Nonparametric estimation of customer segments from censored sales panel data

Johannes F. Jörg () and Catherine Cleophas ()
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Johannes F. Jörg: RWTH Aachen University Kackertstraße 7
Catherine Cleophas: Christian-Albrechts-Universität zu Kiel

Journal of Revenue and Pricing Management, 2022, vol. 21, issue 4, No 3, 393-417

Abstract: 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.

Keywords: Nonparametric statistics; Demand estimation; Demand segmentation; Censored panel data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1057/s41272-021-00339-6

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