As AI assistants become fully embedded within e-commerce platforms, the question becomes: how are consumers actually using them? This MSI working paper analyzes the behavior of over 31 million users on a major travel platform to uncover who adopts shopping AI, when it is used in the purchase journey, and what consumers rely on it for. The findings reveal that AI assistants complement—not replace—traditional search, helping consumers navigate complex, exploratory decisions and reshaping how discovery happens online.
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This MSI working paper introduces TextBO, a novel AI framework designed to improve marketing decisions more efficiently by minimizing costly evaluation cycles. By combining large language models with Bayesian optimization principles, the approach enables AI systems to iteratively refine outputs—such as ad creatives—while requiring fewer real-world tests. The result: faster learning, better-performing outcomes, and a more scalable path to AI-driven decision-making.
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Turns out there isn’t just one type of influencer. Some are content creators, while others focus more on self-presentation. Which type best represents your brand? This MSI working paper introduces a new behavioral framework that can help you decide. It’s for classifying influencers based on communication style and shows how content focus and self-focus shape engagement, reach and marketing effectiveness.
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Every two years, the Marketing Science Institute (MSI) asks every member company Trustee to provide input to help set priorities for the research that will guide our activities for the next few years.
These priorities enable MSI to engage in its most critical mission: moving the needle on important marketing problems.
One of the priorities focuses on the need to develop new approaches that enable firms to gain insights from multiple approaches, to synthesize, to bring together disparate methods to drive action. There is also a sense that the old methods aren’t working as well, and that some of the traditional indicators and metrics are less effective.
Here are some topics that illustrate where significant research is needed in this area:
- How to bring multiple sources and types of information together to gain insight and to make better decisions (e.g., big data meets unstructured data; data scientist meets anthropologist). Can such synthesis be automated?
- Integrating behavioral theory and marketing frameworks into big data marketing
- How can firms speed up the process by which they collect data, synthesize, identify insights, take action, get feedback, and do any necessary course correction? Is this process different for B2B and B2C?
/research/2016-2018-research-priorities/new-data-new-methods-and-new-skills-how-to-bring-it-all-together
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