Why you should consider AB testing in your Ad Campaigns

It doesn’t matter if it’s for a Landing Page, a Creative or a Copy. For design optimization and greater Results, A/B gives data you can’t ignore.

Before getting started, it is important to mention that there is a plethora of options for experimenting variants:

A/B testing, A/B/n testing, multivariate testing, multi-armed bandit testing, multi-page funnel testing etc. For the sake of the article’s length, we will only cover the use of A/B testing on ads.

What is A/B testing?

A/B testing, commonly known as Split testing, is a randomized experimental procedure in which two versions of a piece of content are tested (Creatives, Copy, Landing Pages, etc.). In simpler words, in an A/B test, you take a piece of Content such as an Image, and you modify it to create a second version of the same Image. This change can be as simple as a single headline Font, Background Color or a Layer. The original version of the Image (known as the control) is then shown to half of your Audience, while the updated version (the variation) is presented to the other half. Users’ interaction with each encounter is measured, gathered, and then evaluated. After that, you will be able to see whether the modification of the experience had a good, negative, or neutral impact on the user’s behavior.

Source: Data Science Dojo

A/B testing on Ads may be done on the following digital elements:

Design and layout
Ad Copy
Headlines
Video and Image Colors
Call to Action

Why do A/B testing on ads?

A/B testing is vital because it allows you to determine what works best in your particular situation. Many researches have been conducted to determine the best effective marketing tactics in general. General guidelines are a good place to start, but they aren’t necessarily the best in every circumstance. You may transform generic concepts into particular outcomes for your business by conducting your own testing.

Organic or Promotional Split Testing reveals the preferences and dislikes of your target audience. Remember, people who follow you on Instagram, may not have the same taste as those who follow you on Facebook, or those who search for you on Google. With the help of continuous A/B Testing, you will gain insights about what works best for you on each channel.

Why continuous A/B Testing?

If you already A/B tested your campaign and found a winner, you are not done.

It is tempting to think that once you have achieved your goal, you will have to stick with the campaign that brought you good results and stop testing.

Well, eventually the winning campaign will no longer be relevant. Your audience will become tired of seeing the same thing over and over, and your performance will suffer. Make sure you stick to A/B testing for all your advertising efforts whether it’s google ads, facebook ads etc.

What if the campaign loses performance during A/B Testing?

For many marketeers, this is a very reasonable worry. Starting a new A/B testing on an Ad that is performing well might lead your audience to a variant that it’s not, resulting in missed chances. We get that, but how will you know what works for your business if failure is not there.

Each time you identify a variant that did not perform well, you just learn from it and know what works and what doesn’t, not only for future campaigns, but also for the rest of the parts of your marketing efforts. Remember that A/B testing isn’t just for determining the ideal Variant for your audience; it’s also for understanding your audiences’ behavior.

Now that you know what A/B testing is and how it works in advertising, try it out on an ad that does not work well. You’ll have a better understanding of your customers while also increasing the efficacy of your marketing.

Those of you who might have made sense of what I have been talking about in this article, you may ask the 5 million dollar question:

“But Demetri, haven’t machine-based optimization algorithms automated A/B testing?”

Well Jeez, we’ve just covered what A/B testing is and why it is so important on digital advertising; machine learning and all that, is a question to be answered perhaps on a later date blog post! Stay tuned!