Best Practices for Testing, Segmenting and Optimizing Facebook Ads
Facebook isn’t just a gathering place, it’s become an increasingly popular marketplace and haven for all things consumer-related. While this makes it a great space for advertisers, it also makes it a very competitive marketplace.
What’s necessary for Facebook advertisers to understand is that great attention to detail is required to find the audience you’re looking for as well as to enagage them to want to find out more about your products. The following three tips outline strategies for refining your levels of engagement and ensuring that your message is reaching the right audience and standing out among the crowd.
Multivariate ad tests
Online marketers have been using A/B tests since the very beginning, and it’s standard practice at this point. What we know about advertising is that it’s the audience who decides which of your ads are the best. You may find yourself surprised to learn that an ad or landing page, or combination of the two, performed better than you might have expected, but at the end of the day, the most important person in this equation is the consumer.
By employing multivariate testing techniques, you’re getting a lot more out of ad testing. For example, you can create multiple versions of your ads in which you test the headlines, images, offers, prices, colors, arrangement of the forms and other aspects. By creating different versions of these ads, you can drill down deep to find out which combinations and which of these aspects are most successful.
One thing that’s necessary to get good data from tests of this nature is traffic. With very little traffic, it’s difficult to ascertain the results of these tests, especially if you’re using many variables.
A method to employ in this case is full-factorial multivariate testing, in which you test three ads with three combinations of elements. For a Facebook ad those elements might be the headline, text and image. This gives you nine combinations from which to distribute daily budget that, in the end, gives you a full set of data from which you can get the highest-performing element from each variation that you can then use to create your best ad from each of these variables.