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Database Submission--The ISMS Durable Goods Data Sets

Author

Listed:
  • Jian Ni

    (Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202)

  • Scott A. Neslin

    (Tuck School of Business at Dartmouth, Dartmouth College, Hanover, New Hampshire 03755)

  • Baohong Sun

    (Cheung Kong Graduate School of Business, New York, New York 10019)

Abstract

This paper describes two new data sets available to academic researchers (at http://www.informs.org/Community/ISMS ). The first is a panel data set containing the transactions of 19,936 households made over the period from December 1998 to November 2004 at a major U.S. consumer electronics retailer. There are a total of 173,262 transactions, including purchases and returns of products as well as extended warranties. There are 16 product categories and 292 subcategories, ranging from big-ticket items such as televisions to small-ticket items such as CDs and batteries. The second data set features a field experiment for a Christmas promotion that took place in December 2003 in the form of a direct mailing sent to a randomly selected group of households at the end of November 2003. We describe the data and the potential research issues that can be studied using these two durable goods data sets.

Suggested Citation

  • Jian Ni & Scott A. Neslin & Baohong Sun, 2012. "Database Submission--The ISMS Durable Goods Data Sets," Marketing Science, INFORMS, vol. 31(6), pages 1008-1013, November.
    • Handle: RePEc:inm:ormksc:v:31:y:2012:i:6:p:1008-1013
    DOI: 10.1287/mksc.1120.0726
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    Citations

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    Cited by:

    1. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    2. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    3. Michele Samorani & Aydın Alptekinoğlu & Paul R. Messinger, 2019. "Product Return Episodes in Retailing," Service Science, INFORMS, vol. 11(4), pages 263-278, December.
    4. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    5. Yoonju Han & Sandeep R. Chandukala & Hai Che, 2017. "Exchange and refund of complementary products," Marketing Letters, Springer, vol. 28(1), pages 113-125, March.
    6. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    7. El Kihal, Siham & Nurullayev, Namig & Schulze, Christian & Skiera, Bernd, 2021. "A Comparison of Return Rate Calculation Methods: Evidence from 16 Retailers," Journal of Retailing, Elsevier, vol. 97(4), pages 676-696.

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