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Developing A Service Improvement System for the National Dutch Railways

Author

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  • Peter C. Verhoef

    (University of Groningen, 9712 CP Groningen, Netherlands)

  • Martin Heijnsbroek

    (MIcompany, 1017 HL Amsterdam, Netherlands)

  • Joost Bosma

    (National Dutch Railways, 3511 ER Utrecht, Netherlands)

Abstract

Customer satisfaction is essential for public and railway services, because firms in these industries have contracts with governments requiring them to achieve specific customer satisfaction targets. In this paper, we describe a National Dutch Railways project in which we identify the major determinants of customer satisfaction. By combining multiple data sources, we link operational and marketing data to customer satisfaction. Our models show that train punctuality and sufficient seating within a train are important factors in customer satisfaction, as are other service elements, such as the availability of Wi-Fi in a train and the condition of facilities at a station. Drawing on the model results, the National Dutch Railways pursued initiatives to increase customer satisfaction. Among these initiatives is the development of an application that allows passengers to check seating availability, the design of a marketing dashboard reflecting developments in customer satisfaction, and the creation of a tool to identify the most critical determinants of customer satisfaction.

Suggested Citation

  • Peter C. Verhoef & Martin Heijnsbroek & Joost Bosma, 2017. "Developing A Service Improvement System for the National Dutch Railways," Interfaces, INFORMS, vol. 47(6), pages 489-504, December.
  • Handle: RePEc:inm:orinte:v:47:y:2017:i:6:p:489-504
    DOI: 10.1287/inte.2017.0915
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    References listed on IDEAS

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    1. Leo Kroon & Dennis Huisman & Erwin Abbink & Pieter-Jan Fioole & Matteo Fischetti & Gábor Maróti & Alexander Schrijver & Adri Steenbeek & Roelof Ybema, 2009. "The New Dutch Timetable: The OR Revolution," Interfaces, INFORMS, vol. 39(1), pages 6-17, February.
    2. Feld, Sebastian & Frenzen, Heiko & Krafft, Manfred & Peters, Kay & Verhoef, Peter C., 2013. "The effects of mailing design characteristics on direct mail campaign performance," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 143-159.
    3. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, October.
    4. de Haan, Evert & Verhoef, Peter C. & Wiesel, Thorsten, 2015. "The predictive ability of different customer feedback metrics for retention," International Journal of Research in Marketing, Elsevier, vol. 32(2), pages 195-206.
    5. Verhoef, P.C. & Donkers, A.C.D., 2001. "Predicting Customer Potential Value: an application in the insurance industry," ERIM Report Series Research in Management ERS-2001-01-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    Cited by:

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