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It’s time to close the experimentation gap in advertising: Confronting myths surrounding ad testing

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  • Campbell, Colin
  • Runge, Julian
  • Bates, Kenneth
  • Haefele, Stacey
  • Jayaraman, Neeraj

Abstract

Marketers know that running experiments is a proven way to improve results and gain competitive advantage against rivals. Despite this knowledge—and the fact that experiments are now easier to conduct than ever before—data shows that marketers consistently under-experiment. In this article, we examine why this gap exists and what can be done to close it. We do so by connecting with senior-level marketing professionals representing seven consumer-facing industries in two phases. First, through a series of interviews, we gain initial understanding of the concerns, challenges, and realities of those working in the industry. Following this phase, we surveyed a larger group to corroborate and extend our initial findings, comparing cases to identify challenges and the strategies used to overcome them. We present our findings as a series of experimentation myths before closing with a broader perspective on how organizations can infuse experimentation into their culture.

Suggested Citation

  • Campbell, Colin & Runge, Julian & Bates, Kenneth & Haefele, Stacey & Jayaraman, Neeraj, 2022. "It’s time to close the experimentation gap in advertising: Confronting myths surrounding ad testing," Business Horizons, Elsevier, vol. 65(4), pages 437-446.
    • Handle: RePEc:eee:bushor:v:65:y:2022:i:4:p:437-446
    DOI: 10.1016/j.bushor.2021.05.004
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    References listed on IDEAS

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    1. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    2. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    3. Thomas Blake & Chris Nosko & Steven Tadelis, 2015. "Consumer Heterogeneity and Paid Search Effectiveness: A Large‐Scale Field Experiment," Econometrica, Econometric Society, vol. 83, pages 155-174, January.
    4. Joseph O. Eastlack, Jr. & Ambar G. Rao, 1989. "Advertising Experiments at the Campbell Soup Company," Marketing Science, INFORMS, vol. 8(1), pages 57-71.
    5. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    6. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    7. Thomke, Stefan & von Hippel, Eric & Franke, Roland, 1998. "Modes of experimentation: an innovation process--and competitive--variable," Research Policy, Elsevier, vol. 27(3), pages 315-332, July.
    8. Marc Fischer & Sönke Albers & Nils Wagner & Monika Frie, 2011. "Practice Prize Winner --Dynamic Marketing Budget Allocation Across Countries, Products, and Marketing Activities," Marketing Science, INFORMS, vol. 30(4), pages 568-585, July.
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    Cited by:

    1. Weijia Dai & Hyunjin Kim & Michael Luca, 2023. "Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Marketing Science, INFORMS, vol. 42(3), pages 429-439, May.

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