You Ask, I Answer: What Makes Effective Facebook Ads?

In today's episode, Jen asks, how can brands find out which kind of Facebook ads work best for them? Interesting question. The way that I think you would tackle this problem, or at least one way that you could tackle this problem was with large-scale data analysis. The Facebook API does allow some limited extraction of data, and there are certainly plenty of services, competitive social media monitoring services, uh Facebook data services. Uh one example is Facebook's crowd tangle service that allow you to extract uh large amounts of information that's publicly facing, publicly available, including advertising, and then do some analysis on it.

So one approach you could take would be to go to one of these services, put in uh your company's uh Facebook page, put in a list of all the major competitors you have in your space. Maybe some companies that have functionally similar business models to you. Uh so for example, if you're a coffee shop, you might put in like tea shops and pizza shops and things like that. And extract out all the Facebook posts, paid and unpaid, that these companies have have uh run in the last you know however long, and then sort it, look at uh which of the uh the uh pieces of content that were paid, and then assess what worked, what resonated. Now, with this technique, you won't get every single ad because there are certainly you know there's so many different types, but you will get thematically the types of messaging and imagery and copy and timing and audience sizes uh for what's working best in that sector.

It may be, you know, five or ten percent of all of the content available uh for your industry, but that's enough to give you a sample that looks at, okay, these are the things that seem to work. Uh maybe it's images of a certain type or even a color palette, maybe it's a day of the week or an hour of the day. When you have that large-scale data uh set, you can look at what is in the top five or ten or twenty percent of the data and say, okay, what got engagement? What got people interested? Is it and are those things unique?

Now here's the challenge. The data is only uh semi-ready to analyze. There'll be some things that you can obviously look at right away, engagement types, you know, likes, comments, shares, the different reactions. Um you'll be able to get URLs to the various images, but then you're gonna have to spend a fair amount of time as uh a human or a team of humans manually appending some of the information. So you'll need to uh, for example, look at the imagery on the post, and then maybe in this uh think of it as a spreadsheet, you'd have to add columns for like what types of of images are in there.

And you would have to be somewhat descriptive, like, you know, people, cars, coffee cups, um silly clip art, drawings, whatever the image type is, you would need to manually note that in the spreadsheet. You would also need to append because you won't get the text of the comments, uh general uh themes in comments if people have left comments at all. And uh for those comments you would need to append and say, like this is you know generally positive, generally negative, uh things like that. That manual augmentation of the data is essential in order to make this process work because there is a lot to a Facebook ad that is not immediately visible to a machine. Right?

Again, this thematically understand what are the themes of the images, uh, particularly if you're looking at images across different uh pages. Again, using the coffee shop example, if you have Starbucks and Dunkin' Donuts and things like that, they may have their own sort of visual palette that is unique to their brand that you would not be able to replicate. Uh you have to use your own uh a design palette to do that. But the ability for you to at least get a head start with the the the raw data itself and especially the engagement data is where you're gonna get a lot out of of value out of this procedure. Now again, this is not every ad type.

This is uh gonna be mainly things like sponsored posts and stuff, but it's a good starting point because if you can't get any traction at all on you know a sponsored post where the engagement rates are so terrible, um, then you you know that whatever ad strategies are currently being out there uh used out there may not necessarily be all that effective. There are other uh tools that can pull in some fa some uh social media advertising data as well. Uh I haven't used them in a while, so uh I know back in the day I believe uh SEMrush did that. Um but you can look at uh comparable performance of uh Google ads is also to see uh from a messaging perspective are there common themes. Tools like uh SEMrush and spy foo, uh RFs uh all do the have the ability to extract out that type of data.

And one of the things you could test is does a a ad copy theme, title, etc. that works on Google ads also work on Facebook. If you're are are they similar audiences or different audiences? Uh one way to tell this uh for your own brand page is to look at your Google Analytics uh demographics data, look at your Facebook audience insights demographics data, and if there's a wide disparity on basic things like age and gender, then you know that you don't have the same audience, and what works in say one platform may not work on the other. On the other hand, if there's substantial overlap between the two audiences, there's a good chance that if something's working for you or a competitor in your Google ads, then it may also have applicability in your Facebook ads.

So there are a lot of ways to attack this problem with data to try and determine what are the things that could work or should work, and build a testing plan. That's the important thing is the next step in this process is not just willingly nearly stuff start copying things. You want to build an actual testing plan. Uh that is uh an A B test where you have A would be the ads you would have run anyway, and B would be these new ads that you have uh designed based on the data you found. And you run them in parallel, same audiences, same budget spend, same time frame, uh, etc.

to see which ad set works better. When you do that, you'll have a uh sense uh over a fairly long period of time uh about whether your data-driven approach is a better approach than the normal creative that you would have done uh otherwise. Depending on the skill of your creative team, uh and depending on the the uh the themes and the data you get out of from your competitors, you may not find an advantage. You may find that the data-driven approach works worse because your competitors suck. Um and you're drawing on data that they've produced.

Uh so be aware of that possibility. It just because you're using data does not guarantee a better result. On the other hand, if you have a creative team like me that can barely put together stick figure art, uh, the data-driven approach probably is going to work better for you. Uh, because you'll be able to pick up on themes and use uh your reasonable uh commercially available clip art and stuff to make better stuff than your incompetent cre uh creative team uh was putting together. Again, referring to myself here.

So that's the approach. Do the data analysis, identify the the common themes, build a testing plan, run the testing plan, and see which performs better. With the understanding that the data you find may not be all that high quality. Uh if you have follow-up questions on this, please leave them in the comments box below. Subscribe to the YouTube channel on the newsletter, and I'll talk to you soon.

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