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

As marketers, we got used to letting social media marketing platforms (and Facebook specifically, a.k.a. Meta) do our work with us.

We let these platforms follow the client journey from our ads completely to conversion. We let them watch. We let them learn and we allow algorithm optimize and target the correct audience.

The algorithm did everything. It had been convenient.

At the beginning, Facebook used to talk about that information around and we’re able to learn simultaneously because the algorithm learned. We was previously in a position to analyze our audience, our followers, what they liked, what age these were, what gender, marital status, how many other websites they visited, and how many other pages they followed. We knew just as much as the algorithm did.

But that information was no more available. Yet we didnt care as the algorithm was doing its thing and we were consistently getting amazing results. So we got comfortable, too comfortable.

Fast forward to April 2021 and the iOS 14.5 release

The planet for marketers using Meta imploded a little.

For a few, it imploded a whole lot.

Users needed to be asked for permission to be tracked across apps and websites and 95% of these didn’t give such permission in the U.S. (84% worldwide).

Since that time, social media marketing platforms experienced terrible visibility into what’s happening to individuals who select an ad. After they leave Meta that’s just about it!

Meta did some work to supply estimates. However in my experience things such as squeeze page arrivals as well as conversion attributions are definately not the true numbers (because of Google Analytics and UTMs for the backup tracking ability).

Interest-based targeting is among the few tools we’ve left.

Therefore the theory would be to feed the funnel with cold leads at the brand awareness stage so they flow through the funnel and convert without barriers.

There’s one problem: because algorithms still have trouble determining positive interaction from negative interaction and, for example, they will have trouble understanding context engagement and interest with a specific brand might not mean that they would like to be approached by that brand.

Interest-based marketing is a great starting place but misses the mark often.

Researchers analyzed the accuracy of Facebook activity on the interest-based ads and discovered that almost 30% of interests Facebook listed weren’t real interests. Which means that when your ad is founded on the set of interests, you can skip the mark about 30% of that time period.

This study may be the to begin its kind and contains a comparatively small dataset, but considering comments and the engagement generated in interest-based ads I’ve run, I start to see the biggest percentage of confused and unhappy comments with this ad set, so NC State is onto something here.

In the event that you got to this aspect of this article, you may be re-thinking your daily life choices as a paid social media marketing marketer.

However, there’s something still very helpful in the platforms:

Lookalike audiences

Facebook might not have just as much information regarding your converters since it did before, nevertheless, you or your clients do!

Rather than feeding this theoretical funnel to cold audiences, lets visit the end of the funnel and discover people just like the converters.

The procedure is similar in every platforms:

  • Get your seed set of converters.
  • Develop a custom audience with this particular list by uploading it to your social media marketing platform of preference.
  • The platform will match the info from what they find out about each individual in the platform (mostly email or contact number).
  • You can find minimum matches necessary for this list to be valid and each platform has its rules because of this.
  • After the custom audience is established and valid we are able to generate a lookalike audience where we tell the platform find people who have similar profiles to individuals with this list.

By creating lookalike audiences we have been taking the funnel and tipping it ugly. We start in the bottom and generate a listing of cold audiences so much like our current converters they could be almost considered warm audiences.

We have been now utilizing the social media marketing platforms to greatly help us create personas predicated on data we realize is accurate and targeting them.

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

Why?

As you aren’t targeting one interest, one element, which will be irrelevant 30% of that time period. You’re targeting several elements, interests or platform behaviors. That substantially reduces inaccuracy.

After doing A/B tests between interest-based audiences and lookalike audiences I could tell that I’ve had results improve around 40% for a few lookalike audiences. Sometimes the outcomes are no more than 15% but I’ll take any improvements and efficiency I could get when optimizing my ads.

Wouldnt this give an excessive amount of control back again to the algorithms?

Are we setting ourselves up for exactly the same scenario we’d pre-iOS 14.5 by letting algorithms run our paid media? It depends.

  • There exists a little trust we have been giving back again to the algorithms, however now we know never to put our eggs in a single basket. We realize that interests identified by Facebook remain 60-70% accurate, so knowing your audiences interest is quite valid, even though we skip the mark a bit.
  • Audiences shift, their interests change, and we ought to be moving using them. Is it possible to tell me your audience looks exactly the same now since it did in 2019? My recommendation is by using lookalike audiences normally as you possibly can but complement them with interest-based ads and continuously A/B test their efficiency.

Think about your campaign objective

Sometimes lookalike audiences are proficient at converting but might not be nearly as good at engagement.

In a single A/B test I run, the interest based audience had 30% more expensive per click however the rate of positive engagement was double. This audience wasnt converting, these were spreading the message.

We not merely need audiences that follow the funnel way to conversion effectively, sometimes we also need audiences that cheer us on and help us spread awareness.

Please contemplate this before using lookalikes

A lookalike audience is founded on a custom list (seed list), which list should only be made up of data you possess and also have permission to utilize.

Check each platforms policies regarding custom lists to comprehend this better.

Keep your lists and online privacy policy updated

If people unsubscribe from your own communications, have an idea to update your lookalike audiences.

If people usually do not desire to hear from you, then why can you desire to advertise to somebody with exactly the same profile?

Remember: Platforms change as time passes, so we should evolve using them to remain relevant and sometimes which means heading back to basics. All the best on the market.

Watch: Using lookalike audiences to reverse the marketing funnel and generate quality leads

Below may be the complete video of my SMX Advanced presentation.


Opinions expressed in this post are those of the guest author rather than necessarily INTERNET SEARCH ENGINE Land. Staff authors are listed here.


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    CONCERNING THE Author

    Naira Perez has been around marketing for nearly 20 years. She’s caused clients from several industries and Fortune 500 brands. She got her begin in direct response advertising, building brands on TV, radio and print before digital was a good thing. In 2016, she founded SpringHill, which specialized in the development and implementation of digital marketing strategies like paid media, integrated campaign design and identifying audience patterns. In 2021, she joined the Portland Trail Blazers as Sr. Digital Marketing Manager to greatly help grow their innovative and expanding digital marketing department.


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