IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v34y2017i2p480-498.html
   My bibliography  Save this article

The impact of contextual television ads on online conversions: An application in the insurance industry

Author

Listed:
  • Guitart, Ivan A.
  • Hervet, Guillaume

Abstract

The use of contextual advertising, defined as the delivery of ads in related editorial contents, is expected to grow in the television industry due to the development of new technologies. However, the impact of this practice on behavioral consumer responses remains elusive. Using data from an online price comparison company, we study the short- and long-term effects of contextual television advertising on consumers' online conversions. We find that contextual ads have a higher immediate effect than non-contextual ads on consumers' responses for the product that is related to the context. However, both effects are similar in the long term. Additionally, our results suggest that the total impact of contextual advertising can be smaller than the net impact of neutral advertising due to a “spillover inhibition effect”: Compared with ads placed in neutral contexts, contextual ads generate fewer online conversions for products that are unrelated to the context. Our findings stress the importance of assessing the long-term impact of contextual advertising on the product portfolio, rather than just on the context-related product. Failing to do so could lead to advertising allocation decisions with lower returns on investment.

Suggested Citation

  • Guitart, Ivan A. & Hervet, Guillaume, 2017. "The impact of contextual television ads on online conversions: An application in the insurance industry," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 480-498.
    • Handle: RePEc:eee:ijrema:v:34:y:2017:i:2:p:480-498
    DOI: 10.1016/j.ijresmar.2016.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811616301288
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijresmar.2016.10.002?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. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    2. Joo, Mingyu & Wilbur, Kenneth C. & Zhu, Yi, 2016. "Effects of TV advertising on keyword search," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 508-523.
    3. Lee, Ju-Yeon & Sridhar, Shrihari & Palmatier, Robert W., 2017. "The effect of firms' structural designs on advertising and personal selling returns," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 173-193.
    4. Theodore J. Noseworthy & Fabrizio Di Muro & Kyle B. Murray, 2014. "The Role of Arousal in Congruity-Based Product Evaluation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(4), pages 1108-1126.
    5. Peter E. Rossi, 2014. "Invited Paper —Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications," Marketing Science, INFORMS, vol. 33(5), pages 655-672, September.
    6. Russell, Cristel Antonia, 2002. "Investigating the Effectiveness of Product Placements in Television Shows: The Role of Modality and Plot Connection Congruence on Brand Memory and Attitude," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(3), pages 306-318, December.
    7. Jan R. Landwehr & Aparna A. Labroo & Andreas Herrmann, 2011. "Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts," Marketing Science, INFORMS, vol. 30(3), pages 416-429, 05-06.
    8. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    10. Theodore J. Noseworthy & Fabrizio Di Muro & Kyle B. Murray, 2014. "The Role of Arousal in Congruity-Based Product Evaluation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(4), pages 1108-1126.
    11. Vincent R. Nijs & Marnik G. Dekimpe & Jan-Benedict E.M. Steenkamps & Dominique M. Hanssens, 2001. "The Category-Demand Effects of Price Promotions," Marketing Science, INFORMS, vol. 20(1), pages 1-22, September.
    12. de Haan, Evert & Wiesel, Thorsten & Pauwels, Koen, 2016. "The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 491-507.
    13. Burke, Raymond R & Srull, Thomas K, 1988. "Competitive Interference and Consumer Memory for Advertising," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(1), pages 55-68, June.
    14. Pauwels, Koen & Neslin, Scott A., 2015. "Building With Bricks and Mortar: The Revenue Impact of Opening Physical Stores in a Multichannel Environment," Journal of Retailing, Elsevier, vol. 91(2), pages 182-197.
    15. Natalie Mizik & Robert Jacobson, 2004. "Are Physicians ÜEasy MarksÝ? Quantifying the Effects of Detailing and Sampling on New Prescriptions," Management Science, INFORMS, vol. 50(12), pages 1704-1715, December.
    16. Gerard J. Tellis & Philip Hans Franses, 2006. "Optimal Data Interval for Estimating Advertising Response," Marketing Science, INFORMS, vol. 25(3), pages 217-229, 05-06.
    17. Krinsky, Itzhak & Robb, A Leslie, 1986. "On Approximating the Statistical Properties of Elasticities," The Review of Economics and Statistics, MIT Press, vol. 68(4), pages 715-719, November.
    18. Virginie Maille & Nathalie Fleck, 2011. "Perceived Congruence and Incongruence: Toward a Clarification of the Concept, its Formation and Measure," Post-Print hal-02291857, HAL.
    19. Roland T. Rust & Duncan Simester & Roderick J. Brodie & V. Nilikant, 1995. "Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria," Management Science, INFORMS, vol. 41(2), pages 322-333, February.
    20. Ganesh Iyer & David Soberman & J. Miguel Villas-Boas, 2005. "The Targeting of Advertising," Marketing Science, INFORMS, vol. 24(3), pages 461-476, May.
    21. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    22. Meyers-Levy, Joan & Tybout, Alice M, 1989. "Schema Congruity as a Basis for Product Evaluation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(1), pages 39-54, June.
    23. Dirk Bergemann & Alessandro Bonatti, 2011. "Targeting in advertising markets: implications for offline versus online media," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 417-443, September.
    24. P. De Pelsmacker & M. Geuens & P. Anckaert, 2002. "Media context and advertising effectiveness: The role of context appreciation and context-ad similarity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 02/162, Ghent University, Faculty of Economics and Business Administration.
    25. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    26. Kaifu Zhang & Zsolt Katona, 2012. "Contextual Advertising," Marketing Science, INFORMS, vol. 31(6), pages 980-994, November.
    27. Mingyu Joo & Kenneth C. Wilbur & Bo Cowgill & Yi Zhu, 2014. "Television Advertising and Online Search," Management Science, INFORMS, vol. 60(1), pages 56-73, January.
    28. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    29. Goldberg, Marvin E & Gorn, Gerald J, 1987. "Happy and Sad TV Programs: How They Affect Reactions to Commercials," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 387-403, December.
    30. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    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. Lesscher, Lisan & Lobschat, Lara & Verhoef, Peter C., 2021. "Do offline and online go hand in hand? Cross-channel and synergy effects of direct mailing and display advertising," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 678-697.
    2. Hervet, Guillaume & Guitart, Ivan A., 2022. "Increasing the effectiveness of display social media ads for startups: The role of different claims and executional characteristics," Journal of Business Research, Elsevier, vol. 153(C), pages 467-478.
    3. Shankar, Venkatesh & Kushwaha, Tarun, 2021. "Omnichannel marketing: Are cross-channel effects symmetric?," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 290-310.
    4. Ivan A. Guitart & Guillaume Hervet & Sarah Gelper, 2020. "Competitive advertising strategies for programmatic television," Journal of the Academy of Marketing Science, Springer, vol. 48(4), pages 753-775, July.
    5. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.

