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Nonparametric estimation of customer segments from censored sales panel data

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  • Johannes F. Jörg

    (RWTH Aachen University Kackertstraße 7)

  • Catherine Cleophas

    (Christian-Albrechts-Universität zu Kiel)

Abstract

Specifically addressing different customer segments via revenue management or customer relationship management, lets firms optimize their market response. Identifying such segments requires analysing large amounts of transactional data. We present a nonparametric approach to estimate the number of customer segments from censored panel data. We evaluate several model selection criteria and imputation methods to compensate for censored observations under different demand scenarios. We measure estimation performance in a controlled environment via simulated data samples, benchmark it to common clustering indices and imputation methods, and analyse an empirical data sample to validate practical applicability.

Suggested Citation

  • Johannes F. Jörg & Catherine Cleophas, 2022. "Nonparametric estimation of customer segments from censored sales panel data," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(4), pages 393-417, August.
    • Handle: RePEc:pal:jorapm:v:21:y:2022:i:4:d:10.1057_s41272-021-00339-6
    DOI: 10.1057/s41272-021-00339-6
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    References listed on IDEAS

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    1. Hiroyuki Kasahara & Katsumi Shimotsu, 2014. "Non-parametric identification and estimation of the number of components in multivariate mixtures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 97-111, January.
    2. Vivek F. Farias & Srikanth Jagabathula & Devavrat Shah, 2013. "A Nonparametric Approach to Modeling Choice with Limited Data," Management Science, INFORMS, vol. 59(2), pages 305-322, December.
    3. Garrett van Ryzin & Gustavo Vulcano, 2015. "A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models," Management Science, INFORMS, vol. 61(2), pages 281-300, February.
    4. Shadi Azadeh & M. Hosseinalifam & G. Savard, 2015. "The impact of customer behavior models on revenue management systems," Computational Management Science, Springer, vol. 12(1), pages 99-109, January.
    5. Agresti, Alan & Caffo, Brian & Ohman-Strickland, Pamela, 2004. "Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 639-653, October.
    6. Sumit Kunnumkal, 2014. "Randomization Approaches for Network Revenue Management with Customer Choice Behavior," Production and Operations Management, Production and Operations Management Society, vol. 23(9), pages 1617-1633, September.
    7. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    8. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    9. Paat Rusmevichientong & David Shmoys & Chaoxu Tong & Huseyin Topaloglu, 2014. "Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters," Production and Operations Management, Production and Operations Management Society, vol. 23(11), pages 2023-2039, November.
    10. Kalyan Talluri, 2009. "A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters," Economics Working Papers 1141, Department of Economics and Business, Universitat Pompeu Fabra.
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