It’s a known fact that you can’t hold onto something that’s been working for you in your previous campaigns. Advertisement demands innovative and up to date strategies to outrank the competitors. But these strategies don’t always guarantee to boost your campaign results. That’s where AB testing comes into action. It allows you to scale the outcome of new implementing strategies. You can also use this testing methodology to specify the better performing ads in your ad campaigns. Let’s dig more deeply into this.
‘AB Testing’ is actually parallel testing of two different variants or parameters named A and B. This split testing technique lets you determine the outperforming and underperforming factors of two different ad copies. Based on conversion rates or other metrics, you can decide which one outperforms the other one.
Setting up AB testing
For running an A/B test, you need to set up and create two different variants of the ad you want to run a test on, with changes to the variables to be analyzed. Then, you’ll show these two versions to two same kinds of audiences. Depending on the engagements and the performing metrics you would easily analyze which one performed better over a specific time period.
So we can say that:
• You create two parallel variations of an ad copy.
• Set and identify the Goal for which you want to test your ad copies.
• Specify the time period of testing.
• Divide your traffic between the two.
• Target the same traffic
• Record the results over a specified time period
• Observe and analyze which is more effective in generating targeted conversions
Strategies of AB testing
We can devise different strategies in AB testing, depending on the kind of test and the availability of the variants. Well here are two kinds of AB testing strategies:
1. Testing Two Variants
It is the basic and the core AB testing strategy. In this, advertisers split test the two variants during their testing process. In this blog we will further discuss the benefits, problem scenarios and effective techniques of this strategy.
2. Testing Multiple variants
Multi-variant testing uses the same mechanism as A/B testing of two variants, but in this you can compare a higher number of variables, and get more information about how these variables interact with one another. As in an A/B test, engagements or traffic is split between different variants. The purpose of a multivariate test, then, is to measure the effectiveness each design combination has on the ultimate goal.
You can compare the data from different variants to identify the most successful one, similarly you can also potentially get to understand that which elements have the greatest positive or negative impact on a visitor’s interaction.
The problems that can arise here are:
• Having a lot of data to be analyzed.
• Internal competition of ad copies.
Well, you would have to keep a close eye on this to improve your performance measuring. To overcome the issue of internal competition, by setting up a campaign experiment. It allows you to set up campaigns for testing purpose. Learn more about Campaign Experiment here.
Benefits of using AB testing
There are a whole lot of benefits of AB testing including:
• More detailed and specified understanding of User behavior.
• Reduced Bounce rates as improved optimization would assist increase relevance.
• Improved approach of audience targeting.
• Effective strategy development through proper testing.
• Enough data to process the further development of optimization strategies for advertising campaigns.
Some Testing Scenarios:
• AB testing for analyzing User Behavior
As an example let’s consider a Call to Action statement testing. You can use AB testing to have an understanding that if adding a certain call-to-action (CTA) statement at the top of your ad copy text or headline would improve the click-through rate of your Ad copy rather than an Ad copy without
To perform A/B test on this theory, you would have to create two variants of a single ad copy, one with Call to Action and one with missing a CTA. Set the existing copy as Version A. While Version B is the “challenger.” Then, you would parallel test the performing factors of the two. Ideally, you would find a significant traffic difference between these two.
But you are also expected to see not any significant difference among their performance. The problem can be because of the Call to Action value. CTA must be strong and effective to drive actions to your Ad copy. Otherwise, you won’t be able to drive enough clicks to your ad copy. Split testing in this scenario would help determine whether a CTA I working for your ad or not.
• Design analysis AB Testing
Design and color combination is of the critical importance for driving conversions. What format and color would work for your action buttons can’t be said for sure without proper testing data results.
To A/B test this, you’d design two alternative CTA buttons with different button color that leads to the same landing page as the control. If you are using a red call-to-action button in your marketing content, and the green variation receives more clicks after your A/B test, this would suggest changing the default color of your call-to-action buttons to green from here on.
Common problems that might arise during AB testing
Inducing an Invalid Hypothesis
One of the most important A/B testing mistakes is inducing an invalid hypothesis based on the wrongly measured performance parameters.
What actually is an A/B testing hypothesis? It’s basically is a theory about why you’re getting particular results on a web page or ad copy and how you can improve those results. It usually happens if you don’t consider to measure the right metrics for the right goal or performance scales.
For example, let’s assume that an ad copy of your advertising campaign is not getting enough clicks corresponding to the number of impressions that it is getting. In this case, most probably the issue would be lying on the relevancy factor between your bidding keywords your targeted audience. While advertisers might get this confused with the bidding prices and end up increasing bids. Still the results will remain the same additionally you would only be adding to your wasted spend.
Changing the settings during a Testing time period
Changing or editing the settings of your test variations during the time period of running of test would make your test lose credibility. You won’t be able to measure the parallel performance difference without proper and consistent data.
Changing the parameters and data can result in skewed results of metrics you are scaling for performance difference. You can’t expect to have a clearer performance overview of your campaign within a single day. You must test and run your ad variants for some longer specific time period.
Split Testing Multiple Variations
Here is one of the most usual A/B testing mistakes lots of people make and that is trying to split test too many items in a single test.
It might seem like testing technique that might prove to be time saving, but it doesn’t get to. What happens is that you wouldn’t be able to understand and determine which change is responsible for the results. It complicates the things and makes it difficult to identify the outperforming copy as you won’t be observing your ad copies effectively.
Not Running the Test for Long Enough
You need to run your A/B test for a specific amount of time to achieve concrete and mature statistical A/B testing data to hold on to. The time gives your ad copy results a factor of maturity and more accuracy.
Depending on the results from these tests you are safe and free to make new marketing decisions for better optimization of your advertisement.
Don’t just follow but learn from previous case studies
It’s fine to use case studies to get ideas for how and what to split test, but be aware of the fact that what worked for another business might not work for yours too, because your business is unique and it might need some other strategies to be worked on.
So it’s better that you use A/B testing case studies just as a starting point for developing your own A/B testing strategy for your Advertising campaign. That’ll let you see what works best for your own customers, not someone else’s.
Don’t forget to Scale right performance measures
Even though AB testing is a quite potential thing to do still advertisers must have a proper knowledge of what to measure while testing. You should have a clarity that for what you are going to run this split testing.
Measuring the right performance measures is a critical and vital thing in AB testing. You must identify the measures to be measured before starting off your testing process. Read this blog: Essential CRO Knowledge to develop an understanding of the parameters to be measured for different areas of your campaign.