to the essential. Finally, we validate the framework on a large database of real mobility data, coming from GPS devices of private cars.