Contextual bandits

Contextual Bandits for Personalization

Atom Commerce leverages contextual bandits—a type of reinforcement learning—to optimize promotions by personalizing discount offers for individual customers. This approach moves beyond traditional A/B testing to deliver true 1:1 personalization.

Understanding Contextual Bandits

Contextual bandits extend the classic multi-armed bandit framework by incorporating customer-specific features when making decisions. They consist of two main components:

Promotion Performance Predictor

Our Promotion Performance Predictor forecasts how well each discount or promotion option is likely to work by looking at key customer data:

• Purchase history
• Average spend
• Engagement level (e.g. email opens, clicks)
• Browsing behavior
• Current cart contents
• Other behavioral signals

By using these insights, Atom Commerce can estimate which discount will drive the best results for each shopper—so you can deliver smarter, more effective promotions without any guesswork.

Exploration Strategy

Alongside exploitation (choosing the current best-known discount), the bandit occasionally explores other options to discover potentially better strategies. This exploration is balanced against exploitation to continually refine the system’s choices.

How Atom Commerce Applies Contextual Bandits

Personalized Discount Optimization

Atom Commerce uses contextual bandits to evaluate a range of discount options and select the one most likely to be effective for each individual customer. By leveraging rich customer data, the system can predict how an individual will respond to a specific discount offer, leading to a tailored promotion strategy instead of a uniform discount applied across the board.

Dynamic Learning from Customer Behavior

Every interaction with a discount offer—whether accepted or not—provides immediate feedback. The model uses this data to update its predictions continuously, ensuring that the promotion strategy adapts in real time. As a result, the system refines its choices and consistently prioritizes the best-performing offers.

Efficient Handling of a Large Decision Space

Marketing promotions can vary along multiple dimensions, such as discount amount, type, timing, and delivery channel. Traditional A/B testing struggles as the number of combinations grows exponentially. In contrast, contextual bandits efficiently navigate this complex landscape by making one-to-one decisions for each customer rather than relying on aggregated group-level comparisons.

Advantages Over Traditional A/B Testing

Accelerated Learning

Traditional A/B testing splits traffic evenly among a few static variants and requires a long period to gather enough data for statistically significant results. In contrast, contextual bandits learn from every individual interaction in real time, requiring less data and time to converge on an optimal strategy.

Individual-Level Customization

A/B testing generates aggregate data that may overlook important behavioral differences among customer segments. Contextual bandits make decisions at the individual level, ensuring that each customer receives the discount most likely to maximize their conversion or engagement.

Scalability for Complex Marketing Campaigns

When multiple promotion dimensions are involved—such as discount type, messaging, timing, and channel—the total number of possible combinations becomes unmanageable with A/B testing. Contextual bandits efficiently manage this multi-dimensional decision space, scaling gracefully as complexity increases.

Resource Efficiency

Faster convergence toward effective strategies reduces the risk and cost associated with prolonged experimentation. Marketers achieve better outcomes sooner, translating into improved conversion rates and higher overall return on investment.

Using Offer Optimizations in Atom Commerce

To leverage contextual bandits for your promotions:

1. Create multiple offer variants (e.g., different discount amounts or types)
2. Navigate to “Offer Optimizations” in the main menu
3. Create a new optimization group containing your offer variants
4. Activate the optimization group
5. The system will automatically show the most appropriate offer to each shopper based on their profile and behavior

Monitoring Performance

Track how your personalized offers are performing:

1. Go to the “Offer Optimizations” dashboard
2. View real-time performance metrics for your optimization groups
3. See how different offers perform across various customer segments
4. Monitor overall lift compared to standard, non-personalized offers

Best Practices

• Include diverse offer variants in your optimization groups
• Allow enough time for the system to gather sufficient data
• Use meaningful offer differences (e.g., 10% vs. 20% off rather than 19% vs. 20%)
• Review performance regularly but avoid making premature conclusions
• Consider seasonal factors that might influence customer behavior