One of the most efficient types of targeting in Facebook (2nd to retargeting, of course!) is lookalike audience targeting. Lookalike targeting is basically a targeting type in Facebook that allows you to use 1st-party data (e.g. customer lists, remarketing lists, etc.) as a seed audience and target users very similar in characteristics, behaviors, and traits.

In this post, we’ll help get you started in using Lookalike Audiences. We’ll start with how to choose your seed audience, then move into how to segment that audience, then close with how to structure your lookalike ad sets.

Choose Your Seed Audience

When leveraging lookalike targeting, the audience you select as your seed audience is extremely important. Essentially, you want to make sure you are developing audience lists off of your best performing audience. For example, you could choose between people who signed up for a free trial of your product and people who actually purchased your product. You should start off with the audience that has actually purchased the product – because these are the users driving revenue to your business and we want to find more users similar to our customers.

Okay, so now we know we want to target our customers. Are we ready to get started and build LAL audiences off of it? Nope! Hang tight!

Segment Your Seed Audience

If you have a large customer base, we don’t want to necessarily just dump that in and create LALs off of it. We should segment that base out into groups with definable characteristics. For example: segment out your customer base by High LTV (lifetime value), mid LTV, and low LTV. You could also segment by product categories (for example, if you are an electronics store, you may want to segment out your customer list by TVs, Laptops, etc.).

Structure Your Lookalike Ad Sets

So now it’s time to build out your lookalike audiences. First off, you should know that LALs range from 1% – 10%. What this means is that the 1% are audiences closest in similarities, traits, and behaviors as your seed audience. The 10% is farthest away in similarities as your seed audience, but it also represents a far bigger audience.

So as you launch on LALs, you want to start off with a 1% and move to higher percentages as you reach efficiency and need to scale. Each ad set should represent one seed audience LAL. So for the high, mid, low LTV example, you would start off with 3 ad sets.

 

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  1. High LTV – LAL 1%
  2. Mid LTV – LAL 1%
  3. Low LTV – LAL 1%

Now let’s say you need to scale and want to move onto targeting LAL of 2%. Now you will create 3 more ad sets targeting the 2%.

  1. High LTV – LAL 2%
  2. Mid LTV – LAL 2%
  3. Low LTV – LAL 2%

While you work your way through the percentages, you want to make sure you implement the following nesting strategy:

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As you scale to the next level of LALs, it is important to exclude the prior LAL from your targeting to completely separate the audiences. So back to our example, in the LAL 2% ad sets you also want to make sure that you exclude your LAL 1%. This ensures separation of our audience and removes overlap. This is important because it helps us truly understand the performance of each audience type, which allows us to optimize bids accurately.

LAL targeting is a great way to make the most of Facebook’s incredible wealth of data and get your ads in front of the right core audience. By implementing the right strategies, you can make the most of this targeting type to not only develop brand awareness but achieve some powerful direct-response success along the way.

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Sana Ansari
Sana Ansari, General Manager of 3Q Accelerate , has worked in digital marketing since 2009, with stints at QuinStreet and Accenture preceding her tenure at 3Q. Sana has worked with a range of clients, from SMBs to enterprise accounts, helping companies make exponential revenue gains and driving profitable spend in verticals including insurance, travel, and eCommerce. Sana's expertise in SEM, the Google Display Network, landing page optimization, copy and creative optimization, remarketing, and driving lead quality has been fueled by a data-centric methodology reinforced in all of her team members. In her time at 3Q, she has been responsible for driving some of the agency's greatest success stories, taking companies with limited budgets and big ideas and turning them into names familiar across the country.