Time-varying effects in the analysis of customer loyalty: A case study in insurance

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Time-varying effects in the analysis of customer loyalty : A case study in insurance. / Guillen, Montserrat; Perch Nielsen, Jens; Scheike, Thomas; Pérez-Marín, Ana Maria.

In: Expert Systems with Applications, Vol. 39, No. 3, 2011, p. 3551-3558.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Guillen, M, Perch Nielsen, J, Scheike, T & Pérez-Marín, AM 2011, 'Time-varying effects in the analysis of customer loyalty: A case study in insurance', Expert Systems with Applications, vol. 39, no. 3, pp. 3551-3558. https://doi.org/10.1016/j.eswa.2011.09.045

APA

Guillen, M., Perch Nielsen, J., Scheike, T., & Pérez-Marín, A. M. (2011). Time-varying effects in the analysis of customer loyalty: A case study in insurance. Expert Systems with Applications, 39(3), 3551-3558. https://doi.org/10.1016/j.eswa.2011.09.045

Vancouver

Guillen M, Perch Nielsen J, Scheike T, Pérez-Marín AM. Time-varying effects in the analysis of customer loyalty: A case study in insurance. Expert Systems with Applications. 2011;39(3):3551-3558. https://doi.org/10.1016/j.eswa.2011.09.045

Author

Guillen, Montserrat ; Perch Nielsen, Jens ; Scheike, Thomas ; Pérez-Marín, Ana Maria. / Time-varying effects in the analysis of customer loyalty : A case study in insurance. In: Expert Systems with Applications. 2011 ; Vol. 39, No. 3. pp. 3551-3558.

Bibtex

@article{eb5dff6d55ae41e783910a488ba0a3d6,
title = "Time-varying effects in the analysis of customer loyalty: A case study in insurance",
abstract = "Insurance customers usually hold more than one contract with the same insurer. A generalization of classical survival analysis methods is used to examine the risk of losing a customer once an initial insurance policy cancellation has occurred. This method does not assume that the model parameters are fixed over time, but rather that the parameters may fluctuate. Our results suggest that the kind of contracts held by customers and the concurrence of an external competitor strongly influence customer loyalty right after that cancellation, but those factors become much less significant some months later. Our study shows how predictions of the probability of losing a customer can be readjusted and improves the way companies manage business risk.",
author = "Montserrat Guillen and {Perch Nielsen}, Jens and Thomas Scheike and P{\'e}rez-Mar{\'i}n, {Ana Maria}",
year = "2011",
doi = "10.1016/j.eswa.2011.09.045",
language = "English",
volume = "39",
pages = "3551--3558",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Pergamon Press",
number = "3",

}

RIS

TY - JOUR

T1 - Time-varying effects in the analysis of customer loyalty

T2 - A case study in insurance

AU - Guillen, Montserrat

AU - Perch Nielsen, Jens

AU - Scheike, Thomas

AU - Pérez-Marín, Ana Maria

PY - 2011

Y1 - 2011

N2 - Insurance customers usually hold more than one contract with the same insurer. A generalization of classical survival analysis methods is used to examine the risk of losing a customer once an initial insurance policy cancellation has occurred. This method does not assume that the model parameters are fixed over time, but rather that the parameters may fluctuate. Our results suggest that the kind of contracts held by customers and the concurrence of an external competitor strongly influence customer loyalty right after that cancellation, but those factors become much less significant some months later. Our study shows how predictions of the probability of losing a customer can be readjusted and improves the way companies manage business risk.

AB - Insurance customers usually hold more than one contract with the same insurer. A generalization of classical survival analysis methods is used to examine the risk of losing a customer once an initial insurance policy cancellation has occurred. This method does not assume that the model parameters are fixed over time, but rather that the parameters may fluctuate. Our results suggest that the kind of contracts held by customers and the concurrence of an external competitor strongly influence customer loyalty right after that cancellation, but those factors become much less significant some months later. Our study shows how predictions of the probability of losing a customer can be readjusted and improves the way companies manage business risk.

U2 - 10.1016/j.eswa.2011.09.045

DO - 10.1016/j.eswa.2011.09.045

M3 - Journal article

VL - 39

SP - 3551

EP - 3558

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3

ER -

ID: 38374201