Title
Driving Profiles Computation and Monitoring for Car Insurance CRM.
Abstract
Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, 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 we 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 context when the number of customers is large—namely, the communication bottleneck—by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation.
Year
DOI
Venue
2016
10.1145/2912148
ACM TIST
Keywords
Field
DocType
Driving profiles, distributed clustering, privacy
Customer relationship management,Countermeasure,Data mining,Market segmentation,Computer security,Segmentation,Computer science,Server,Risk analysis (engineering),Global Positioning System,Computation,Scalability
Journal
Volume
Issue
ISSN
8
1
2157-6904
Citations 
PageRank 
References 
1
0.37
37
Authors
5
Name
Order
Citations
PageRank
Mirco Nanni1141284.47
Roberto Trasarti271045.82
Anna Monreale358142.49
Valerio Grossi4305.18
Dino Pedreschi53083244.47