Reinforcement Learning (RL)

Reinforcement Learning solves a fundamental marketing problem: how do you get AI systems to optimize for outcomes you care about without hand-coding every rule? Traditional AI requires humans to label thousands of examples. RL takes a different approach—it learns by interacting with an environment and getting feedback.

Imagine you're training a recommendation engine. Instead of telling it "show Product A to Customer B," you let it make recommendations and measure what happens. If a customer clicks and buys, that's a reward signal. If they skip it, that's a penalty. Over thousands of interactions, the system learns which recommendations drive conversions, engagement, or retention—whatever metric you define as "winning."

In marketing tools, RL shows up in email send-time optimization, bid management in paid search, and dynamic pricing. For example, a marketing automation platform might use RL to learn the best time to send emails to each individual customer. It tries different times, observes open rates and click-through rates, and gradually shifts toward the times that work best for that person. No human has to write rules like "send to John at 9 AM on Tuesdays."

The key difference from other AI approaches: RL systems improve their own decision-making based on real-world outcomes. They don't need a human to say "this was right" or "this was wrong"—they infer it from what actually happened in your business. This makes RL particularly powerful for optimization problems where the right answer changes over time or varies by individual.

When evaluating AI tools, ask whether they use RL for optimization. If they do, they should show you evidence that recommendations improve over time. If they don't, you're likely paying for static rules that won't adapt to your audience or market changes.