AI for Marketing Analytics: The Good, the Bad, and the Ugly

If you’re a human reading this, chances are you tried AI for data analysis . . . and have likely been sorely disappointed with the results.

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While the headlines and LinkedIn shock-and-awe posts are promising a self-driving marketing analytics future, many of us are left scratching our heads. Can AI  fundamentally change the way Revenue and Marketing Operations professionals approach analytics given hallucinations and confident  but erroneous optimism? While many are still wary of AI's role in their work, the potential to drive efficiency and uncover deeper insights is undeniable.

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The question du jour: how do you move from cautious curiosity to confident, impactful implementation? 🤔

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Join this discussion with Nadia Davis - VP of Marketing at CaliberMind, Joe Schattschneider - Marketing Data Strategy leader at CaliberMind, and James Tibert, a Salesforce Solutions Manager at PayIt, where they’ll separate hype from reality, do some sober myth busting  and  take a clear-eyed look at leveraging GenAI in your day-to-day marketing analytics - exploring the good, the bad, and the ugly of this transformative technology, including:

• How the "intern test" can help you evaluate AI outputs
• Some tried and true examples of successful AI use in MOps and RevOps
• The difference between system instructions, saved prompts, and agents, and when to use each to build a scalable AI workflow
• And of course, the critical human element, because AI for analytics doesn't remove the need for human curiosity and interpretation

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You’ll leave this session with a strategic framework for embedding AI into your analytics function, a clear understanding of its real-world ROI, and the confidence to consult and guide your stakeholders with  your organization into the next era of data-driven marketing.

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