The new frontier of personalization: Why reinforcement learning needs better data

The promise of "right message, right person" has guided marketers for decades. But the static tactics we've relied on—segments, rules, and lookalike audiences—are no longer enough. They treat potential consumers as averages rather than as individuals, leaving revenue on the table. But the frontiers are moving (sorry Terminus). Today, adaptive decisioning systems are leaving these limitations behind, learning in real time to adjust to each customer instead of locking them into a preset journey with the rest of their segment.

This is where reinforcement learning enters the picture: an AI methodology designed to personalize at the individual level by experimenting, adapting, and improving with every interaction. But not all reinforcement learning approaches are created equal—those with access to rich context start with a decisive advantage.

And that’s exactly where Faraday fits into the ecosystem. We provide the context reinforcement learning needs: the verified identity, essential consumer datapoints, and predictive insights that let these systems work faster, smarter, and more efficiently. So how does Faraday make this possible?

How reinforcement learning works in marketing

Well first, let’s break down how reinforcement learning actually works.

At its core, reinforcement learning (RL) is a type of AI that learns by experimenting. Instead of relying on fixed rules or static segments, it tries different actions, measures the outcomes, and adjusts based on what works. Traditional segmentation might decide in advance that “all new homeowners get offer A” or “all loyalty members get email B.” RL, by contrast, doesn’t assume the same choice will work for everyone in a segment.

For instance, one new homeowner might be a bargain shopper who responds best to discount offers, while another might value premium experiences and care more about exclusivity than a perceived discount. Even though both fall into the same segment, they require entirely different outreach. RL recognizes those differences and adapts accordingly.

Two common approaches within RL are known as bandits:

• Multi-armed bandit: starts with no prior knowledge. A marketer might randomly test several subject lines in an email campaign and gradually shift toward the one that produces the highest open rate.

• Contextual bandit: uses additional information—like demographics, past purchases, or engagement history—to make more informed choices from the start. For example, it might send one subject line to long-time loyalty members and another to first-time shoppers, while still experimenting and refining over time.

The key idea: context gives reinforcement learning a head start. Instead of wasting cycles testing irrelevant options, the system begins with signals that already separate one customer from another—like income, household size, or likelihood to convert. That means it doesn’t just converge faster on the right actions, it also avoids costly missteps along the way and delivers more relevant outcomes from the very first interaction.

The challenge: Contextual AI requires rich data

The challenge, however, is that contextual bandits are only as good as the data they’re given. And most brands don’t have enough first-party data to support them effectively.

This problem typically stems from three key issues:

• Limited internal data: Customer data in a CRM or CDP only goes so far. Without external signals, the system can’t distinguish between two lookalike customers.
• Cold start problem: For brand new prospects, there’s no behavioral history at all. RL can’t guess what to promote without context.
• Slow learning: Even with some context, bandits learn by running real-world experiments. That takes time, which delays value.

So we get it, context is king, but where can I get all this juicy data?

Solving the context gap with Faraday

Through our powerful Faraday Identity Graph (FIG), which contains 1500+ consented and verified datapoints on over 240M consumers and their households, Faraday delivers the three layers of data that reinforcement learning systems require to be effective from day one. With these inclusions, instead of learning blindly, our platform gives your systems a decisive head start using:

• Verified identity data: Ensure the people in your dataset are real and reachable.
• Essential consumer datapoints: Curated attributes like homeownership, household size, and income that add meaningful depth.
• Custom predictive datapoints: Scores and models trained on your outcomes—likelihood to convert, churn risk, or best product recommendation—that deliver precision and business impact.

Together, these layers make your data both complete and actionable. FIG provides the breadth, enrichment provides the depth, and predictions provide the precision. That combination drives ROI in everyday campaigns—and it also accelerates emerging approaches like reinforcement learning.

Which brings us to an important point: the relationship between predictive modeling and reinforcement learning. These approaches aren’t competing—they actually strengthen one another.

Predictions and reinforcement learning are better together

We established earlier that contextual bandits are a major leap forward because they use data to make smarter initial guesses, speeding up this process. But we can take this a step further. When you supercharge a contextual bandit with predictive insights—like a customer's likelihood to convert or churn—you create an even more powerful personalization engine.

The primary drawback of reinforcement learning is that it can be extremely slow in real-world applications. It needs to run countless experiments to learn. This is where predictive modeling acts as a powerful accelerator.

Instead of forcing the RL system to test every possible variable for every customer, Faraday’s predictive models first simplify the problem—a concept known as dimensionality reduction. Our models can identify high-level personas or critical attributes (like churn risk or likelihood to engage), allowing the RL system to:

• Focus on a smaller set of key factors for more targeted experimentation.
• Prioritize customers who are already likely to respond, rather than “swimming against the current” by testing offers on uninterested prospects.

In short, predictive models do the heavy lifting of finding the right patterns and people first. The RL system can then take over to fine-tune the details, like which specific message or offer resonates best within those pre-qualified groups. The result is faster time to value, more efficient personalization, and stronger outcomes.

Making your data work for you

Reinforcement learning is a compelling example of how marketing is evolving toward more adaptive, individualized experiences. But whether you’re experimenting with the latest AI techniques or simply trying to run more efficient campaigns, the real question is the same: is your data working for you?

That’s Faraday’s mission. By making identity data reliable, consumer attributes accessible, and predictive insights easy to use, we turn static records into a living foundation for growth. With the right context in place, every system you plug in—reinforcement learning included—can deliver faster, smarter, and more measurable results. That’s what it means to make your data work for you.

Want to learn more about how Faraday’s data can power AI for your company? Let’s talk.