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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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    1. 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.
    2. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    3. 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.
    4. Eric J. Johnson & Wendy W. Moe & Peter S. Fader & Steven Bellman & Gerald L. Lohse, 2004. "On the Depth and Dynamics of Online Search Behavior," Management Science, INFORMS, vol. 50(3), pages 299-308, March.
    5. Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
    6. 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.
    7. 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.
    8. 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.
    9. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    10. Klein, Lisa R., 1998. "Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods," Journal of Business Research, Elsevier, vol. 41(3), pages 195-203, March.
    11. 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.
    12. Patrali Chatterjee & Donna L. Hoffman & Thomas P. Novak, 2003. "Modeling the Clickstream: Implications for Web-Based Advertising Efforts," Marketing Science, INFORMS, vol. 22(4), pages 520-541, May.
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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. 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.
    3. 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.
    4. 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.
    5. Min Tian & Paul R. Hoban & Neeraj Arora, 2024. "What Cookie-Based Advertising Effectiveness Fails to Measure," Marketing Science, INFORMS, vol. 43(2), pages 407-418, March.
    6. 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.
    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. 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).
    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.
    13. 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.

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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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