When to Use Multi-Armed Bandit Testing Vs. A/B Testing

When to Use Multi-Armed Bandit Testing Vs. A/B Testing

Multi-armed bandits might sound like something from the new Indiana Jones movie, but they’re actually a powerful way to test your eCommerce strategy.

Unlike A/B testing, multi-armed bandit (MAB) testing uses machine learning to figure out what iterations of a website perform better. The idea is to let the testing algorithm evaluate data in real-time and adjust the content served based on that data, eliminating some of the wasted performance that’s inherently a part of A/B testing.

A/B Testing and Multi-Armed Bandit Testing

A/B testing (also known as “split testing”) compares two versions of something to help you determine which version performs better. When applying A/B testing to website performance, you split your audience into two groups—a control group and a test group. While A/B testing typically involves two variants, it can (and often is) extended to multiple variants of the same variable (e.g. if you’re testing where to put product recommendations on a webpage, you can test multiple locations simultaneously).

The control group is shown the existing webpage while the test group sees a different version of the page. After accruing enough data, you evaluate what worked best. Performance is based on a predetermined metric like conversion rate or clickthrough rate. 

Multi-Armed Bandit (MAB) Testing, or dynamic testing, uses a machine learning algorithm to automatically show your website visitors different versions of a webpage based on real-time performance. It adjusts over time, showing people the better performing version of the webpage as it learns what’s working. 

The key difference between A/B testing vs. MAB Testing is that A/B testing requires more time to optimize performance. That’s because you must wait for the data sample sizes to be big enough to analyze—and analysis is done manually. 

Since MAB Testing is dynamic, it gets to the optimization part more quickly. MAB algorithms adjust how content is shown based on real-time performance data. They can also show more than two versions of a webpage, where A/B testing only looks at the test group versus the control group.

When to Use Multi-Armed Bandit Testing

You should use Multi Armed Bandit testing when: 

  • Rapid learning is crucial – Since MAB algorithms make real-time adjustments based on incoming data, this method is useful when you need to move quickly. With traditional A/B testing, by the time you learn which variation is best, the opportunity to apply that knowledge could be gone. MAB testing, on the other hand, can quickly shift more traffic to better-performing variants, maximizing impact. 
  • You’re balancing exploration and exploitation – MAB testing helps solve the explore-exploit dilemma—the decision between exploring new options or exploiting known successful ones. Unlike A/B testing, which explores first and then exploits, bandit testing simultaneously includes exploration and exploitation. This is how MAB testing can help minimize opportunity costs and regret. 
  • Handling many variants and doing continuous experimentation – Because MAB algorithms automatically shift traffic to higher-performing variations, they provide a low-risk solution for continuous optimization. This means they can continuously optimize a campaign or test without constant monitoring. 

When to Use A/B Testing

You should use A/B testing when:

  • Validating major changes – A/B testing, which is a straightforward way of comparing two different things – is a reliable tool for validating a major change before rolling it out permanently. It not only helps prevent costly mistakes, it increases the likelihood of success. Use A/B testing to better understand how features like website redesigns, new pricing strategies, and updated app navigation impact usability, conversions, and sales. 
  • Making clear, binary decisions – A/B testing is straightforward way to compare two versions of something like ads, images, colors, and headlines (to name a few examples). 
  • Ensuring confidence in results – A/B testing helps you isolate exactly what’s impacting performance. Changing only one variable at a time and comparing the performance of the two versions gives you reasonable confidence that the thing you’re testing is what caused a fluctuation in performance. 

Deciding between MAB and A/B Testing

The best way to decide which testing approach to use is by assessing your business objectives and experiment complexity. Dynamic testing is good when you’re implementing a one-to-many personalization strategy over a short period of time. It’s also good when you’re not sure what will resonate with your audience. 

To this end, MAB is perfect in an eCommerce environment since it maximizes traffic performance, which translates to more conversions when you’re implementing it for short-term initiatives like sales and promotions. Since dynamic tests can be run on a smaller volume of traffic versus A/B tests, they allow you to learn quickly, plus they’re fully automated. That means they don’t require manual monitoring and are focused on exploiting the leader in the test.

Another thing to consider is risk tolerance when deciding between MAB and A/B testing. If you’re okay taking risks for potentially higher rewards, MAB testing is a good choice.  A/B testing may incur less risk, but it also slows you down. Plus, it’s not without risk since you may end up sending more people to the poorer performing version of whatever you’re testing.

The context of your experiment also matters. Again, MAB is ready-made for eCommerce scenarios where you need answers—and results—quickly. If you have time to spare, then A/B testing can provide more control, if you are prepared to manually monitor results, and you need a high resolution of performance for all variables. 

Should I Run a Dynamic Test or an A/B Test?

Not sure which type of test is best? This chart will help you decide between running a standard A/B test vs. a dynamic test.

Standard A/B Test Dynamic Test
  • Makes one decision
  • One-to-Many Decisions
  • Static: Assumes Nothing Changes
  • Adaptive: Reacts to Changes in Behavior
  • Thoughtful Experiment Design & Tactics: Sample Size Calculator, Goals, & Conditions
  • Just Tactics: Goals & Conditions
  • Significance: “Measure of Surprise.”
  • Probability: “Chance the leader is actually the best and driving ROI, given everything we know.”
  • Reach Significance, Stop, Promote
  • Continuous Testing and Learning Drives More ROI
  • Manual Monitoring
  • Fully-Automated
  • High Resolution of Performance on all Variations
  • Focused on Exploiting the Leader
  • Measured Understanding of all Metrics
  • Observation of Metrics Beyond Goal of the Leader

MAB testing: A Gateway to Multivariate Testing

Multivariate testing (MVT) evaluates multiple elements on a webpage to determine the most effective combination. MAB testing compliments multivariate testing by leveraging machine learning to dynamically allocate visitor traffic to better-performing variations. 

As the test progresses, underperforming variations receive less traffic, while promising ones get more. Dynamic traffic allocation is a statistically robust method to continuously identify the best version of a page. It ensures most of the traffic gets directed to the winning variant.

Since it evaluates multiple elements of a given scenario, multivariate testing is useful for fine tuning webpage design. It can help you understand what elements create friction and what elements are important to customers on a complex and ‘busy’ page. 

Areas on the page where the density of information is very high, such as price and discount presentation, or multiple CTAs in proximity, can be effectively tested using MVT. 

Evolve your A/B testing and multi-armed testing approach

The process of improving online experiences is continuous. You’ll need to adapt your A/B and Multi Armed Bandit testing approaches based on what’s happening in real time. Think about factors like your expanding reach and growing customer base, which might call for a shift from A/B to MAB testing. 

Business growth plays a huge role in what method you end up using. With MAB’s automation and machine learning, you can handle more data and run multiple tests efficiently. This allows time-crunched and resource-strapped marketing teams to quickly (and effectively) optimize eCommerce experiences.

Finally and importantly, make testing and optimization part of your culture. This is the best way to adapt to changing customer behaviors and preferences. Plus, it ensures you minimize CX risk when it comes to finding a clear winner in any testing scenario.