6 min read
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
A practical guide to choosing between the three core bandit algorithms. Compare Epsilon-Greedy, Thompson Sampling, and UCB1 on convergence speed, regret, tuning requirements, and real-world suitability.
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.