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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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