For the better part of a decade, lookalike audience targeting has been a go-to marketing tool for brands and organizations to expand their reach and sell more. After all, marketers have long used audience targeting to match their prospective client list with the behaviors and demographics of their best customers.
Even before digital marketing, being able to spot, track, and engage high-value audiences was a priority for businesses across the board, from car dealerships to beauty brands. However, now that AI-driven marketing tools are widely available, companies both large and small have access to the latest and greatest lookalike audience targeting technology.
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In 2013, Facebook launched what is widely regarded as the industry’s first lookalike audience marketing platform, tracking potential customers based on shared interests and behaviors with existing customers. Since then, many more platforms have integrated a similar service into their marketing capabilities.
There is evidence that lookalike audiences can sharply increase a company’s ability to outperform its competitors. McKinsey & Company found that incorporating behavioral insights into marketing efforts increased sales growth by 85 percent compared to similar companies without lookalike targeting. Using customer data in this way also helped these companies outperform the competition by more than 25 percent in gross margin.
Case in point: Lookalike audience targeting is one of the most powerful tools in ecommerce to separate your brand from the rest of the pack.
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Creating a lookalike audience for specific products and services requires research and insight into what makes your customers loyal to your business. No matter what your brand is all about, there are various tricks to keep in mind as you set out to capture new batches of engaged consumers.
Finding common ground between existing customers and new lookalike audiences requires the identification of lookalike audience seeds. Think of this as the collected behaviors, demographics, and interests, of your ideal target audience for a particular product or campaign.
Identifying audience seeds will help your team reach customers who would have previously been unknown to your company, in turn expanding the reach of your advertising and sales messaging.
Lookalike seeds should be specific enough to create a targeted audience type, showing consistent behaviors throughout its scope of potential customers. These seeds usually consist of anywhere between 1,000 to 50,000 users, the recommended audience seed size provided by Facebook.
There are a few different ways to collect the data necessary to build out these seeds — though it’s important to note that smaller companies may have more trouble defining their seeds due to a lack of customer data.
The first step you need to take when building out lookalike audiences is to identify the behavioral targeting segments most valuable to your company. Targeting segments are the bits and pieces used to create lookalike audiences. Each segment represents an audience that shares the same demographics and behaviors of a particular audience seed used to create your lookalike customer profiles.
For example, if you have two products that perform better with young and old audiences respectively, you can create two different behavioral targeting segments for each product. Maybe one of the segments connects behavioral buying triggers in the 18-35 age group while the other targets buyers in the 50+ age segment.
Creating segments using customer data is essential to engaging with customers who fit the lookalike audience profile you created when finding audience seeds. Wide-ranging segments may get your messaging to more customers but may have limited ROI compared to a smaller, more targeted segment. Finding the right balance between size and efficiency in your segments will largely depend on your own company goals.
Imagine this: Your company determines users on mobile devices show a much higher level of engagement than desktop browsers when offered a deal. At the same time, your executive team has prioritized reducing existing inventory in preparation for a new lineup of products.
By connecting the behaviors of your specific audience segments (mobile users vs. desktop users), you can now direct your marketing dollars toward the audience most likely to assist you in reaching your company objectives.
This is just one example of how connecting lookalike behaviors to your marketing priorities can help you reach a more engaged audience. Instead of spending more to reach all potential customers, you can use lookalike audience seeds to gain a higher ROI and nail your quarterly goals all at the same time.
Lookalike targeting isn’t just useful for finding new customers. It can also help inform retargeting campaigns if the initial push was unsuccessful or didn’t result in conversions from some customers. In truth, retargeting relies heavily on the same practices that allow lookalike audience segmentation.
A retargeted lookalike audience may consist of users who have previously abandoned their shopping carts or shown engagement or intent through another channel. Then, in the same way you would build a lookalike audience segment, you can divide those users by demographic, location, and behavior to further personalize retargeting ad campaigns.
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There are a number of platforms that support lookalike targeting. Facebook was one of the first, but its competitors sooned joined as the value of lookalike audiences became obvious.
Names, email addresses, phone numbers, location, age, and gender help define lookalike audiences based on Facebook’s user data. This is called a source audience — the data gathered here is then used to place dynamic ads in front of customers who exhibit similar behaviors.
The platform recommends you use segmented audience data (e.g., data gathered from your best customers versus your full customer pool), and your source audience must include at least 100 people from the same country to qualify. Up to 500 different lookalike audiences can be created from a single source audience.
With help from Google Analytics, you can create a “custom affinity audience” that targets users who would most likely be interested in your product or service. Google Ads can even target customers who visit your competitor’s homepage, placing your ads in front of engaged browsers who might not otherwise know about your company.
The platform’s dynamic prospecting service uses artificial intelligence to determine which customer behaviors are of the most value to your ad campaigns, even if they haven’t engaged with your ads. Customer Match can also help you connect with similar audiences through search, Gmail, Youtube, and the Google Display Network.
Adding to the company’s Matched Audience segmentation features, LinkedIn uses its own proprietary algorithm to create lookalike audiences up to 15 times larger than your original audience size. The platform leverages user data to create these segments, and each segment audience must contain at least 300 matched members to generate a pool of lookalike users.
It should be noted that LinkedIn does not segment audiences based on sensitive information such as gender or age on its platform.
Keeping track of the numerous lookalike audience segments on all of the platforms your company uses is a full-time job. That’s why it’s recommended to invest in an intelligent digital ad platform to keep track of your customer data and audience segments in one location.
Through AdRoll, you can level up your lookalike audience targeting using pixel data and audience segments to match your ideal customer behaviors and demographics to its database of 1.2 billion digital profiles.
AdRoll audience targeting connects to millions of websites and mobile apps like AOL, Google, and Yahoo, including an integration with Shopify, allowing you to match your own ecommerce data to Shopify customers — that’s exactly what you need to get your brand on the road to higher conversion rates, improved marketing ROI, and a new crop of engaged consumers.
Last updated on July 15th, 2024.