When it comes to testing and analyzing the performance of your website, application, or marketing tools, you need to gain statistics on how users engage with your content in order to know how to optimize and update. As market behaviors and application use are always changing, it is important to gain insights into how your target audience is engaging with your content to better reach them with your products or services.
While the main way of testing for the best marketing strategy has been A/B Testing – an emerging testing method known as Multi-Armed Bandit Testing could offer some further insights and capabilities that A/B Testing is limited in discovering.
Basics of A/B Testing
The gold standard in the online testing market has been A/B Testing. With A/B Testing, two versions of the same website page, email, or application are built and run against one another to discover and compare the performance rates of both. With A/B Testing, the testing occurs randomly with users, with analysis of click-rates, conversions, and other behaviors driving decisions as to the best way to update and optimize the final version.
A/B Testing allows for ongoing analysis of commonly-used tools that companies use to reach clients. However, running a simple A/B testing method on each individual tool can be an extensive process – especially if you are needing to test several elements at once. To help overcome the ever-growing need to test, optimize, and upgrade, the abilities of machine-learning have been brought to testing through the process of the Multi-Armed Bandit method.
What Is Multi-Armed Bandit Testing?
Multi-Armed Bandit testing takes its name from the slot-machine world, where the “one-armed bandit” slot machine would see players pull a giant lever repeatedly to gain an unknown payout each time.
For marketing, Multi-Armed Bandit testing provides a more complex testing solution that is built upon machine-based learning algorithms. Through these AI-based solutions, you can run several options simultaneously instead of a binary option in one iteration.
For example, an online news organization may offer a variety of content for many different users. When it comes to targeting advertising and click-through posts, the standard A/B testing may not be able to give effective or efficient results in an ever-changing, multi-target audience. In this case, a testing algorithm that takes a variety of real-time options into effect is better. With Multi-Armed Bandit testing, various “arms” are pulled on the slot machine to try different outcomes at the same time. As traffic is driven to a variety of options, various outcomes are offered for what content is best for that particular use.
A/B Testing vs. Multi-Armed Bandit Testing
So which version of testing is best for your business’s needs? In choosing between an A/B testing or Multi-Armed Bandit method, you essentially choose between “exploitation” and “exploration”. With A/B testing, your outcomes are limited by traffic being allocated between only two versions of an option. Once one version emerges as a leader, you will often move solely in that direction – which may mean that resources were wasted on the other version’s creation and implementation.
To overcome this limitation, the Multi-Armed Bandit method is more adaptive and flexible through the machine-learning involved. Instead of relying on a binary choice, various scenarios are explored with a different amount of traffic to each. This leads to a variety of “winning” options that can emerge with much more complex statistical analysis available. As the machine learns which option is most effective in real-time, you don’t risk wasting traffic resources on a “losing” method that you will never implement.
Multi-Armed Bandit Algorithms
While Multi-Armed Bandit testing sounds like a great option, it does contain quite a bit of complexity in the ways that the testing operates – which can make the process difficult for those without experience in the method. In order to overcome the complexity that can exist in the Multi-Armed Bandit testing strategy, computer scientists have developed three algorithms to help give quality results:
Epsilon-Greedy
The Epsilon-Greedy method is a machine-learning algorithm that tries to draw a balance between the concepts of “exploration” and “exploitation”. At its most basic understanding, the Epsilon-Greedy algorithm operates by pulling the highest-payout lever most often, while a random lever is pulled a fraction of the time.
Upper Confidence Bound
Based on a principle known as “Optimism In the Face of Uncertainty”, the Upper Confidence Bound strategy makes an assumption that the payoff of each arm will result in the highest payoff based on previous data on the test.
Thompson Sampling (Bayesian)
The Thompson Sampling method uses a randomized probability matching strategy that tries to match the consistency of the “lever pulls” with a statistical probability that the use of that level would be the best and most optimal choice.
Ways To Use Multi-Armed Bandit Testing
Looking for some examples of the best way to use Multi-Armed Bandit testing? Here are some great times to use this strategy over common A/B testing:
Short-Term Marketing Campaigns
If you are working with a limited-time marketing opportunity, A/B testing could cost you valuable time waiting for results. With real-time bandit testing, you could run your campaign tests and implement results quickly.
Continuous Exploring For Changes in Live Marketing
When you have a marketing element that is needing to be tested many times over a period of time, bandit testing provides a great opportunity to run a long-term test over rerunning an A/B test over and over.
Targeting & Learning Based On Common Users
Another great way to use Multi-Armed Bandit testing is with testing content for a specific or target audience. The machine-learning involved with bandit testing can quickly pick up and adapt to common user behavior while spending fewer resources on less-valuable user actions.
Learn More About Marketing Testing For Your Company or Business with Mach7Marketing
Want to learn more about A/B Testing, Multi-Armed Bandit Testing, and many other methods for helping your company or organization build successful marketing strategies? Check out Mach7Marketing – a full marketing team built by industry experts who are focused on helping your team find the best solutions for your industry. Check out more online today.
