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Scheier, C., & Held, D. (2024). Double Impact: Harnessing Generative and Evaluative AIs for Effective Marketing Decisions. NIM Marketing Intelligence Review, 16(1) 36-41. https://doi.org/10.2478/nimmir-2024-0006

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NIM Marketing Intelligence Review – Generative AI - Reshaping the Marketing Landscape

Double Impact: Harnessing Generative and Evaluative AIs for Effective Marketing Decisions

Generative AI Evaluative AI Marketing Decision Making Effective Content

Authors

  • Dr. Christian Scheier, Founder and Managing Director, Aimpower GmbH
  • Dr. Dirk Held, Founder and Managing Director, Aimpower GmbH
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Abstract:
While generative AI is already on everyone’s lips, evaluative AI and its potential are currently less widely discussed. The evaluative AI market is characterized by a growing number of vendors, many specializing in distinct aspects like predictive eye tracking, image persuasion analysis, image memorability prediction, text content analysis or social media click-through rate predictions. However, the landscape is not limited to these niche offerings. Holistic solutions are also emerging, addressing all relevant marketing touchpoints. They offer capabilities ranging from execution aspects, such as visual attention and simplicity assessments, to strategic evaluations that ensure alignment with the intended brand message. Looking ahead, the trend is moving towards fully integrated platforms that encompass the entire AI-based marketing workflow. These platforms promise to cover everything from audience insights and strategy development to creative execution and performance monitoring. The future of marketing lies in the seamless integration of generative and evaluative AI, guided by human expertise.

Authors

  • Dr. Christian Scheier, Founder and Managing Director, Aimpower GmbH
  • Dr. Dirk Held, Founder and Managing Director, Aimpower GmbH
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Suggested Citation

Scheier, C., & Held, D. (2024). Double Impact: Harnessing Generative and Evaluative AIs for Effective Marketing Decisions. NIM Marketing Intelligence Review, 16(1) 36-41. https://doi.org/10.2478/nimmir-2024-0006



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