A/B testing (or split testing) has long been a staple tool for marketers, a useful way of testing two or more creatives against each other to determine which gives the best ROI. It has never been perfect, but there's never been a viable alternative. Until now.
Should you make the switch? Let's examine how AI compares to A/B.
What is A/B Testing?
A/B testing involves comparing two versions of an ad to see which one performs better over time. Marketers take many samples by running the different versions of the ad and measuring performance, with the aim of reaching a statistically-relevant conclusion regarding their performance.
You continue testing until you reach a predetermined level of confidence about your results. If you want to be 99% certain that your results are statistically correct, you'll need a lot more samples than if you are happy with a 95% certainty.
A/B testing is a scientific approach which, when done right, will give you an accurate result and actionable data. However, this method isn't always appropriate, for several reasons:
A/B Testing Requires a Large Volume of Samples
For most tests, the number of samples required is quite high. For example, let's say we have one variation with a conversion rate of 5% against which we want to test a second variation. If you do the calculations you'll discover it takes a sample size of almost 750,000 per variation to statistically prove whether one variation is better or worse than the other (assuming a minimum detectable rate of 2%). We can reduce this number, but only by making the test less statistically accurate.
If you cut the test short the results you've got aren't statistically relevant, so you must see it through to the end. This problem only increases when a business wants to test multiple ad designs against each other - the number of samples you need really adds up.
Businesses with smaller marketing budgets can soon eat up their monthly spend testing various creatives without necessarily finding an improvement (and the spending on the less-than-optimal creatives can be a significant opportunity cost).
Using the Same Ads for Long Periods Causes Ad Fatigue
With repeated viewings, ads become so familiar that they might as well be part of the furniture. This phenomenon is known as 'ad fatigue' and is a serious problem for many marketers; as a creative becomes more familiar, customers become less likely to respond to it. In some campaigns, ad fatigue can start to set in after as little as three days.
By the time you've tested multiple versions of an ad, customers may already be starting to experience fatigue. It won't matter how much you improve your ad if customers are bored of it and no longer paying attention.
Marketers Are Under Pressure to Test Faster
With ad fatigue a problem, marketers are under pressure to deliver results fast. The constraints and pressures they experience encourage them to cut A/B tests short when they see a seemingly positive result (even though it isn't statistically proven yet). This misses the whole point of A/B testing, which is to arrive at a statistically-relevant conclusion.
Instead of spending the proper time on their testing, businesses are instead wasting money with unproven creatives. Cutting A/B testing short, or relying on personal opinion and taste to decide the direction a marketing campaign takes, results in few long-term improvements in ad quality.
A Failure to Optimize Ads Pushes Up Costs
Because of these problems, many marketers are either failing to optimize their creatives or are suffering from ad fatigue. When ad quality suffers, advertising costs (e.g. CPM, CPC,creep up, and testing becomes even more expensive.
For many businesses, this is a vicious cycle that can be difficult to escape from.
How Can Artificial Intelligence Help?
Advances in AI mean it can be used as a predictive tool that uses market and consumer data to predict the future success of an ad before it has been launched. Because the work is done using advanced algorithms, artificial intelligence can perform this function far more accurately and at a much larger scale than a human marketing manager.
Additionally, the predictions offered by an AI offer significant value compared to A/B testing because you get an indication of your results before spending any money on your ads. Marketers save time, money and effort, which enables them to make more informed decisions about a much larger range of creatives.
Introducing ReFUEL4's Automated Creative Scoring
• Creative Quality - The AI scores each ad creative according to its predicted future performance, advising if you should switch out your current creatives with new ones.
• Creative Quantity - The number of creatives in your ad set will impact your ad performance and CPM. ReFUEL4's AI will indicate the correct number of creatives to optimize performance.
• Creative Refresh - The creative refresh rate advises on how often you should swap out ads to prevent ad fatigue creeping in.
ReFUEL4 works with a global network of more than 10,000 designers who deliver a wide range of creatives on-demand. Using ReFUEL4's predictive AI, businesses can quickly choose, launch and manage their ads, achieving efficient results and an effective digital campaign in a fraction of the time it normally takes . In a matter of days, a business can test a variety of ads that might take them months to achieve internally.