In our previous article, we briefly touched on the nature of multi-armed bandit testing and how it was a better alternative to traditional A/B testing. But let’s take a deeper dive into what it really is, why it’s so effective, and how businesses can use it to optimize conversions.
In simple terms, multi-armed bandit testing is a method that balances experimentation with real-time optimization. Instead of rigidly splitting traffic between different versions of a webpage, ad, or email, like A/B testing tends to do, it dynamically allocates more visitors to the better-performing option while still exploring other variations. Think of it as a “smart” A/B test that learns as it goes, minimizing wasted opportunities and maximizing results faster.
Think about it: if one variation is clearly outperforming the others early on, why keep wasting traffic on the losing options? That’s precisely the inefficiency that multi-armed bandit testing eliminates. It dynamically shifts more traffic toward the better-performing variations while still leaving room for exploration. This means businesses can start seeing the benefits of optimization much sooner, rather than waiting weeks (or even months) for conclusive A/B test results.
For companies focused on conversion rate optimization (CRO), this is a game-changer. Instead of letting a large portion of traffic go to underperforming variations, MAB testing ensures more visitors are funneled toward the most effective experience. And with the growing demand for free CRO tools, many businesses are looking for ways to implement this adaptive testing method without massive overhead costs.
But how exactly does it work? And why should marketers, product teams, and growth strategists care?
The core idea behind multi-armed bandit testing
The name “multi-armed bandit” comes from a classic problem in probability theory.
Imagine you’re in a casino with multiple slot machines (or “one-armed bandits,” as the concept was derived from), and each of these machines has a different but unknown payout rate. Your goal is to maximize your winnings.
You could take a traditional A/B testing approach—pulling the lever of each machine an equal number of times and then, after a set period, committing to the one with the highest average payout. But that means you’d be wasting a lot of money on machines that don’t pay well.
A better approach? Start by distributing your attempts across multiple machines but, as you gather data, start favoring the ones that perform better while still occasionally testing others to ensure you’re not missing out on an even bigger jackpot. That’s the essence of multi-armed bandit testing: a balance between exploitation (focusing on the best-performing option) and exploration (continuing to test other possibilities just in case there’s something better).
This method is particularly useful in digital marketing, where conditions change rapidly. Ad performance fluctuates, audience behavior shifts, and what works today might not work tomorrow. MAB testing accounts for these dynamic factors by continuously adjusting its allocation strategy.
In fact, one of the most well-documented examples of MAB testing in action is Google’s approach to ad optimization. A study published in the Journal of Machine Learning Research found that Google Ads uses a variation of multi-armed bandit algorithms to dynamically adjust bidding strategies in real-time. Instead of testing ad creatives in a static A/B format, Google’s system continuously shifts budgets to the best-performing ads while still exploring new variations.
The result? A more efficient allocation of ad spend and a higher return on investment (ROI) for advertisers. Businesses using Google’s smart bidding features are, in essence, benefiting from MAB-style optimization without even realizing it.
How multi-armed bandit testing differs from A/B Testing
At first glance, A/B testing and MAB testing seem similar—they both compare different variations to determine the best performer. But their methodologies, efficiency, and overall impact on conversion rate optimization are vastly different.
Traditional A/B testing follows a rigid structure:
- You create two (or more) variations of a webpage, ad, or email.
- Your traffic is evenly split between these variations.
- You wait until a statistically significant amount of data is collected.
- You declare a winner and roll it out to your entire audience.
The problem? This approach assumes that traffic should be evenly distributed—even when one variation is clearly underperforming. It also ignores changes in user behavior over time.
Multi-armed bandit testing, on the other hand, is dynamic. As soon as one variation starts outperforming the rest, more traffic is allocated to it while still allowing for occasional testing of other versions. This speeds up optimization and reduces opportunity costs, especially for businesses where every percentage point in conversion matters.
The algorithms behind multi-armed bandit testing
MAB testing isn’t just one technique—it’s a collection of different algorithms designed to balance exploration and exploitation in various ways. Some of the most commonly used methods include:
- Epsilon-greedy strategy
This method splits time between exploration (randomly testing different variations) and exploitation (focusing on the best-performing option). For example, if you set an “epsilon” value of 20%, the system will randomly test different variations 20% of the time while using the best-known performer 80% of the time.
Best for: Simple tests where you want a balance between trying new options and maximizing performance.
- Thompson sampling
This is a more probabilistic approach, where variations are selected based on their likelihood of being the best option. The algorithm continuously refines its predictions, making it highly efficient for conversion rate optimization.
It’s best for situations where you need a more statistically robust approach to decision-making.
- Upper confidence bound (UCB) algorithm
This method prioritizes variations that have performed well but also ensures that under-tested variations still get occasional traffic. It’s ideal for cases where you want to maximize efficiency, long-term learning, and optimization strategies.
Why every business should be using MAB testing and how to get started
If you’re serious about conversion rate optimization, relying solely on traditional A/B testing is like driving with one foot on the brake. It’s slow, inefficient, and wastes valuable traffic on suboptimal variations.
Multi-armed bandit testing, on the other hand, ensures that every visitor is being directed toward the most effective version of your page, ad, or email campaign at all times. This results in:
- Higher conversion rates: More users are exposed to the best-performing variation sooner.
- Faster decision-making: No need to wait weeks for conclusive results.
- Reduced wasted traffic: Less time spent pushing ineffective variations.
- Better adaptability: Ideal for fast-changing industries or seasonal trends.
And the best part? You don’t have to manually adjust anything. AI-powered tools like Growie, a free CRO tool, handle all the complexity for you. Growie’s AI CRO Manager not only runs MAB tests but also identifies conversion leaks and provides actionable solutions—proactively optimizing your website without you having to lift a finger.
In the end, multi-armed bandit testing isn’t just an alternative to A/B testing—it’s an upgrade.
Whether you’re optimizing landing pages, fine-tuning ad creatives, or experimenting with pricing models, MAB testing ensures that you’re always making the most of your traffic. And in today’s hyper-competitive digital landscape, that’s an edge you can’t afford to ignore.
