A/B or split testing has been the standard way to optimize marketing campaigns for years. Google first ran an A/B test in 2000 to identify the optimum number of searches to display on its result pages. Today A/B testing is common practice in many different digital marketing channels including display ads, landing pages, email marketing, and pretty much anywhere that copy, images, or placement can be adjusted.
A basic example of A/B testing would be splitting visitors to a website into two groups (A and B) and showing each group a slightly different version of the homepage.
Everything else might be the same on the page apart from the header image. Let’s say group A sees an image of a group of smiling people and group B sees an image of a city skyline.
The reactions of each visitor are then recorded to see if they are completing the desired action. In this case that might simply be clicking through to another page on the website.
Once a sufficiently large group of people have been shown either version A or version B of the website, you should have enough data to decide which version is most effective.
If 30% of group A converts, but only 10% of group B converts, you can reasonably conclude that the group A image is more effective at converting website visitors.
A/B testing enables you to increase your conversions without increasing your traffic. By experimenting with different images, headlines, CTAs, colors, and other variables on your site, you can optimize your conversion rate. However it can take some time before you’ve tested enough different variations with a large enough visitor group to make a definitive conclusion.
A/B testing works but it isn’t terribly efficient. You’ll need to use a significant amount of time and resources to carry out your tests before you can gain any meaningful results.
AI is now providing an exciting alternative to A/B testing that enables websites, ads, and other online assets to “self-optimize” in real-time.
Software driven by artificial intelligence can analyze the actions of each individual web visitor as different variations of your site are continually served up to each user.
In this way, rather than testing A versus B, you can introduce C,D,E,F, and G into the equation as well and try out different combinations such as header image A with headline B and CTA C.
Analyzing the data to work out exactly which combination of these different variables produces the best result would be a highly complex task for any human, but machine learning algorithms can continuously collect data and deliver the best possible variation to each individual user in real-time.
Not only that, but instead of considering each visitor to be equal as in split A/B testing, AI can take into consideration factors such as demographics, customer status, and previous behavior to dynamically serve up different versions of your site to different groups of users.
The power of AI enables you to personalize and optimize your web properties from thousands of potential variations to display the single version that offers the best chance of conversion for each individual visitor.
Machine learning algorithms sound like a complex undertaking – and they are – but as the software takes care of everything and makes the most of the decisions for you, all you need to do is provide some different variables to test, and then sit back and let it do its thing.
We’ve already touched on some of the advantages that AI has over traditional A/B testing, but just to lay it out in plain English:
AI is Faster and More Efficient than A/B Testing
Traditional split testing is very time consuming, especially if you want to test out several different variables. You have to carry out a single test for each different element you’re experimenting with and wait for a fairly conclusive result before you can continue with the next test.
Because you need a substantial audience size for each test to obtain any kind of meaningful results, A/B testing can also be very resource-intensive, often requiring a team dedicated to the task or outsourcing to an expensive marketing company.
As AI does the hard work for you, it frees up your time and resources to concentrate on other more strategic aspects of your marketing strategy.
As your assets can be optimized continuously in real-time, there’s no need to carry out A/B tests for months before deciding on the final result.
A/B Testing Only Tests One Variable
If all you want to do is compare a couple of different images or headlines, and assume all your users will react to them in the same way, then split testing will do the job.
However, AI enables you to experiment with a potentially infinite number of different variables. And, you can take into account the fact that different users may be more or less likely to convert depending on one or more of those variables being changed.
You might be so focused on trying out different colors and images that you don’t even think to check if making your headline text a few pixels bigger will make any difference to your conversion rate, but AI can figure this out for you.
AI can also determine that one headline is optimal for converting female mac users aged 25 – 35, while another one is best for male PC users aged 45 – 55. It’s just not possible to achieve this level of granularity with normal A/B testing.
AI Enables You to Optimize Your Entire Funnel
With A/B testing, just as you can only test changing one variable at a time, you can only concentrate on optimizing one page or asset at a time.
As a standard sales funnel may consist of several different landing pages, emails, ads, and other assets, it can be very challenging, not to mention time-consuming and resource-intensive to make sure that each part of the funnel is optimized to your liking.
AI enables you to follow each individual user on their own unique path through the funnel, testing and optimizing as you go. This increases the effect of your optimization efforts, resulting in a higher conversion rate and better performing funnel overall.
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