A practical introduction to multi-armed bandit algorithms for website optimization. Learn how bandits balance exploration and exploitation to maximize conversions without wasting traffic.
March 27, 2026
Bandit's variant chat drafts new versions for an experiment from the page it runs on and how each existing version is doing. Here is what the model sees, what it won't do, and what each draft costs.
A test that never ends has to forget, or last spring's winner keeps winning forever. Here is how Bandit's discounted and sliding-window memory work, what the defaults are, and which experiments they apply to.
A bundle tests every piece of copy on a page at once. One request returns a version for each slot, one payout teaches every slot the visitor saw, and a second model can learn which pieces work together once there is enough traffic.
Bandit picks the subject line and body copy for each recipient, counts the human click on your site, treats an unsubscribe as a cost, and carries what each framing earned into your next campaign. You keep your email service.
Bandit's Audiences page finds the visitor groups that respond to your versions differently from everyone else, and names each one with a rule like device: mobile and source: google. Here is how it finds them, what the numbers mean, and where it stops.
Bandit's Shopify app tests the headline, subhead, call to action, banner, and badge in your theme. A test set to margin scores each order by what it earned after unit cost and refunds, not by how many orders it brought in.
A thought experiment: apply A/B testing, multi-armed bandits, and funnel algorithms to retail floorspace and product placement. See why a chain with hundreds of locations is the ideal environment for algorithmic optimization — and how bandits adapt to seasonality without a single manual re-test.
A/B testing, multi-armed bandits, contextual bandits, and custom trained models each need an order of magnitude more data than the last. Here's how to tell which rung your traffic actually supports.
We compared Epsilon-Greedy, UCB1 and Thompson Sampling on convergence speed, regret, tuning and real-world suitability, then shipped only one of them. Here is the reasoning, and what you configure instead.
The explore-exploit dilemma is the fundamental challenge in optimization. Learn how bandit algorithms navigate this trade-off through real-world analogies, interactive visualizations, and practical examples.
A step-by-step tutorial for launching your first multi-armed bandit experiment. From creating an experiment to integrating the SDK and tracking conversions.
Most A/B tests end inconclusively. Learn the five most common reasons, from insufficient traffic to the peeking problem, and how adaptive algorithms can help.
Watch a live simulation comparing traditional A/B testing against bandit algorithms. See how adaptive traffic allocation reduces wasted conversions and cumulative regret.
A step-by-step tutorial to integrate the Bandit SDK into your application. Install, initialize, get assignments, and track conversions, with copy-paste code examples.
A visual deep-dive into Thompson Sampling, the most effective bandit algorithm for website optimization. Understand Beta distributions, posterior updates, and why Bayesian exploration naturally balances the explore-exploit tradeoff.
Traditional A/B testing requires thousands of visitors to reach significance. Learn why multi-armed bandit algorithms are better suited for low-traffic sites and how to calculate when each approach makes sense.
Learn how to run linked experiments across your entire conversion funnel. Optimize landing pages, signups, onboarding, and purchases as a coordinated system instead of isolated tests.