Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14032
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dc.contributor.authorPagidimarri, Venkatesh
dc.contributor.authorKasivajjala, Vamsichandra
dc.contributor.authorMiyan, Deepti
dc.date.accessioned2015-07-08T06:32:14Z
dc.date.available2015-07-08T06:32:14Z
dc.date.issued2015
dc.identifier.citationPagidimarri, V., Kasivajjala, V., & Miyan, D.. (2015). Predicting Policy Renewal Probability in Auto Insurance Sector. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14032
dc.description.abstractWe are entering a new phase of the information era, in which organizations query and analyze huge volumes of diverse data in real-time to improve outcomes for their most critical business processes. In today’s competitive edge, the concept of Predictive Analytics is used in every business sector. This paper discusses about how Predictive Analytics helps in increasing the policy renewal rate for Auto Insurance sector. Customer demographics data, Type of vehicle, historical claims data is used to understand the behaviour of customers in renewing the policies. The model identifies the parameters which impacts the policy renewal rates in customers. Customers were grouped in to High, Medium and Low segments based on their policy renewal probabilities. This will help the insurers to identify their potential customers that are more likely to go for the policy renewal. The last section of the paper outlines the financial benefits in increasing the policy renewal rate using the predictive model.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;032
dc.subjectPredictive modelsen
dc.subjectInsuranceen
dc.subjectPolicyen
dc.subjectRenewal Rateen
dc.subjectLogistic regressionen
dc.subjectSegmentationen
dc.subjectBusiness Impacten
dc.titlePredicting Policy Renewal Probability in Auto Insurance Sectoren_US
dc.typeArticleen_US
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

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