IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03027981.html
   My bibliography  Save this paper

The Improvement of Retargeting by Big Data: a Decision Support that Threatens the Brand Image?

Author

Listed:
  • Maria Mercanti-Guérin

    (IAE Paris - Sorbonne Business School)

Abstract

With the emergence of Big Data and the increasing market penetration of ad retargeting advertising, the advertising industry's interest in using this new online marketing method is rising. Retargeting is an innovative technology based on Big Data. People who have gone to a merchant site and window-shopped but not purchased can be re-pitched with the product they showed an interest in. Therefore click rates and conversion rates are dramatically enhancing by retargeting. However, in spite of the increasing number of companies investing in retargeting, there is little academic research on this topic. In this paper we explore the links between retargeting, perceived intrusiveness and brand image. As results show the importance of perceived intrusiveness, ad repetition and ad relevance, we introduce new analytical perspectives on online strategies with the goal of facilitating collaboration between consumers and marketers.

Suggested Citation

  • Maria Mercanti-Guérin, 2020. "The Improvement of Retargeting by Big Data: a Decision Support that Threatens the Brand Image?," Post-Print hal-03027981, HAL.
  • Handle: RePEc:hal:journl:hal-03027981
    Note: View the original document on HAL open archive server: https://hal.science/hal-03027981
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03027981/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    2. Insaf Khelladi & Sylvaine Castellano & Laurie Limongi, 2013. "The impact of profile and location-based personalization on customer behavior in a mobile context," Post-Print hal-01514498, HAL.
    3. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    4. Reichlin, Lucrezia, 2002. "Factor Models in Large Cross-Sections of Time Series," CEPR Discussion Papers 3285, C.E.P.R. Discussion Papers.
    5. Mathias Dewatripont & Lars Peter Hansen & Stephen Turnovsky, 2003. "Advances in economics and econometrics :theory and applications," ULB Institutional Repository 2013/9557, ULB -- Universite Libre de Bruxelles.
    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. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    2. Ioannides, Yannis M. & Soetevent, Adriaan R., 2007. "Social networking and individual outcomes beyond the mean field case," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 369-390.
    3. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    4. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
    5. Rao, B. Bhaskara, 2010. "Estimates of the steady state growth rates for selected Asian countries with an extended Solow model," Economic Modelling, Elsevier, vol. 27(1), pages 46-53, January.
    6. Cho, Seo-young & Vadlamannati, Krishna Chaitanya, 2010. "Compliance for big brothers: An empirical analysis on the impact of the anti-trafficking protocol," University of Göttingen Working Papers in Economics 118, University of Goettingen, Department of Economics.
    7. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    8. John Geanakoplos & Robert Axtell & J. Doyne Farmer & Peter Howitt & Benjamin Conlee & Jonathan Goldstein & Matthew Hendrey & Nathan M. Palmer & Chun-Yi Yang, 2012. "Getting at Systemic Risk via an Agent-Based Model of the Housing Market," American Economic Review, American Economic Association, vol. 102(3), pages 53-58, May.
    9. Jeffrey Frankel, 2014. "Mauritius: African Success Story," NBER Chapters, in: African Successes, Volume IV: Sustainable Growth, pages 295-342, National Bureau of Economic Research, Inc.
    10. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
    11. Balima, Hippolyte Weneyam, 2020. "Coups d’état and the cost of debt," Journal of Comparative Economics, Elsevier, vol. 48(3), pages 509-528.
    12. Christian Hellwig, 2004. "Heterogeneous Information and the Benefits of Public Information Disclosures (October 2005)," UCLA Economics Online Papers 283, UCLA Department of Economics.
    13. Saaed, A.A.J., 2007. "Inflation and Economic Growth in Kuwait: 1985-2005. Evidence from Co-Integration and Error Correction Model," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(1).
    14. Chong, Alberto E., 2006. "Does It Matter How People Speak?," IDB Publications (Working Papers) 1946, Inter-American Development Bank.
    15. Gärtner, D.L. & Zhou, J., 2012. "Delays in Leniency Application : Is There Really a Race to the Enforcer’s Door?," Other publications TiSEM cbb8fac0-0cd7-4a0c-a6d4-a, Tilburg University, School of Economics and Management.
    16. Saki Bigio & Eduardo Zilberman, 2020. "Speculation-Driven Business Cycles," Working Papers Central Bank of Chile 865, Central Bank of Chile.
    17. Arturo Galindo & Alberto Chong & César Calderón, 2001. "Structure and Development of Financial Institutions and Links with Trust: Cross-Country Evidence," Research Department Publications 4251, Inter-American Development Bank, Research Department.
    18. Long Xue & Qianyu Zhang & Xuemang Zhang & Chengyu Li, 2022. "Can Digital Transformation Promote Green Technology Innovation?," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    19. Vojtěch Roženský, 2012. "Mandatorní výdaje a flexibilita fiskální politiky v ČR [Mandatory Expenditure and the Flexibility of Fiscal Policy in the Czech Republic]," Politická ekonomie, Prague University of Economics and Business, vol. 2012(1), pages 40-57.
    20. Mothuti Gosego & Phiri Andrew, 2018. "Inflation-Growth Nexus in Botswana: Can Lower Inflation Really Spur Growth in the Country?," Global Economy Journal, De Gruyter, vol. 18(4), pages 1-11, December.

    More about this item

    Keywords

    Big Data; Retargeting; Perceived Intrusiveness; Ad Relevance;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hal:journl:hal-03027981. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.