IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v8y2020i1d10.1057_s41270-020-00068-6.html
   My bibliography  Save this article

Protecting survey data on a consumer level

Author

Listed:
  • Matthew J. Schneider

    (Drexel University)

  • Dawn Iacobucci

    (Vanderbilt University)

Abstract

This paper offers an easy-to-implement approach to protect multivariate survey data common in marketing, such as attitudes and demographics. Our approach preserves multivariate distributions by releasing a protected data set with privacy protections. The data represent a highly detailed multivariate survey with severe privacy issues that enables us to demonstrate the tradeoff between data utility and data privacy. We create a data privacy metric that quantifies the ability of a data intruder successfully identify survey respondents and their sensitive responses. We provide data privacy measurements for a variety of competitor methods such as sampling and random noise addition and we show that by comparison, our approach can prevent a data intruder from targeting individuals while maintaining a very high level of data utility.

Suggested Citation

  • Matthew J. Schneider & Dawn Iacobucci, 2020. "Protecting survey data on a consumer level," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(1), pages 3-17, March.
    • Handle: RePEc:pal:jmarka:v:8:y:2020:i:1:d:10.1057_s41270-020-00068-6
    DOI: 10.1057/s41270-020-00068-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-020-00068-6
    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/s41270-020-00068-6?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. Schneider, Matthew J. & Jagpal, Sharan & Gupta, Sachin & Li, Shaobo & Yu, Yan, 2017. "Protecting customer privacy when marketing with second-party data," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 593-603.
    2. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    3. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    4. Amalia R. Miller & Catherine Tucker, 2009. "Privacy Protection and Technology Diffusion: The Case of Electronic Medical Records," Management Science, INFORMS, vol. 55(7), pages 1077-1093, July.
    5. John C. Duchi & Michael I. Jordan & Martin J. Wainwright, 2018. "Minimax Optimal Procedures for Locally Private Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 182-201, January.
    6. Matthew J. Schneider & Sharan Jagpal & Sachin Gupta & Shaobo Li & Yan Yu, 2018. "A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data," Marketing Science, INFORMS, vol. 37(1), pages 153-171, January.
    7. Ng, Irene C.L. & Wakenshaw, Susan Y.L., 2017. "The Internet-of-Things: Review and research directions," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 3-21.
    8. Holtrop, Niels & Wieringa, Jaap E. & Gijsenberg, Maarten J. & Verhoef, Peter C., 2017. "No future without the past? Predicting churn in the face of customer privacy," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 154-172.
    9. Graeme Blair & Kosuke Imai & Yang-Yang Zhou, 2015. "Design and Analysis of the Randomized Response Technique," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1304-1319, September.
    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. Maria Petrescu & Anjala S. Krishen, 2021. "A tribute to our heroes and thoughts about collaborative relationships," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 81-82, June.

    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. Wieringa, Jaap & Kannan, P.K. & Ma, Xiao & Reutterer, Thomas & Risselada, Hans & Skiera, Bernd, 2021. "Data analytics in a privacy-concerned world," Journal of Business Research, Elsevier, vol. 122(C), pages 915-925.
    2. Verhoef, Peter C. & Stephen, Andrew T. & Kannan, P.K. & Luo, Xueming & Abhishek, Vibhanshu & Andrews, Michelle & Bart, Yakov & Datta, Hannes & Fong, Nathan & Hoffman, Donna L. & Hu, Mandy Mantian & No, 2017. "Consumer Connectivity in a Complex, Technology-enabled, and Mobile-oriented World with Smart Products," Journal of Interactive Marketing, Elsevier, vol. 40(C), pages 1-8.
    3. Bandara, Ruwan & Fernando, Mario & Akter, Shahriar, 2020. "Explicating the privacy paradox: A qualitative inquiry of online shopping consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    4. Shaobo Li & Matthew J. Schneider & Yan Yu & Sachin Gupta, 2023. "Reidentification Risk in Panel Data: Protecting for k -Anonymity," Information Systems Research, INFORMS, vol. 34(3), pages 1066-1088, September.
    5. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    6. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    7. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    8. Stefan Vamosi & Michael Platzer & Thomas Reutterer, 2022. "AI-based Re-identification of Behavioral Clickstream Data," Papers 2201.10351, arXiv.org.
    9. Muhammad Farooq & Valliappan Raju, 2019. "Want to Stay the Market Leader in the Era of Transformative Marketing? Keep the Customers Satisfied!," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 257-266, September.
    10. Ritu Agarwal & Michelle Dugas & Guodong (Gordon) Gao & P. K. Kannan, 2020. "Emerging technologies and analytics for a new era of value-centered marketing in healthcare," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 9-23, January.
    11. 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.
    12. Dominik M. Wielgos & Christian Homburg & Christina Kuehnl, 2021. "Digital business capability: its impact on firm and customer performance," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 762-789, July.
    13. Krzysztof BORODAKO & Jadwiga BERBEKA & Michał RUDNICKI & Mariusz ŠAPCZYŃSKI, 2021. "Online Visibility and Knowledge-Intensive Business Services Performance: The Scope of Interrelatedness," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 157-173, August.
    14. Arora, Ashish & Forman, Chris & Nandkumar, Anand & Telang, Rahul, 2010. "Competition and patching of security vulnerabilities: An empirical analysis," Information Economics and Policy, Elsevier, vol. 22(2), pages 164-177, May.
    15. Castro, Jordi, 2012. "Recent advances in optimization techniques for statistical tabular data protection," European Journal of Operational Research, Elsevier, vol. 216(2), pages 257-269.
    16. Cai, Ya-Jun & Lo, Chris K.Y., 2020. "Omni-channel management in the new retailing era: A systematic review and future research agenda," International Journal of Production Economics, Elsevier, vol. 229(C).
    17. Carsten D. Schultz & Björn Gorlas, 2023. "Magic mirror on the wall: Cross-buying at the point of sale," Electronic Commerce Research, Springer, vol. 23(3), pages 1677-1700, September.
    18. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    19. Sylvain Chassang & Christian Zehnder, 2019. "Secure Survey Design in Organizations: Theory and Experiments," Working Papers 2019-22, Princeton University. Economics Department..
    20. Nikita Moiseev & Alexey Mikhaylov & Hasan Dinçer & Serhat Yüksel, 2023. "Market capitalization shock effects on open innovation models in e-commerce: golden cut q-rung orthopair fuzzy multicriteria decision-making analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.

    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:jmarka:v:8:y:2020:i:1:d:10.1057_s41270-020-00068-6. 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-journals.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.