McKinsey on the Future of Personalization

Personalization isn’t new, but it’s having a moment with the rise of AI. What makes this AI‑fueled moment different?

Eli: I think of personalization over the past ten years or so as Mad Libs. And that's been tremendously effective in some cases. Think about recommended movies, products, etcetera, where the more specific you can get something, the better. And that's really where machine learning and discriminative AI have been very helpful. 

At the same time, it's almost depersonalized in a way. If you think about all the things that we try to do as marketers—be inspiring, be educational, make someone's life more convenient—the “Mad Libs" approach has taken away from that. AI enables you to create content at a scale and still be inspiring, relevant, and educational vs. just filling in the blanks. 

That's what I think the next horizon of personalization will be, using AI to fulfill the promise of all we wanted ten years ago when we started to really personalize.

Scaling personalization is not just creating more content, but making it truly resonant. How do you ensure personalized content truly connects?

Eli: I think where some folks have gone wrong with AI is being thrilled by the capabilities to produce at scale. But in some ways, it’s the tail wagging the dog in personalization. Meaningful personalization requires understanding your audience and microsegments. You have to understand the data triggers to find the right moments to respond to them. And then, of course, you have to be able to produce at scale. That’s the part unlocked by AI.

Without understanding and prioritizing audiences and evaluating the contextual data that can help unlock the moments where they're influenceable, then you're just not able to reach full ROI potential with your content.

Jasper’s State of AI in Marketing research has found the majority of marketers are still using AI in very ad hoc ways. How do organizations move past that to full AI-driven workflows?

Eli: Deploying AI longitudinally throughout the journey is really the unlock for companies that have delivered value at scale. For folks deploying AI at point use cases, we've seen single digit cost savings or some growth from personalization, but nothing more. When we see companies that are really successful, they're doing four things: rethinking the workflow, rethinking roles, making AI interoperable, and building capabilities.

The workflow changes with AI. If you just put AI in your existing workflow, you’ll go no faster or only marginally faster than if you didn’t have AI in the first place. The same steps will slow down the gears. It’s a paradigm shift, going from thinking of AI as a cool new tool to thinking of it as catalyzing a new era of marketing.

What are your best practices to build AI workflows from the ground up?

Eli: First is to have a strong understanding of your current workflow and how it happens today. Then, you reimagine what your workflow could be, and that should be a derivative of the type of marketing you want to do.

For instance, if you're sending millions of weekly emails with highly personalized, ML-based recommendations, you’ll have a much different workflow than a company focused on mid-funnel social media activations. You have to know what your specific goals are. Then, you reimagine your workflow from the bottom up.

Consumer expectations and economic pressures are always evolving, and drive urgency within organizations to think of personalization as more than a nice-to-have. What do you think about personalization as a differentiator?

Eli: I think for a long time, personalization has kind of been the sprinkles on the ice cream. The ketchup on the burger. Whatever your analogy is. The point is that personalization has really not been a tool to increase relevance and authenticity largely because the costs of producing content at that scale were prohibitive.

Now I think we are in an era where personalization can increase the relevance and authenticity of how you connect with consumers if you start with an audience and data lens.

Our research shows that 75% of customers are turned off by content that doesn't seem relevant to them, and the bar for that relevance will keep increasing. And when I talk to marketing leaders, particularly CMOs, and hear about the value drivers that they’re anchoring on—brand, revenue, growth, efficiency—I think personalization can help with all of them.

This is just a preview of the conversation with Eli Stein on AI Meets Personalization. Watch it now to unlock the full playbook.

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