Generative search may help consumers move from broadly stated needs to more focused shopping decisions. In a field experiment involving more than 6.4 million users of Meituan—a Chinese ecommerce platform, consumers given AI-generated responses placed more orders and generated greater transaction value than those using conventional keyword search. They also spent less time browsing and concentrated their attention on fewer, more relevant categories and merchants.
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AI is transforming how organizations generate insights and forecast outcomes, but questions remain about reliability and the need for human oversight. On September 14-15 in NYC, industry and academic leaders gathered to discuss integrating AI into analytics workflows and maintaining transparency.
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As privacy changes make it harder for marketers to track individual customer journeys, media and marketing mix modeling (m/MMM) is experiencing renewed interest. Open-source tools from Meta, Google and the PyMC ecosystem promise to make these methods more accessible, particularly for organizations without the resources for customized proprietary models. In this MSI working paper, Julian Runge and Koen Pauwels evaluate the leading open-source approaches, examining their capabilities, differences and tradeoffs. Their analysis shows that while automation can democratize sophisticated marketing measurement, using these tools responsibly requires careful attention to model assumptions, diagnostics, calibration and validation.
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