Using lookalike audiences to reverse your marketing funnel and generate quality leads

As marketers, we’ve gotten used to letting social media platforms (especially Facebook, aka Meta) do our work for us.

We allow these platforms to follow the customer journey from our ads to conversion. We let them watch. We let them learn and the algorithm optimizes and targets the right audience.

The algorithm did everything. It was comfortable and easy.

At the very beginning, Facebook shared this data with us and we were able to learn at the same time as the algorithm. We used to be able to analyze our audience, our followers, what they liked, what age they were, what gender, marital status, what other websites they visited and what other pages they followed. We knew as much as the algorithm knew.

But then this information was no longer available. But we didn’t care because the algorithm was doing its job and we were getting amazing results. We were so comfortable, too comfortable.

Fast forward to April 2021 and the release of iOS 14.5

The world of marketers using Meta has fallen apart a bit.

He imploded a lot to some.

Users had to be asked for permission to be tracked through apps and websites and 95% of them are decided not to issue such a license in the US (84% worldwide).

Since then, social media platforms have had a terrifying insight into what happens to people who click on an ad. Once they leave Meta, that’s pretty much it!

Meta has done some work to provide ratings. But in my experience, things like landing page arrivals or even conversion attribution are far from real numbers (thanks to Google Analytics and UTM for backup tracking).

Interest-based targeting is one of the few tools we have left.

So the theory is to fill the flow with cold leads at the stage of brand recognition so that they flow through the funnel and convert without obstacles.

There’s one problem: because algorithms still have trouble distinguishing positive interaction from negative interaction, and thus have trouble understanding context—engagement and interest in a particular brand may not mean they want to approach that brand.

Interest-based marketing is a good starting point, but it often misses the mark.

Researchers analyzed the accuracy of Facebook’s activity on their interest-based ads and found that nearly 30% of the interests reported by Facebook were not real interests. This means that if your ad is based on an interest list, you could miss the target about 30% of the time.

This study is the first of its kind and has a relatively small data set, but when I look at the comments and engagement generated on the interest-based ads I’ve shown, I see the highest percentage of confused and displeased comments on this set of ads, so NC State is at something here.

If you’ve made it to this point in the article, you may be rethinking your life choices as a paid social media marketer.

However, there is still something very useful about the platforms:

A similar audience

Facebook may not have as much information about your converters as it used to, but you do – or your customers do!

Instead of feeding this theoretical funnel to cold audiences, let’s go to the end of the funnel and find people like converters.

The process is similar on all platforms:

• Get an initial list of converters.
• Create a custom audience with this list by uploading it to your social media platform of choice.
• The platform will compare the data to what they know about each person on the platform (most commonly email or phone number).
• A minimum match is required for this list to be valid, and each platform has its own rules for this.
• Once a custom audience is created and valid, we can create a lookalike audience where we tell the platform to “find people with similar profiles” to the people on that list.

By creating lookalike audiences, we take the funnel and turn it on its head. We start at the bottom and create a list of cold audiences that are so similar to our current converts that they could almost be considered warm audiences.

We now use social media platforms to help us create personas based on data we know to be accurate and then target them.

Platforms know a lot about our behavior within the platform. They’re not perfect, but these platform-generated personas are far more accurate than assumed interests.

Why?

Because you’re not targeting one interest, one element, that 30% of the time will be irrelevant. You are targeting a group of platform elements, interests, or behaviors. This significantly reduces inaccuracy.

After doing A/B tests between interest-based audiences and lookalike audiences, I can say that results have improved by up to 40% for some lookalike audiences. Sometimes the results are only 15%, but I will use all the improvements and efficiencies I can get to optimize my ads.

Wouldn’t that give too much control back to the algorithms?

Are we setting ourselves up for the same scenario we had before iOS 14.5 by letting algorithms drive our paid media? Yes and no.

• We are restoring some trust in algorithms, but now we know not to put all our eggs in one basket. We know that interests identified by Facebook are still 60-70% accurate, so knowing what your audience is interested in is very important, even if we miss the mark a bit.
• Audiences change, their interests change, and we should move with them. Can you tell me that your audience looks the same now as it did in 2019? My recommendation is to use lookalike audiences as often as possible, but supplement them with interest-based ads and constantly A/B test their effectiveness.

Consider the goal of your campaign

Sometimes a lookalike audience is good at conversion, but maybe not so good at engagement.

In one A/B split test I ran, interest-based targeting had a 30% higher CPC, but double the engagement rate. This audience was not pretending, it was spreading the message.

Not only do we need an audience that effectively follows the funnel path to conversion, sometimes we also need an audience that cheers us on and helps us spread awareness.

Please consider this before using similes

A lookalike audience is based on a custom list (seed list), and this list should only be created with data that you own and have permission to use.

Please check each platform’s policies regarding custom listings to understand this better.

Update your listings and privacy policy

If people unsubscribe from your messages, have a plan to update your similar audiences.

If people don’t want to hear from you, then why would you want to advertise to someone with the same profile?

Remember: platforms change over time, so we need to evolve with them to stay relevant, and sometimes that means going back to the basics. Good luck out there.

See: Using Lookalike Audiences to Turn Your Marketing Funnel and Generate Quality Leads

Below is the full video of my SMX Advanced presentation.

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The opinions expressed in this article are those of the guest author and not necessarily those of Search Engine Land. Staff authors are cited here.

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