Technical
How Bayesian Cart Reminder Optimization Works for Shopify
A merchant-friendly explanation of Bayesian testing and Thompson sampling for Shopify cart reminder messages and timings.
Most cart reminder testing is manual. A merchant writes two messages, splits traffic, waits, and eventually picks a winner. That works, but it wastes traffic on losing variants longer than necessary.
Bayesian optimization is a cleaner fit for cart reminders because the system can keep learning while gradually favoring better-performing message and timing combinations.
The simple version
Each message and timing combination is treated like an arm in an experiment. When a reminder is shown and clicked, the system updates its belief about that arm.
Instead of permanently choosing a winner too early, it samples from the current belief distribution. Better arms tend to be shown more often, but other arms still get enough traffic to keep learning.
Why Thompson sampling fits cart recovery
Cart reminders have many contextual variables: product type, price, customer language, timing, discount, and copy. A rigid A/B test can be slow when there are several combinations.
Thompson sampling balances exploration and exploitation. It tries promising options more often while still testing alternatives.
- Explore new reminder variants.
- Exploit variants that appear to work.
- Avoid locking in too early.
- Learn from clicks over time.
- Support timing and message combinations.
What merchants need to know
The merchant does not need to understand the math to benefit. They need clear controls: message variants, timing options, discount rules, and reporting.
The system should make testing easier, not turn the app into an analytics project.
FAQ
Is Bayesian testing better than A/B testing?
It can be better for continuously optimizing multiple variants, but the right method depends on traffic volume and goals.
What does Thompson sampling optimize for?
In a cart reminder flow, it can favor message and timing combinations that produce more desired events, such as clicks.
Freddy uses experimentation so cart reminder copy and timing can improve without constant manual tuning.
See Freddy