Traditional A/B testing follows a rigid but straightforward approach: split your traffic evenly between variants, wait for statistical significance, then pick a winner. But what if you could start capturing more conversions while your test is still running?

The Problem with Traditional A/B Testing

In a standard 50/50 split test, half your visitors see the losing variant for the entire duration of the experiment. If you're testing for two weeks and Variant B is clearly outperforming, you're still sending 50% of your traffic to the underperforming version. That's lost revenue and missed opportunities.

Enter Multi-Armed Bandits

Multi-armed bandit algorithms take a smarter approach. Instead of maintaining a fixed traffic split, they continuously analyze performance and gradually shift more traffic to better-performing variants. Think of it as "learning while earning"—you're gathering data for statistical validity while simultaneously maximizing conversions.

The name comes from slot machines (one-armed bandits) in casinos. If you had multiple slot machines with varying payout rates, you'd want to identify the best one while occasionally checking the others. Multi-armed bandits solve this "exploration vs. exploitation" trade-off elegantly.

How Autopilot Mode Works

Optux.ai implements multi-armed bandit optimization automatically:

1. Initial exploration: Traffic starts distributed across all variants

2. Continuous learning: As data accumulates, the algorithm identifies patterns

3. Dynamic reallocation: More traffic gradually flows to winning variants

4. Sustained optimization: The system keeps monitoring to catch any shifts in performance

The Business Impact

The benefits are substantial:

• Increased revenue during testing: By sending more visitors to better variants, you capture additional conversions that would be lost in traditional tests

• Faster ROI: You don't have to wait until the end of the test to benefit from insights

• Reduced opportunity cost: Less traffic wasted on poor-performing variants

• Automatic optimization: No manual monitoring or intervention required

When to Use Autopilot Mode

Multi-armed bandits are ideal when:

• Conversion rate optimization is your primary goal.

• You're testing on high-traffic pages where quick wins matter.

• You want to strike a balance between learning and achieving immediate business results.

• You're running ongoing optimization rather than one-time research.

With Autopilot Mode, you transform testing from a static experiment into a dynamic optimization engine—one that learns and earns simultaneously.