Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

Benoit, Dries F.; Poel, Dirk Van den

doi:10.1016/j.eswa.2009.01.031

Cited by 88 publications

(45 citation statements)

References 37 publications

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“…Since quantile regressions are estimated simultaneously, degrees of freedom are not calculated by quantile but as a system. By keeping in all the information available from the dataset, quantile regression thus provides the analyst with better in-depth insights into the effects of the covariates than would a series of independent standard linear regressions (Benoit and Van den Poel, 2009). et al (2008) are among the first authors to resort to quantile regression (QR) for addressing the market heterogeneity issue in housing research.…”

Section: Literature Reviewmentioning

confidence: 99%

Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach

et al. 2016

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In this paper, the heterogeneity of the Paris apartment market is addressed through assessing the differences in the hedonic price of housing attributes over the 2000-2006 period for various price, hence income, segments of the housing market. For that purpose, quantile regression is applied to the 20 Paris "arrondissements" as well as to the 80 neighborhoods, called "quartiers" -or quarters -(each "arrondissement" is composed of four quarters), with market segmentation being based on price deciles (deciles 1 to 9). The database includes some 159,000 sales spread over a seven year period (2000 -2006). Housing descriptors include, among other things, a price index, building age, apartment size, number of rooms and bathrooms, unit floor level, the presence of a lift and of a garage, the type of street and access to building (boulevard, square, alley, etc.) as well as a series of location dummy variables standing for both the "arrondissements" and the quarters.Findings clearly suggest that hedonic "relative" prices of several housing attributes significantly differ among deciles, although discrepancies tend to vary greatly in magnitude depending on the attribute. Among other findings, the elasticity coefficient of the size variable, which stands at 1.07 for the first price decile (cheapest units), is down to 1.03 for units belonging to the ninth one (dearest units). The number of rooms, of service rooms, of bathrooms, the housing type, the apartment floor level as well as the number of parking slots all exhibit strong implicit price fluctuations among deciles while it is less so for the building period that affect prices in a more uniform way. Finally, the lower the apartment price, the higher the potential for price appreciation over time.

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Section: Literature Reviewmentioning

confidence: 99%

Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach

et al. 2016

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“…Here J is the number of different services sold, Servij,t is a dummy indicating whether customer i purchases service j at time t, Usageij,t is the amount of that service purchased and Marginij,t is the average profit margin for service j (Beniot and Poel, 2009). …”

Section: Customer Lifetime Valuementioning

confidence: 99%

“…Since the largest group of customers buys no or only a very limited amount of products or services and only a small group of customers buys many products or services, the researcher should be aware of the fact that he or she is modeling rare events. In this rare-event situation, it is known that parametric choice models easily break down (Gupta et al, 2006;Beniot and Poel, 2009). The other approach, where the…”

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Customer Lifetime Value Analysis to Profitability Management: A Framework to Strategic Marketing

Safari

2015

ILSHS

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The more a marketing paradigm evolves, the more long-term relationship with customers gains its importance. Nowadays most of corporations and firms in the world, including manufacturers and servicers, increasingly gain their incomes and profits through constructing and maintaining long-term relationship with customers. The move towards a customer-centered approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Furthermore as marketing endeavours to be more accountable, the need of tools and models for measuring and evaluating efforts and investments that accomplish in marketing extent, is felt. This research aimed to present a framework to analysis customer lifetime value in order to strategic marketing practices.

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“…Baesens et al ( Baesens et al 2004) used Bayesian network classifiers to estimate the parameters of where in the life cycle a customer might currently be. Benoit and van den Poel ( 2009) have used quantile regression to estimate customer lifetime value. All these models concentrate on the purchase aspects -time to and value of next purchase and churn-and do not include the default risk elements which can affect profitability in a major way.…”

Section: Challenge 8: Modelling Loss Given Default and The Collectionmentioning

confidence: 99%

Consumer finance: challenges for operational research

Thomas

2010

Journal of the Operational Research Society

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: Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance-and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance.

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Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

Cited by 88 publications

References 37 publications

Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach

Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach

Customer Lifetime Value Analysis to Profitability Management: A Framework to Strategic Marketing

Consumer finance: challenges for operational research

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