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A/B Testing Made Simple: What You Need to Know and Why You Need It

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As a results-oriented organization, defining metrics and KPIs are critical to the success of our clients. At KM&A, evaluating data is an everyday practice to ensure that our marketing efforts are effective and continue to garner measurable results.

A/B testing is the process of comparing two marketing elements against each other to see which one performs better. From email subject lines to display ads and social media imagery, A/B testing allows marketers to test which version resonates with audiences by measuring the click-through rate (CTR), open rate, desired consumer behavior and other indicators crucial to campaign success.

Why A/B testing?

There are a lot of grand ideas that happen in marketing and advertising. At a glance, some seem great, some not so good, but even the most creative idea does not guarantee results without testing. A/B testing in digital and social spaces allows our team to test which campaign element is more effective and optimized for increased success. By continuing to test different variations, we’re able to make data-driven decisions that not only resonate with the target audience but improve conversion rates and ultimately move the needle for our clients.

For example, a client is not sure if the language and graphics used during a Facebook ad campaign is the best option. By segmenting the target audience into groups and delivering a different version to each, marketers can see which has the best performance, adjust the campaign and achieve the desired outcome.

It’s about the goal.  

A/B testing can be a great way to gather actionable insights from the marketing strategy, but only if the data collected is utilized appropriately.  Prior to implementation, define your goals and objectives. Are you aiming to drive sales? Increase event registration? Or to simply boost your website traffic? This is key in identifying KPIs that align with each objective and use it as a metric when testing whether option A or B was more effective in reaching that goal.

Example:

Your defined goal was to increase the purchase rate =

A saw a high click-through rate, but lower purchase rate

B saw a lower click-through rate, but a higher purchase rate

Because the goals and metrics were established, it is clear that option B should be selected because the goal was to increase the purchase rate, not the click-through rate.

When assessing the analytics after a test, track the goal against the data to evaluate its effectiveness towards reaching the goal. Using the example from earlier, if conversion tracking wasn’t set up, it would have been difficult to determine how A and B were performing in regards to the goal.

Let’s Make it Happen.

From strategy to execution, KM&A is equipped with the team and the tools to help make savvy decisions and deliver results for our clients. Have a question or need consultation on a project?

Click here to contact us!

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