Howard Hubler Which is better to write about in an advertising headline – the main value of the product or promotions and discounts? Which call to action on the button will work better: “Learn more” or “Buy Now”? Which email headline will give the best email open rate? Professional marketers don’t guess, but get clear answers to these questions by conducting A/B tests.
What is A/B testing and where is it used?
A/B tests are used to compare different versions of advertising content in order to determine the most effective one. This tool allows marketers to experiment and analyze audience response to different versions of ads, websites, landing pages, emails, etc.
In A/B testing, two (or more) variations of content are created where one element is changed (e.g., headline, image, or text) and the rest remains the same. These variants are then shown to different audience groups to compare and analyze the results. By collecting data on which variant yields higher engagement, conversion rates, or other key metrics, marketers can make informed decisions. The main tool and pillar of A/B testing is end-to-end ad analytics.
How to properly conduct A/B-testing
For proper A/B tests, you need to pay attention to each step of the process and be sure to rely on analytics.
Below we describe the main components of A/B testing.
- Defining objectives. They should be clearly stated: for example, increasing conversions, increasing clickability, or improving other metrics.
- Hypothesis formation. Every A/B testing should be based on a clear hypothesis. It defines what exactly you are going to test and what changes you expect to see.
- Variant Development. Creating several variants – the original and the modified version (A and B).
- Planning the experiment. Determine how long the test will last, how many shows or users will be needed to get statistically significant results.
- Conduct the test and collect the data. Run the experiment and collect data, making sure that only one of the options applies for each user or event.
- Analyze the results and make decisions. Once the data is collected, analyze and make a decision based on the results.
- Apply the change. If one option performed significantly better, implement changes based on that option.
How hypotheses for A/B testing come about
The main goal in the birth of a hypothesis is to identify a specific aspect that could possibly be changed to improve audience response.
For example, a hypothesis might be that changing the title of an online ad will increase clicks or that changing the location of a call to action (CTA) button on a website will increase conversion rates. Hypotheses may be born from analysis of user behavior, previous test results, or competitor research.
Ad analytics and test results evaluation
Across-the-board analytics is the evaluation of the effectiveness of any advertisement from click to sale. It allows you to track how A/B testing affects all the key metrics listed in the previous section. You’ll see how the change you’re testing affects user behavior at all stages of interaction with your ads. With ad analytics, you can understand if the change is also affecting customer relations in the long run.
Only armed with ad analytics data, professional marketers make final decisions.
Conclusion
A/B tests make it possible to identify the most impactful and converting ad content out of several variants. The testing is based on the verification of one or another hypothesis. Correct A/B-tests are possible only when using an advertising analytics service that shows the effectiveness of the tested content in the context of all the metrics relevant to the marketer.
