IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v135y2021icp112-126.html
   My bibliography  Save this article

Leveraging non-respondent data in customer satisfaction modeling

Author

Listed:
  • Zihayat, Morteza
  • Ayanso, Anteneh
  • Davoudi, Heidar
  • Kargar, Mehdi
  • Mengesha, Nigussie

Abstract

Understanding the high likelihood of a dissatisfied customer leaving, customer satisfaction modeling has received significant attention by marketers and academic research. The major challenge in customer satisfaction modeling is the low response rate of surveys and the potential loss of valuable insights from non-respondents. We introduce a modeling framework that allows marketers to leverage existing information about non-respondents for predicting customer satisfaction at a specific time. We design a novel procedure to discover data-driven attributes that effectively represent the interactions of customers. Then, we propose a time-aware model to predict customer satisfaction or dissatisfaction and the time of events. We also design a learn-to-rank model to leverage non-respondents data for building a more accurate customer satisfaction model. A real-world dataset from an insurance company shows that the proposed framework accurately identifies satisfied or dissatisfied customers at a specific time and achieves a significantly better performance compared to extant methods.

Suggested Citation

  • Zihayat, Morteza & Ayanso, Anteneh & Davoudi, Heidar & Kargar, Mehdi & Mengesha, Nigussie, 2021. "Leveraging non-respondent data in customer satisfaction modeling," Journal of Business Research, Elsevier, vol. 135(C), pages 112-126.
    • Handle: RePEc:eee:jbrese:v:135:y:2021:i:c:p:112-126
    DOI: 10.1016/j.jbusres.2021.06.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296321004227
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2021.06.006?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. Lee, Hyoung-joo & Shin, Hyunjung & Hwang, Seong-seob & Cho, Sungzoon & MacLachlan, Douglas, 2010. "Semi-Supervised Response Modeling," Journal of Interactive Marketing, Elsevier, vol. 24(1), pages 42-54.
    2. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    3. Haenlein, Michael, 2013. "Social interactions in customer churn decisions: The impact of relationship directionality," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 236-248.
    4. Ferreira, D.C. & Marques, R.C. & Nunes, A.M. & Figueira, J.R., 2018. "Patients’ satisfaction: The medical appointments valence in Portuguese public hospitals," Omega, Elsevier, vol. 80(C), pages 58-76.
    5. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    6. Jin Cao & Zhibin Jiang & Kangzhou Wang, 2016. "Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction," International Journal of Production Research, Taylor & Francis Journals, vol. 54(5), pages 1303-1321, March.
    7. Jian-Wu Bi & Yang Liu & Zhi-Ping Fan & Erik Cambria, 2019. "Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7068-7088, November.
    8. van Doorn, Jenny & Leeflang, Peter S.H. & Tijs, Marleen, 2013. "Satisfaction as a predictor of future performance: A replication," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 314-318.
    9. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    10. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    11. Hwang, Syjung & Kim, Jina & Park, Eunil & Kwon, Sang Jib, 2020. "Who will be your next customer: A machine learning approach to customer return visits in airline services," Journal of Business Research, Elsevier, vol. 121(C), pages 121-126.
    12. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    13. Anita Pansari & V. Kumar, 2017. "Customer engagement: the construct, antecedents, and consequences," Journal of the Academy of Marketing Science, Springer, vol. 45(3), pages 294-311, May.
    14. Kos Koklic, Mateja & Kukar-Kinney, Monika & Vegelj, Spela, 2017. "An investigation of customer satisfaction with low-cost and full-service airline companies," Journal of Business Research, Elsevier, vol. 80(C), pages 188-196.
    15. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    16. Miranda, Sandra & Tavares, Patrícia & Queiró, Rita, 2018. "Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector," Journal of Business Research, Elsevier, vol. 89(C), pages 371-377.
    17. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.
    18. Hon Keung Yau & Ho Yi Horace Tang, 2018. "Analyzing customer satisfaction in self-service technology adopted in airports," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(1), pages 6-18, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fawz Manyaga & Umit Hacioglu, 2021. "Investigating the impact of mobile telecom service characteristics on consumer satisfaction in urban Uganda," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(6), pages 19-33, September.

    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. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    2. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    3. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    4. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    5. Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    6. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    7. Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    8. Ebru Pekel Ozmen & Tuncay Ozcan, 2022. "A novel deep learning model based on convolutional neural networks for employee churn prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 539-550, April.
    9. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    10. Lamrhari, Soumaya & Ghazi, Hamid El & Oubrich, Mourad & Faker, Abdellatif El, 2022. "A social CRM analytic framework for improving customer retention, acquisition, and conversion," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    11. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    12. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2021. "A multi-period analysis of the integrated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 292(2), pages 483-499.
    13. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    14. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
    15. Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021. "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers 2105.07671, arXiv.org, revised Jul 2021.
    16. Gary Mena & Kristof Coussement & Koen W. Bock & Arno Caigny & Stefan Lessmann, 2024. "Exploiting time-varying RFM measures for customer churn prediction with deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 765-787, August.
    17. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
    18. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    19. Lewlisa Saha & Hrudaya Kumar Tripathy & Soumya Ranjan Nayak & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review," Sustainability, MDPI, vol. 13(9), pages 1-35, May.
    20. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.

    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:eee:jbrese:v:135:y:2021:i:c:p:112-126. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.