Smart A/B Testing: Multi-Armed Bandit Turnkey

Classic A/B testing requires waiting for statistical significance — often weeks with moderate traffic, losing conversions on suboptimal variants. Multi-Armed Bandit adapts in real time: automatically reallocates traffic to the best variant while data accumulates. For an e-commerce site with 50,000 daily visitors, each day on a suboptimal variant costs hundreds of lost orders. We implement MAB turnkey — from algorithm selection to integration with your experimentation platform.

When MAB Outperforms Classic A/B?

High-frequency decisions (email subject lines, push notifications, UI elements). When the cost of error is high — e.g., losing 10% conversion for a week means tens of thousands in lost revenue. When there are more than two variants — classic tests require exponential sample growth, while MAB scales easily to dozens of variants.

How MAB Algorithms Work

Thompson Sampling — Bayesian approach: for each variant, maintain a beta distribution of conversion probability. On each request: sample from distributions → show the variant with the highest sample → update distribution based on the outcome. The exploration/exploitation balance is mathematically built-in. Epsilon-Greedy is simpler: with probability ε — random variant (exploration), with probability 1-ε — current best (exploitation). ε decays over time (ε-decay). Contextual Bandit extends MAB by considering user context: device, traffic source, on-site behavior. We use LinUCB, NeuralLinear — each user sees the optimal variant for their profile.

Comparison: MAB vs Classic A/B

How We Implement MAB

We use Python (Vowpal Wabbit — instant performance, up to 1M requests per second on a single core) or custom PyTorch code. Redis for storing statistics with session timeouts. Feature flags platform (Unleash, LaunchDarkly) for variant management and rollback. Monitoring: cumulative regret (losses from suboptimal choice), conversion by variant over time, traffic distribution. One project: for an e-com site with 500k visits/day, we reduced regret by 37% in two weeks by switching traffic from an ineffective banner that was shown 60% of the time. The Thompson Sampling algorithm ensured an optimal balance.

How to Implement MAB: Step-by-Step

1. Audit current experimentation infrastructure: analyze traffic, goals, existing A/B tests.
2. Select MAB algorithm (Thompson Sampling, Epsilon-Greedy, Contextual Bandit) based on your data.
3. Integrate via API, SDK, or feature flags: we support Python, Node.js, Go.
4. Set up monitoring: dashboards with cumulative regret, conversion by variant, exploration rate.
5. Launch and optimize: parameter tuning, A/B validation.

Timeline: 3–5 weeks depending on integration complexity.

What’s Included in the Work?

• Audit of current experimentation infrastructure: traffic analysis, goals, existing A/B tests.
• Selection of MAB algorithm for your task and stack.
• Integration via API or SDK: Python, Node.js, Go — for any backend.
• Monitoring and dashboards: metrics, regret, conversion, exploration rate.
• Documentation and team training: how to interpret results, how to add new variants.
• Support for the first month: parameter tuning, help with interpretation.

Why Choose Experienced Engineers?

MAB setup mistakes are costly: wrong ε leads to over-exploitation of a bad variant, ignoring context leads to incorrect conclusions. Our engineers are certified ML specialists with 10+ years in production ML. We’ve implemented MAB for 20+ projects, including fintech and e-commerce with million-user audiences. Book a consultation — we’ll analyze your project and propose the optimal solution.