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Why Facebook Ads keep failing: Lessons learned from spending over US$1m on Facebook Ads

Author

Listed:
  • Huntinghouse, John

    (TAB Bank, USA)

  • Franks, Emma

    (TAB Bank, USA)

  • Fife, Ben

    (Fluid Advertising, USA)

Abstract

Facebook’s utilisation of machine learning and artificial intelligence (AI) can often identify profitable targets more quickly and more effectively than can human campaign managers. However, there is still much that marketers may do to ensure optimal placement and delivery when it comes to their Facebook Ad campaigns and leveraging the power of Facebook’s machine learning and AI capabilities. This article discusses the some of the issues that marketers face in trying to fully utilise Facebook’s machine-learning capabilities and how many marketers struggle to create campaign structures that allow them to optimise and scale their digital campaigns on Facebook. This article will detail a few of these frameworks, strategies and tactics. The article does not advocate the ‘right’ framework to use but rather argues simply that having a framework will ensure better results and support learning throughout the campaign.

Suggested Citation

  • Huntinghouse, John & Franks, Emma & Fife, Ben, 2021. "Why Facebook Ads keep failing: Lessons learned from spending over US$1m on Facebook Ads," Journal of Digital & Social Media Marketing, Henry Stewart Publications, vol. 8(4), pages 298-307, April.
    • Handle: RePEc:aza:jdsmm0:y:2021:v:8:i:4:p:298-307
    as

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    More about this item

    Keywords

    Facebook; digital marketing; digital advertising; machine learning; social media; video marketing;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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