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Split test in PPC and get more important data

Learn how to work with split tests in PPC campaigns to extract a wealth of information from search to improve performance and success.

There is no need to teach experienced PPC marketers about the import israel mobile database ance and significance of split testing. Each of them has experienced the simple path of testing – analysis – application and knows that different tests are a fundamental indicator of what works best for users. In this article, you will learn how to effectively conduct testing leading to increased campaign profits and return on investment.

What are split tests?

A/B split testing evaluates two variations of the same element, usually in eric davis chief operating officer / director of marketing and sales the form of a call to action (CTA) or text ad.

Example: In any e-shop, your goal is to end the shopping process with a purchase. And with A/B testing, you can test whether your decisions make customers click the Buy or Add to Cart button on the product tab . You then keep the text that works better on the button.

In addition to simply deploying two tracking options, you can also te thailand data st several things at once, such as how the layout of elements on the landing page works, etc. These techniques provide additional useful information, but they are already advanced, and before you get started with them, you should master basic A/B testing .

Why is differential testing important?

Even though you have collected a lot of campaign data and know exactly how your advertising account is working, the results of split tests may surprise you and show you new opportunities for improvement.

Unlike different preferences and assumptions, A/B testing is clear evidence of how your customers behave and what suits them better.

Another advantage is that you can first test any new strategy on a smaller scale before initiating larger changes.

What to test?

At the beginning of split testing, you decide what you want to test and why. Think about what you want to achieve from your tests and create a clear hypothesis that explains your goals. This will come in handy later when analyzing the results of your experiments.

Let’s say you have a simple factual text ad, but your hypothesis is that a conversational tone will work better. You assume that this will create a greater sense of trust and make the ad more user-friendly, leading to a higher click-through rate (CTR) .

When considering what to test, think about the problems you need to solve and select metrics that show poor campaign performance.

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