Audiences are one of the key variables for a Facebook campaign to be successful.
It’s not enough to have a great product, and it’s not even enough to have a great product AND make great content for it. If you’re not targeting the right audience, you might as well be selling ugly, cheap stuff. Say you sell luxury watches of big-name brands, and you pay the best designer in town to make your creatives. If your ads are seen by 16 year old girls, the odds of even one of them buying are virtually 0.
Finding the right audience can be challenging for a number of reasons: the many different kinds of audiences there are, the overlap between audiences, the frequency with which a user from a certain audience should see your ad before he buys, the saturation of audiences, etc. To make things more complicated, when you go to your Facebook dashboard, the most you can do is see metrics at the adset level. However, normally, each adset has many audiences, so this doesn’t really tell you how each audience is behaving.
Let's take one step back. There are 3 types of audiences:
- Custom audiences. These audiences can be created based on many things: an email list you have from subscribers (that FB will match to users profiles and serve them ads), from the Facebook pixel based on users’ behavior in your site (like people who visited X page, or clicked Y button, or added to cart); or even from people's interests ('rock music', 'diving') or behaviors they have ('recently married', 'diving instructor')
- Lookalike audience. These audiences are based on custom audiences. Facebook creates audiences of profiles similar to the ones the base-audience contains. You just choose how similar they have to be, from a range of 1% to 20% similarity. The idea is: ‘of all Facebook users, find me the 3% most similar profiles to the ones of the people in my custom audience’. The smaller the number, the most similar profiles will be, and the smaller the audience.
- Behavior/interest audience. For this audience, you choose an interest (like ‘people interested in travel’) or a behavior (‘frequent travelers’), and create an audience based on one or more of those behaviors/interests. You don’t need data from the Facebook pixel, nor a mailing list, which is why this is the go-to type of audience when you don’t have lots of data from your business, when you are opening up a new product, or simply when you are feeling daring/have a little bit of budget left and want to explore new things.
All these different types, as they come from very different places, will naturally have different behaviours. As for audience frequency and saturation, depending on the audiences and products you have, there are different frequencies that work. It’s more likely that for a big or expensive product, people will have to look at you ads several times before convincing themselves to buy, but for very sensational gadgets, only two or even one glance is enough. Just like when targeting a campaign to people who have already given you their email, they’ll need a different message and different number of interactions than people who have no idea who you are and what you sell.
We built this tool to give you a better visibility on how every one of your audiences has performed in the past, for you to know what to expect in the future, so you can make adjustments accordingly.
Among other things, you can see the 3 best and worst performing adsets for each audience, along with different metrics on how they performed, and the differences between audiences targeted to a specific gender or device.
Say you see ‘Audience A’ which has an outstanding performance for the top 3 adsets, but a terrible performance for the 3 bottom adsets. You see the audience has better numbers for women on mobile, while questionable costs and response from males or in desktop.
You do a bit more digging and check the 3 top adsets. Turns out all 3 had discounts or some kind of promotion. With this whole picture, you now know that this specific audience should be used to target women, on mobile, and only when you have an active promotion.
In the example at the top, you can see that the third audience, ‘ALL_90’, was used for 3 different demographic sections: 2 different age groups, and the possible gender separations. It’s easy to see that when targeted to females, the results are much better - 4 times better! Of course, there are other things you should look at before driving conclusions, like how much money you have spent on each one. If you spent 10x more in the female targeting, then it’s not actually 4x better.
Please note that all the data in this section is based only on data from the past. This section is based purely on data mining, and no predictions are made. It can be possible for an audience that performed very poorly in the past, to show excellent results by changing the demographics or the ad content, as well as it is possible for an audience which has performed great to burn out.