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Price changes and defection levels in a subscription‐type market: can an estimation model really predict defection levels?

Price changes and defection levels in a subscription‐type market: can an estimation model really... This paper examines the relationship between price changes and customer defection levels in a "subscription"-type market, namely car insurance. Two regression models are constructed to estimate this relationship, one model for younger customers and another for older customers. The regression models closely estimate the defection rates associated with different levels of price changes. The analysis also shows that the impact of price decreases on defection rates is less than the impact of price increases, extending previous research. The paper notes that models of this type should offer true predictive ability and therefore tests the ability of the model to predict defection rates for new data. The models performed comparatively poorly in this regard, particularly for price increases. The paper concludes that multiple sets of data are needed to develop and validate predictive models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Services Marketing Emerald Publishing

Price changes and defection levels in a subscription‐type market: can an estimation model really predict defection levels?

John Dawes
Journal of Services Marketing , Volume 18 (1): 10 – Jan 1, 2004
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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
0887-6045
DOI
10.1108/08876040410520690
Publisher site
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Abstract

This paper examines the relationship between price changes and customer defection levels in a "subscription"-type market, namely car insurance. Two regression models are constructed to estimate this relationship, one model for younger customers and another for older customers. The regression models closely estimate the defection rates associated with different levels of price changes. The analysis also shows that the impact of price decreases on defection rates is less than the impact of price increases, extending previous research. The paper notes that models of this type should offer true predictive ability and therefore tests the ability of the model to predict defection rates for new data. The models performed comparatively poorly in this regard, particularly for price increases. The paper concludes that multiple sets of data are needed to develop and validate predictive models.

Journal

Journal of Services Marketing – Emerald Publishing

Published: Jan 1, 2004

Keywords: Customer satisfaction; Pricing; Insurance

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