This All-Too-Common Mistake Could Kill a Facebook Ads Campaign. Here’s How to Fix It.

Split testing, also called A/B testing, is a perennial staple of marketing strategy, and for a very good reason. It's one of the best ways to get a handle on what works and what doesn't.

On Facebook, implementing A/B testing can go a long way toward helping you continually refine your paid advertisements — and as a result, your costs will drop, sometimes dramatically.

There are quite a few places you can use this technique — images, headlines, the content itself — but one of the biggest ones is your audience.

You have to ask yourself: am I targeting the right people?

Targeting the wrong audience with your ads can absolutely tank your entire campaign.

This is fundamental.

Before you start working on headlines, content, imagery, and other aspects of the ads, you need to make sure the right people are going to see them.

Doing so can be easier said than done. Facebook offers tons of audience targeting options. This is great, but at the same time, it can also be overwhelming sometimes.

All of your Facebook advertising efforts are in vain if you're showing your ads to the wrong people — people who simply aren't going to convert.

In a recent blog post from AdEspresso, they go over the details of how to create and implement a split testing strategy to refine your Facebook audience targeting.

Audience

Deciding on the campaign type is a strategic decision. The most important non-strategic decision is your audience, which includes two things: who your audience is and how you find them on Facebook.

The latter is what seem to cause the most problems for people and, in my experience, this often happens because they target an audience that they think is their audience when it actually isn’t. I believe Facebook doesn’t deliberately make it as tricky as it is, it rather tries to offer as many targeting options as possible so you can do more cool stuff. They do make it a bit complicated to figure out, but at least that gives us an opportunity to help out 😉

Finding your audience is more important that testing various images, text and so on because we need to find the right people before we figure out what’s best to show them. If we try to A/B test which image works best, but we show it to the wrong people, then what do we learn? – That, that image doesn’t work on those people! Yeah, right, but… they were the wrong audience so, at the end f the day, we don’t learn anything really. So, first find the right people, then the right image and so on.

I usually start out by running five ad sets within a campaign, with a different target audience in each. A target audience could be as simple as a fan-page, interest, behavior or something similar.

[image source: AdEspresso]

The purpose is to figure out which of the audiences is the best to move forward with. The best practice is to test just one interest or behavior per adset (or only one interest per lookalike audience, if you’re doing that) so that you’re able to determine which is better.

If you group several interests together (bulk-interest adset), you won’t know which one performed better and which ones didn’t.

[image source: AdEspresso]

Another thing is that if you group several interests together and you proceed to test images, you may experience that one interest gets you more traffic on the first image and another interest got more traffic on the second one. Hence your test won’t be accurate.

One scenario where I like to use bulk-interest adset is if you are targeting employees at small companies as the traffic is so little that it’s a probably a waste of time to set up a lot of campaigns. I usually set the bar at 1000 people in my audience.

Action step: Pick the 5 interests (or whatever way you choose to target) you think are most relevant to your target audience and set up a campaign with 5 single-interest adsets.

Continue with the best performing audience; we’ll save the rest for later.

If you are following this guide step-by-step, you might benefit from reading the bottom sections about reporting and statistical significance to an idea of how much traffic is enough to decide on your result.

Read more at AdEspresso.