One of the most efficient types of targeting in Facebook (second to retargeting, of course) is lookalike audience targeting.
Lookalike targeting is basically a targeting type that allows you to use first party data (e.g. customer lists, remarketing lists, etc.) as a seed audience to find new users with very similar characteristics, behaviors, and traits.
It’s applicable for all business models (B2B, B2C, EDU, etc.), but in this post, we’ll lock in on best practices for ecommerce.
When using lookalike targeting, the audience you select as your seed audience is extremely important. Essentially, you want to make sure you are developing audience lists from your best-performing audience.
For example, if you had an option to choose between people who signed up for a free trial of your product or people who actually purchased your product, you should start off with the audience that has actually purchased the product; these are the users driving revenue to your business, and you need to find more users like those.
Okay so now that we know we want to target our customers, are we ready to get started and build lookalike audiences? Nope! Hang tight, especially if you have a large customer base.
Step 1: Create smart segments
If you’ve got a ton of user data to pull from, use it to segment your base into groups with definable characteristics. This helps you tailor creative, messaging, and bidding with far more precision.
When it comes to ecommerce, there are three fundamentals ways you should look to segment your users:
- Average Order Value (AOV): Say you sell a variety of different products, from high-end/expensive items to affordable accessories. Segmenting out your 1st-party data by AOV allows you to better understand the audiences who can afford to go after your more expensive product vs. those who are looking for a bargain.
- Product Category: If you sell multiple different types of products, you’ll want to split out your customers by the product categories they have purchased (e.g. women’s handbags, women’s apparel, etc.). This allows us to better tailor content when running our ads.
- Lifetime Value (LTV): Segmenting audiences by LTV helps you create audiences of users who will most likely be repeat purchasers/loyal customers vs. those who will tend to just buy a few items for their more immediate Separating the two groups will allow you to bid accordingly as you see performance kick in.
Step 2: Build your lookalikes
So now it’s time to build out your lookalike audiences, which range from 1% – 10%. What this means is that the 1% are audiences closest in similarities, traits, and behaviors as your seed audience, and it contains only those users, which restricts scale. The 10% audience is the least similar – but it’s also the biggest pool.
The way you’ll want to set up your campaign is by building ad sets for each segment. So let’s say you chose to segment your first party data by AOV; your setup would look like this:
- Ad set 1: LAL 1% of AOV>300
- Ad set 2: LAL 1% of 300>AOV>100
- Ad set 3: LAL 1% of 100>AOV
Step 3: Customize the experience for each segment
So here is why segmenting out your audiences can be so strong – now we can truly tailor the creative messaging and user experience for each audience.
For example, for the high-AOV audience, we would want to show creative with more expensive products and customize messaging to promote high quality, etc. We would then want to send users to a landing page geared around these more expensive product types.
For the low-AOV audience, we would want creative showcasing more affordable products and would tailor messaging around discounts and deals.
Again, we want to ensure we aren’t sending these users to landing pages with expensive products, as they will bounce. So we choose the landing page destinations accordingly.
Lookalike targeting is a great way to make the most of Facebook’s thousands of data points on its users and get in front of the right core audience.
However, to truly make it successful for ecommerce, it’s critical to craft the right strategy, segment out your first party data smartly, and tailor your messaging, creative, and user experience accordingly.