IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v21y2022i4d10.1057_s41272-021-00339-6.html
   My bibliography  Save this article

Nonparametric estimation of customer segments from censored sales panel data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00339-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-021-00339-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    9. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    2. Barbier, Thibault & Anjos, Miguel F. & Cirinei, Fabien & Savard, Gilles, 2020. "Product-closing approximation for ranking-based choice network revenue management," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1002-1017.
    3. Dimitris Bertsimas & Velibor V. Mišić, 2019. "Exact First-Choice Product Line Optimization," Operations Research, INFORMS, vol. 67(3), pages 651-670, May.
    4. 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.
    5. Qi Feng & J. George Shanthikumar & Mengying Xue, 2022. "Consumer Choice Models and Estimation: A Review and Extension," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 847-867, February.
    6. Catherine Cleophas & Daniel Kadatz & Sebastian Vock, 2017. "Resilient revenue management: a literature survey of recent theoretical advances," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 483-498, October.
    7. Garrett van Ryzin & Gustavo Vulcano, 2017. "Technical Note—An Expectation-Maximization Method to Estimate a Rank-Based Choice Model of Demand," Operations Research, INFORMS, vol. 65(2), pages 396-407, April.
    8. Flores, Alvaro & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2019. "Assortment optimization under the Sequential Multinomial Logit Model," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1052-1064.
    9. Guang Li & Paat Rusmevichientong & Huseyin Topaloglu, 2015. "The d -Level Nested Logit Model: Assortment and Price Optimization Problems," Operations Research, INFORMS, vol. 63(2), pages 325-342, April.
    10. Sanjay Dominik Jena & Andrea Lodi & Claudio Sole, 2021. "On the estimation of discrete choice models to capture irrational customer behaviors," Papers 2109.03882, arXiv.org.
    11. Antoine Désir & Vineet Goyal & Danny Segev & Chun Ye, 2020. "Constrained Assortment Optimization Under the Markov Chain–based Choice Model," Management Science, INFORMS, vol. 66(2), pages 698-721, February.
    12. Hans Corsten & Michael Hopf & Benedikt Kasper & Clemens Thielen, 2018. "Assortment planning for multiple chain stores," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 875-912, October.
    13. Jacob Feldman & Alice Paul & Huseyin Topaloglu, 2019. "Technical Note—Assortment Optimization with Small Consideration Sets," Operations Research, INFORMS, vol. 67(5), pages 1283-1299, September.
    14. Ali Aouad & Vivek Farias & Retsef Levi, 2021. "Assortment Optimization Under Consider-Then-Choose Choice Models," Management Science, INFORMS, vol. 67(6), pages 3368-3386, June.
    15. Kumar Goutam & Vineet Goyal & Agathe Soret, 2019. "A Generalized Markov Chain Model to Capture Dynamic Preferences and Choice Overload," Papers 1911.06716, arXiv.org, revised Dec 2020.
    16. Shivaram Subramanian & Pavithra Harsha, 2021. "Demand Modeling in the Presence of Unobserved Lost Sales," Management Science, INFORMS, vol. 67(6), pages 3803-3833, June.
    17. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 0. "Discovering customer types using sales transactions and product availability data of 5 hotel datasets with genetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 0, pages 1-15.
    18. Xiyuan Ren & Joseph Y. J. Chow, 2023. "Nonparametric estimation of k-modal taste heterogeneity for group level agent-based mixed logit," Papers 2309.13159, arXiv.org.
    19. James M. Davis & Guillermo Gallego & Huseyin Topaloglu, 2014. "Assortment Optimization Under Variants of the Nested Logit Model," Operations Research, INFORMS, vol. 62(2), pages 250-273, April.
    20. Ali Aouad & Danny Segev, 2021. "Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences," Management Science, INFORMS, vol. 67(6), pages 3519-3550, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:21:y:2022:i:4:d:10.1057_s41272-021-00339-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.