Leveraging AI for smarter experimentation: Multi-armed bandit

Understanding and engaging your audience can make or break your digital presence. With that in mind, testing and experimentation have never been more crucial to the content optimization process. 

Traditional A/B testing has long been the backbone of website optimization and content personalization strategies. However, as content management technology advanced, and the era of artificial intelligence (AI) dawned across the marketing landscape, the limitations of traditional testing methodologies have become increasingly apparent, and brands have sought new ways to determine what their customers want from their ideal digital journeys. 

At Contentful, we understand how important it is to be able to craft unique and engaging digital content experiences, and then optimize those experiences to get the most engagement possible from your content. To that end, our AI-powered testing tool sets out an array of digital experimentation possibilities — including both A/B testing and multi-armed bandit (MAB) testing tools. 

In this post, we’re going to take a closer look at multi-armed bandit testing, and how it compares to the more traditional A/B testing approach. Let’s dive in.

What is multi-armed bandit testing?

The multi-armed bandit approach takes its name from a hypothetical gambling scenario, where a player seeks to maximize returns by choosing the most rewarding slot machine (or "arm") from a choice of several. These machines are often referred to as “one-armed bandits,” but when a gambler plays multiple machines in a row, they become “multi-armed bandits” because they now offer multiple possibilities to win (or lose). 

With multiple “bandits” now in play, it’s more likely that a gambler will focus their attention on the slot machines that are paying out more — “performing” better — and neglect the slot machines that aren’t — those that are “performing” worse. 

While it's not really about gambling in the context of content marketing, AI, and experimentation, there is still a degree of speculation on the unknown as you wait for the outcome of your test. That’s because multi-armed bandit algorithms apply that same multiple action principle to competing test variants, with brands observing the results, and managing and allocating resources to each variant based on real-time performance data. 

How is multi-armed bandit testing different?

Unlike traditional testing methods, which divide resources equally, regardless of outcome, multi-armed bandit tests continuously adjust resources in favor of the most effective options. Here, “resources” refers to the traffic that is allocated to the test variant. The thinking is, traffic that is allocated to a better performing variant is much more likely to provide actual value to the business — in the form of a conversion — rather than being “wasted” on a testing variant that’s less likely to convert. 

MAB testing leverages technology to facilitate a smart allocation of traffic, a continuous optimization process which leads to more efficient and effective experimentation, and maximizes the value of every interaction with your audience. 

A/B testing vs. multi-armed bandit

To better understand the advantages of MAB testing, let’s compare it to A/B testing. 

In an A/B test, you’re always testing one variant against another directly, with 50% of traffic directed to variant “A,” and 50% to variant “B.” In this context, 50% of your traffic is, by definition, directed to a poorer performing test variant, that’s less likely to convert, and less likely to provide value to the business. 

A/B testing also requires you to set out, and operate on, a fixed testing schedule, which includes a phase in which to gather data, and an analysis phase to determine the statistical significance of the data you've obtained. This schedule means that there's almost certainly going to be a delay in implementing superior variants on the live site.

While A/B testing is data driven and useful in many contexts, it doesn’t give you the flexibility to adapt to emerging patterns during the testing phase.

The multi-armed bandit algorithm overcomes these limitations by adopting an adaptive learning approach. This approach intelligently distributes exposure amongst variations in real-time, based on their ongoing performance.

While there are clear benefits to the MAB approach, it's also worth pointing out a multi-armed bandit problem: you'll need to make sure you're running your test for sufficient time to obtain accurate data on your variants, otherwise there's a danger you'll rule out a potentially winning variant prematurely. It may be the case that some unknown variable is affecting the performance of one test variant over another, and skewing your test results unfairly.

In short, MAB testing not only accelerates the discovery of optimal content but maximizes engagement and conversion rates throughout the testing process.

Multi-armed bandit testing in action

So, how do multi-armed bandit experiments work? Let’s look at some examples. 

Consider an online retailer testing promotional banners on its homepage.

With A/B testing, equal traffic is directed to two or more variants, with data collected over weeks. When the test is complete, and its results analyzed, a winning variant can be declared and implemented on the live site. 

Now, imagine the same variants in a multi-armed bandit experiment. In this context, the MAB algorithm quickly identifies a frontrunner variant, incrementally directing more traffic to this variant, which significantly boosts sales before the test actually concludes and delivers its results.

Another example multi-armed bandit test might involve a media site experimenting with headline variations with the objective of increasing article views.

With the benefit of an MAB testing tool, the site would be able to dynamically shift traffic exposure toward the better-performing headlines, driving more engagement, elevating overall viewership, and reducing bounce rates more efficiently than a static A/B testing approach.

Take the next step in experimentation

Contentful’s digital testing and experimentation tool, Contentful Personalization includes a range of cutting-edge, AI-powered testing options, including A/B testing and MAB testing features. 

Our native-AI algorithm helps you automate your testing strategy, defining audiences, creating experiments, and performing analysis from within the Contentful headless CMS. Whatever content you want to create, and whatever testing methodology you want to use, our platform helps you explore what’s possible, and keep pace with your customers’ expectations. 

Ready to revolutionize the way you engage with your audience? Unleash the full potential of your content and your customer data today. 

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