    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. Zhang, Jianqiang & He, Xiuli, 2019. "Targeted advertising by asymmetric firms," Omega, Elsevier, vol. 89(C), pages 136-150.
    2. Peitz, Martin & Reisinger, Markus, 2014. "The Economics of Internet Media," Working Papers 14-23, University of Mannheim, Department of Economics.
    3. Guitart, Ivan A. & Gonzalez, Jorge & Stremersch, Stefan, 2018. "Advertising non-premium products as if they were premium: The impact of advertising up on advertising elasticity and brand equity," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 471-489.
    4. Lee, Ju-Yeon & Fang, Eric & Kim, Jisu J. & Li, Xiaoling & Palmatier, Robert W., 2018. "The Effect of Online Shopping Platform Strategies on Search, Display, and Membership Revenues," Journal of Retailing, Elsevier, vol. 94(3), pages 247-264.
    5. Méndez-Suárez, Mariano & Monfort, Abel, 2020. "The amplifying effect of branded queries on advertising in multi-channel retailing," Journal of Business Research, Elsevier, vol. 112(C), pages 254-260.
    6. Alex Jiyoung Kim & Subramanian Balachander, 2023. "Coordinating traditional media advertising and online advertising in brand marketing," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1865-1879, June.
    7. Anindya Ghose & Vilma Todri, 2015. "Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior," Working Papers 15-15, NET Institute.
    8. Liying Yang & Yi Qian & Hui Xie, 2024. "Handling Endogenous Marketing Mix Regressors in Correlated Heterogeneous Panels with Copula Augmented Mean Group Estimation," NBER Working Papers 33265, National Bureau of Economic Research, Inc.
    9. Seshadri Tirunillai & Gerard J. Tellis, 2017. "Does Offline TV Advertising Affect Online Chatter? Quasi-Experimental Analysis Using Synthetic Control," Marketing Science, INFORMS, vol. 36(6), pages 862-878, November.
    10. Ivan Guitart & Jorge Gonzalez & Stefan Stremersch, 2018. "Advertising non-premium products as if they were premium : The impact of advertising up on advertising elasticity and brand equity," Post-Print hal-02312175, HAL.
    11. 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.
    12. Garaus, Marion & Wagner, Udo & Rainer, Ricarda C., 2021. "Emotional targeting using digital signage systems and facial recognition at the point-of-sale," Journal of Business Research, Elsevier, vol. 131(C), pages 747-762.
    13. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    14. Randall Lewis & Dan Nguyen, 2015. "Display advertising’s competitive spillovers to consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 13(2), pages 93-115, June.
    15. Bayer, Emanuel & Srinivasan, Shuba & Riedl, Edward J. & Skiera, Bernd, 2020. "The impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value," International Journal of Research in Marketing, Elsevier, vol. 37(4), pages 789-804.
    16. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    17. Colicev, Anatoli & Kumar, Ashish & O'Connor, Peter, 2019. "Modeling the relationship between firm and user generated content and the stages of the marketing funnel," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 100-116.
    18. Nathan M. Fong, 2017. "How Targeting Affects Customer Search: A Field Experiment," Management Science, INFORMS, vol. 63(7), pages 2353-2364, July.
    19. Gijsenberg, Maarten J., 2014. "Going for gold: Investigating the (non)sense of increased advertising around major sports events," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 2-15.
    20. Paape, Vanessa Isabel, 2022. "The Hidden Influence of Cognitive Processing Style on Consumers' Intention to Adopt Innovative Products," Junior Management Science (JUMS), Junior Management Science e. V., vol. 7(1), pages 112-133.

    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:ijrema:v:34:y:2017:i:2:p:480-498. 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: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    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.