Best For
Multiple valid offer variants, enough traffic to learn from results, and a clear goal such as conversion, AOV, or margin protection
Start With
Get your base offers, analytics, and promotion goals in place before you turn optimization on
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
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:- Create multiple offer variants (e.g., different discount amounts or types)
- Navigate to “Offer Optimizations” in the main menu
- Create a new optimization group containing your offer variants
- Activate the optimization group
- The system will automatically show the most appropriate offer to each shopper based on their profile and behavior
How to Choose Good Variants
Strong optimization groups usually have:- a shared goal such as increasing conversion rate or order value
- meaningfully different offers, not tiny variations
- a clear merchant reason to test one strategy against another
- 10% off order vs. free shipping
- gift with purchase vs. order discount
- different spend thresholds with different rewards
- two nearly identical discount amounts
- variants that conflict because of priority or qualifier reuse problems
Monitoring Performance
Track how your personalized offers are performing:- Go to the “Offer Optimizations” dashboard
- View real-time performance metrics for your optimization groups
- See how different offers perform across various customer segments
- Monitor overall lift compared to standard, non-personalized offers
Build Better Inputs
Pick offer types that are genuinely different enough to teach the optimizer something useful
Measure Outcomes
Review conversion, lift, and campaign performance after launch
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