How successful is your landing page? Could it be better? Should you place that Call-to-Action button at the top or bottom of your page?
A/B tests help you test and control against a variable and find out which variable option determines the best response. Artificial intelligence (AI) is capable of measuring the same type of responses, albeit, in a better way. In this post, we will explore the benefits of AI and how it can complement your A/B tests.
How AI is Surpassing A/B Tests
Marketers have depended on A/B or split testing to find out the best performing aspects of their website. Although, due to its shortcomings, AI and machine learning have emerged as favorable options opposed to A/B tests.
Test More Than One Variable at a Time
You can only test one variable using A/B testing. For example, you can create two separate landing pages of your website and show them to two different subsets of your traffic. The landing page attracting higher engagement emerges as the winner in the split test.
On the other hand, you can use machine learning to run multiple tests simultaneously throughout your entire marketing funnel; whereas, A/B testing restricts you to one variable of one section of your funnel.
As a result, you can free up your time to conduct A/B tests only on significant and prominent hypotheses and use AI to run multiple smaller tests simultaneously, helping you to make more productive uses of your time.
The use of AI can reduce the time it takes to run traditional A/B tests. You can focus on testing larger aspects of your website rather than running split tests for all its elements.
A/B tests also require a lot of prior market research and people power to conduct tests. It takes developers time to code changes and tests, often, running for weeks at a time. If you’re testing a single variable, you will have to plan out space to isolate and measure the impact of its split test.
Machine learning relieves you of the task of conducting both market research, and isolating variables for testing, as its algorithm can take your data, establish user patterns and test changes based upon activity relevant to their customer journey.
The latest algorithms can run tests and optimize future tests based on deep reinforced learning, helping it discover the choices that produce best results while discouraging ineffective choices. It’s easy to see how machine learning can quickly and significantly reduce issues like user drop-off.
You can only use a small subset of your traffic while running an A/B test. It’s not a good idea to test the entirety of your audience, as you might lose traffic if tests perform poorly. You only select as much traffic as required to make the test statistically relevant. Once you have your findings, you are left to extrapolate, leaving you with a small confirmation of an inclination.
You also can’t rule out unrelated factors that might affect your tests. By restricting yourself to only two permutations of one variable and ignoring other factors, you are limiting potential avenues of insight. An individual variable change can help your website increase its conversions; however, multiple, self-correcting variable changes can exponentially increase your website’s conversions in the same time or less.
In reality, industry standards dictate A/B tests yield between 10-20 per cent success rates. This is due to the fact tests are, at most, educated guesses.
AI testing can help you save time by running multiple tests simultaneously and generating meaningful insights into the best combinations of variables affecting user decisions.
AI Reduces Luck in Testing
Luck is elemental in all things, but it especially applies to A/B tests to find the right audience or creative. AI, on the other hand, reduces the impact of luck affecting your tests.
Explore and Exploit Method
The explore and exploit method enables you to optimize website performance, according to criteria you set, through incredibly rapid testing. You can use an evolutionary algorithm to determine the best place to position assets and enhance the outcomes of your core metrics according to their weight in the algorithm.
You just need to select the core metrics you want to optimize for and choose the weighting for each desired outcome. The algorithm will then begin the testing process, reinforcing combinations of assets that help to improve a core metric according to its weight or importance. For example, if your website most prioritizes add to cart conversions, evolutionary algorithms will move web pages assets until they are optimized to incentivize e-commerce actions.
With A/B tests, you can concentrate on single focus element changes, while
AI helps you to exploit the best wholistic variant combinations from the very start.
AI can successfully detect micro-interaction patterns between content human eyes can not. It’s hard for people to understand a wide range of micro-changes and their impact on increasing conversions; however, with the help of AI, we are beginning to understand they tend to create a compounded effect when combined.
You can use AI to augment your user interface designs for best conversions on your chosen metric. UX optimization enables you to change the creative assets of your website across its page sections by taking sample points, each with multiple variations, and exploring combinations in order to determine winners.
What makes UX optimization so powerful is each page can be optimized for a different metric.
This means you can optimize your home, product, and contact pages differently corresponding to their optimal metric.
AI testing lets you quickly gather information by testing seemingly endless asset combinations. By simultaneously testing every possible combination, you will learn far more than using A/B testing alone. If you used A/B testing for the same job, it would take years.
AI can cut down the element of luck by helping you find the right variants from the start. You can also detect micro-changes and optimize your UX to identify the best performing page asset combinations.
Use AI and A/B Testing Together
AI is not here to replace A/B tests – you still need them to test an educated hypothesis. Moreover, AI can help you enhance the testing process and discover the right elements to increase efficiency.
AI as Companion to A/B Testing
You can use AI to explore and fulfill your goal of achieving the optimal conversion levels. When you are relying on A/B tests, you cut down the possibilities of a more effective test which could potentially perform better.
AI also helps you discover unexpected correlations. Once an unexpected change improves your conversions, and you have a working hypothesis as to why its working, use A/B testing as a confirmation tool to see if you can translate success to other assets.
AI is capable of identifying patterns between your best performing content. Take for instance, Google Responsive Ads, which optimizes your content based on a data set. You input variations of ad content such as different headlines, product descriptions, and images and Google Responsive Ads tests each combination over time to identify the best performing ad.
AI can help you derive conclusions more effectively, especially if you are dealing with a larger volume of data. Use AI-based testing to discover successful variable combinations and use A/B tests to confirm your hypotheses; you might be surprised how successful you become.