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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On September 10, DATA POEM introduced interconnected, non-linear optimization using Large Causal Models, which enable real-time, granular decision-making across all marketing levers and market scenarios. This session was designed for leaders who suspect their current optimization framework is leaving significant value on the table—and want to understand what the architectural alternative looks like in practice.
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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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