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Does banner advertising affect browsing for brands? clickstream choice model says yes, for some

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  • Oliver Rutz
  • Randolph Bucklin

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  • Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 231-257, June.
    • Handle: RePEc:kap:qmktec:v:10:y:2012:i:2:p:231-257
    DOI: 10.1007/s11129-011-9114-3
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    References listed on IDEAS

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    6. Danaher, Peter J. & Mullarkey, Guy W., 2003. "Factors Affecting Online Advertising Recall: A Study of Students," Journal of Advertising Research, Cambridge University Press, vol. 43(3), pages 252-267, September.
    7. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    8. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    9. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    10. Havlena, William J. & Graham, Jeffrey, 2004. "Decay Effects in Online Advertising: Quantifying the Impact of Time Since Last Exposure on Branding Effectiveness," Journal of Advertising Research, Cambridge University Press, vol. 44(4), pages 327-332, December.
    11. Nobuhiko Terui & Masataka Ban & Greg M. Allenby, 2011. "The Effect of Media Advertising on Brand Consideration and Choice," Marketing Science, INFORMS, vol. 30(1), pages 74-91, 01-02.
    12. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
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    Citations

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    Cited by:

    1. Peitz, Martin & Reisinger, Markus, 2014. "The Economics of Internet Media," Working Papers 14-23, University of Mannheim, Department of Economics.
    2. Shun-Yang Lee & Julian Runge & Daniel Yoo & Yakov Bart & Anett Gyurak & J. W. Schneider, 2023. "COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?," Papers 2307.09035, arXiv.org, revised Jan 2024.
    3. Goic, Marcel & Álvarez, Rodolfo & Montoya, Ricardo, 2018. "The Effect of House Ads on Multichannel Sales," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 32-45.
    4. El Hana, Nadr & Mercanti-Guérin, Maria & Sabri, Ouidade, 2023. "Cookiepocalypse: What are the most effective strategies for advertisers to reshape the future of display advertising?," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Christina Uhl & Nadia Abou Nabout & Klaus Miller, 2020. "How Much Ad Viewability is Enough? The Effect of Display Ad Viewability on Advertising Effectiveness," Papers 2008.12132, arXiv.org.
    6. Shiri Melumad & Rhonda Hadi & Christian Hildebrand & Adrian F. Ward, 2020. "Technology-Augmented Choice: How Digital Innovations Are Transforming Consumer Decision Processes," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 7(3), pages 90-101, October.
    7. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    8. Nicolás Aramayo & Mario Schiappacasse & Marcel Goic, 2023. "A Multiarmed Bandit Approach for House Ads Recommendations," Marketing Science, INFORMS, vol. 42(2), pages 271-292, March.
    9. Kumar, Ashish, 2021. "An empirical examination of the effects of design elements of email newsletters on consumers’ email responses and their purchase," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    10. 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.
    11. 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.
    12. Shiri Melumad & Rhonda Hadi & Christian Hildebrand & Adrian F. Ward, 2021. "Technology-Augmented Choice: How Digital Innovations Are Transforming Consumer Decision Processes," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 7(3), pages 90-101, October.

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    More about this item

    Keywords

    Internet; Banner advertising; Clickstream; Logit choice models; Heterogeneity; C01; C11; C33; M31; M37;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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