Exploring Multi-Arm Bandit Testing
Similarities and Differences Compared to A/B Testing
Multi-Arm Bandit Testing: An Overview
Multi-armed bandit testing is an adaptive allocation process that aims to maximize overall learning and exploration without unnecessarily sacrificing real-life performance during testing.
Sound like a lot? Don’t worry, in this article we’ll break it down.
By the end of this post, you’ll understand the basics of multi-arm bandit testing, know where to go for a deeper dive, and know how it compares to A/B testing.
Functionally, the definition from above means that if you’re willing to work with a lower degree of confidence than pure A/B testing (a reasonable assumption in the real world), you can test many more variations in parallel to optimize a customer experience.
In my last article, we explored the advantages of A/B testing over uncontrolled testing.
But like many great ideas, A/B testing often works better in theory than in practice.
Sequentially testing features to statistical significance can lead to a months- or years-long backlog.
A faster solution for testing multiple modifications at once is multi-arm bandit testing.
In this blog post, we’ll dive into the world of multi-arm bandit testing and explore its similarities and differences compared to traditional A/B testing.
What is Multi-Arm Bandit Testing?
Multi-arm bandit testing takes its inspiration from the "one-armed bandit" slot machines, where players must choose which slot machine to play.
Instead of the strict binary allocation of traffic seen in A/B testing (test and control), multi-arm bandit testing dynamically distributes traffic to multiple variants based on their performance.
An Example:
Multi-arm bandit testing is a scientific way to test your friend’s theory that fishing spots that are named after animals result in better fishing days.
Without testing at all, you could live your whole life missing out on the best fishing in the world. (In this case, you better bring some beer)
With A/B testing, you’d pour the same amount of time and effort into every fishing hole and then you’d compare the results. (Potentially wasting a lot of vacation days on barren fishing spots)
Instead, with multi-arm bandit testing, you should try to spend the least time exploring the animal-named and non-animal-named spots and the most time exploiting the best spots. (Ensuring that your fishing days are geared to give you the best results)
For a great deep dive into multi arm bandit testing (with cute baby robots as main characters), check out this 6 part series on Towards Data Science
Key Aspects of Multi-Arm Bandit Testing:
Exploration vs. Exploitation: Unlike A/B testing, multi-arm bandit testing strikes a balance between exploring new variants and exploiting the current best-performing variant, allowing for continuous optimization. See Part 6 in the linked series above for a numerical comparison of various exploration/exploitation approaches and when they might be appropriate.
Adaptive Traffic Allocation: Traffic is dynamically distributed to the different variants based on their performance, optimizing the allocation to achieve the best possible results.
Bayesian Testing: Multi-arm bandit testing leverages Bayesian statistical methods to make informed decisions, updating probabilities of success as data accumulates. This means that you’re working with confidences that vary over the course of testing. Warning, these are notoriously unintuitive to work with, but are extremely useful.
A/B Testing: Traditional Approach to Experimentation
A/B testing, also known as split testing or bucket testing, is a method of comparing a test and control condition so you can develop a better understanding of what drives user behavior.
Check out my previous article for a full introduction.
A/B testing has long been the gold standard for testing and optimizing digital elements, but it can be time consuming to sequentially test each variable.
Advantages of A/B Testing:
Simplicity: A/B testing follows a straightforward approach, making it easy to understand and implement.
Statistical Rigor: With large sample sizes, A/B testing can provide reliable statistical significance and confidence in the results.
Clear Winner Determination: A/B testing identifies a single winning variant based on predefined success metrics.
Similarities and Differences Between Both
Similarities:
Test Goals: Both A/B testing and multi-arm bandit testing share the goal of optimizing asset or experience performance and driving desired behavioral outcomes.
Data Analysis: Both methodologies rely on statistical analysis to draw conclusions and make data-driven decisions.
Differences:
Traffic Allocation: A/B testing maintains a constant traffic split between two variants, while multi-arm bandit testing dynamically adjusts traffic allocation based on variant performance and chosen approach.
Experiment Duration: Multi-arm bandit testing generally requires less time to reach optimal results compared to A/B testing, as it continuously adapts the traffic allocation.
Statistical Significance: A/B testing provides a clear statistical significance and winner determination, whereas multi-arm bandit testing relies on probabilistic approaches and optimization algorithms.
Conclusion
While A/B testing has been the go-to method for optimizing digital experiences and assets, multi-arm bandit testing offers a dynamic and adaptive approach to experimentation.
By striking a balance between exploration and exploitation, multi-arm bandit testing enables marketers to maximize performance and learn continuously. Understanding the similarities and differences between these two methodologies empowers marketers to make informed decisions and choose the most suitable testing approach for their goals.