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To Zap or Not to Zap: A Study of the Determinants of Channel Switching During Commercials

Author

Listed:
  • S. Siddarth

    (Anderson School at UCLA, 110 Westwood Plaza, Los Angeles, California 90095-1481)

  • Amitava Chattopadhyay

    (Faculty of Commerce, University of British Columbia, 2053 Main Mall, Vancouver, British Columbia, Canada V6T 1Z2)

Abstract

We present a conceptual framework to describe the commercial zapping phenomenon and use it to identify factors that influence channel switching during commercials. Drawing on previous research, published reports of practitioner gut feel, interventions used by advertisers to reduce channel switching, and proprietary studies reported in the published literature, we describe how these variables might potentially affect the decision to zap a commercial. We use a latent class approach to model the impact of the identified factors on two aspects of the switching decision—whether or not a commercial is zapped (modeled with a binary logit model) and, conditional on a zap having taken place, the number of seconds that the commercial was watched before being zapped (modeled within the proportional hazards framework). The model is estimated on telemeter data on commercial viewing in two categories (spaghetti sauce and window cleaners) obtained from a single-source, household scanner panel. The results from the empirical analysis show that households can be grouped into two segments. The first, which consists of about 35% of households in the sample, is more zap-prone than the second. For this “zapping segment,” the probability of zapping a commercial is lower for households who make more purchases in the product category. Also, zapping shows a J-shaped response to previous exposures to the commercial, with the associated zapping elasticity reaching its minimum value at around 14 exposures and increasing rapidly thereafter. This finding suggests that advertisers should be cautious not to use media schedules that have excessive media weight or that emphasize frequency over reach. We found that zapping probabilities for ads aired around the hour and half-hour marks to be significantly higher than for other pod locations. Based on these results, we argue that prices for advertising pods located around the hour/half-hour marks should be between 5% to 33% lower than those in the remaining portion of the program. We explore the impact of advertising content on zapping and find that the presence of a brand differentiating message in a commercial causes a statistically significant decrease in zapping probabilities. While the magnitude of this effect is small, the finding suggests that it may be helpful to include qualitative variables in future models of advertising response. We propose the expected proportion of time that an ad is watched as a benchmark to compare 15-second and 30-second ad formats from a zapping standpoint. We found no significant differences between the two formats on this dimension. Our analysis also shows that, due to the impact of previous exposures on zapping, the use of 15-second ads runs a greater risk of reaching the threshold exposure level beyond which zapping probabilities start to increase. This implies that while managers must be cognizant of the risks of overexposure for any ad, it is especially important in the case of the shorter, 15-second ad format.

Suggested Citation

  • S. Siddarth & Amitava Chattopadhyay, 1998. "To Zap or Not to Zap: A Study of the Determinants of Channel Switching During Commercials," Marketing Science, INFORMS, vol. 17(2), pages 124-138.
  • Handle: RePEc:inm:ormksc:v:17:y:1998:i:2:p:124-138
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    File URL: http://dx.doi.org/10.1287/mksc.17.2.124
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    References listed on IDEAS

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    Cited by:

    1. Rojas-Méndez, José I. & Davies, Gary & Madran, Canan, 2009. "Universal differences in advertising avoidance behavior: A cross-cultural study," Journal of Business Research, Elsevier, vol. 62(10), pages 947-954, October.
    2. Thales S. Teixeira & Michel Wedel & Rik Pieters, 2010. "Moment-to-Moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing," Marketing Science, INFORMS, vol. 29(5), pages 783-804, 09-10.
    3. Lucia Reisch & Clive L Spash & Sabine Bietz, 2008. "Sustainable Consumption and Mass Communication: A German Experiment," Socio-Economics and the Environment in Discussion (SEED) Working Paper Series 2008-12, CSIRO Sustainable Ecosystems.
    4. Soni, Mayank Jyotsna, 2013. "Advertisement Placement in TV Programs: Different Roles of ELM and Mood Protection Mechanism," IIMA Working Papers WP2013-03-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Pruyn, Ad & Riezebos, Rik, 2001. "Effects of the awareness of social dilemmas on advertising budget-setting: A scenario study," Journal of Economic Psychology, Elsevier, vol. 22(1), pages 43-60, February.
    6. Kenneth C. Wilbur, 2008. "A Two-Sided, Empirical Model of Television Advertising and Viewing Markets," Marketing Science, INFORMS, vol. 27(3), pages 356-378, 05-06.
    7. Hans Jarle Kind & Tore Nilssen & Lars Sørgard, 2009. "Business Models for Media Firms: Does Competition Matter for How They Raise Revenue?," Marketing Science, INFORMS, vol. 28(6), pages 1112-1128, 11-12.
    8. Esther Gal-Or & Mordechai Gal-Or & Jerrold H. May & William E. Spangler, 2006. "Targeted Advertising Strategies on Television," Management Science, INFORMS, vol. 52(5), pages 713-725, May.
    9. Anthony Dukes & Esther Gal–Or, 2003. "Negotiations and Exclusivity Contracts for Advertising," Marketing Science, INFORMS, vol. 22(2), pages 222-245, November.

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