Analytics & Data Science

From Measurement to Decision Intelligence: The Future of Marketing Analytics

  • ARF Invited Paper by Jim Spaeth and Alice K. Sylvester

Marketing effectiveness analytics is undergoing a profound transformation. As marketers face growing complexity across channels, data sources and consumer journeys, artificial intelligence is accelerating the shift from retrospective measurement toward dynamic, decision-oriented systems. This paper examines a decade of innovation in marketing analytics, highlighting the rise of integrated measurement frameworks, experimentation, machine learning and emerging AI-powered modeling approaches that promise to reshape how organizations understand and optimize marketing performance.

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Learn How to Train LLMs to Identify Implicit Consumer Needs

This study explores the potential of large language models (LLMs) to revolutionize marketing research. By partnering with a Fortune 500 food company, the authors replicated qualitative and quantitative studies using GPT-4. The findings indicate that LLMs can effectively generate synthetic respondents, moderate in-depth interviews and perform data analysis tasks, matching or even surpassing human performance in certain aspects. The study highlights the benefits of a Human-LLM hybrid approach, where LLMs assist in various stages of the research process, from study design to data analysis. This approach not only enhances efficiency but also uncovers new insights that might be overlooked by human researchers alone.

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Lagging Indicators Won’t Make It!

  • John Matthews
  • Marketing Evolution

The authors describe a very broad approach to creating omnichannel marketing effectiveness. It calls for real time indicators of campaign success. They create a personal level database through integration of online and offline data and then aggregate persons into clusters with a homogeneity of location and behaviors. That allows them to buy more traditional media but to highly targeted clusters of people.

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