Lookalike audience

A lookalike audience is a group of social network members who are determined as sharing characteristics with another group of members. In the digital advertising age, it refers to a new targeting tool for digital marketing, first initiated by Facebook, which helps to reach potential customers online who are likely to share similar interests and behaviors with existing customers. Since Facebook debuted this feature in 2013, additional advertising platforms have followed suit, including Google Ads, Outbrain, Taboola, LinkedIn Ads and others.
Considerations.
Lookalike audiences anatomize existing customers and their user profiles to find the commonalities between the existing audience. This helps to find highly-qualified customers who previously would have been difficult to identify and reach. This expands the potential audience in different countries and applies to new differentiated audience segments; This approach saves time and lowers advertising costs for the acquisition of a new audience.
In order to be effective, a lookalike audience seed needs to be homogeneous. This is commonly achieved using a consistent behavioral pattern. The homogeneity of the lookalike seed has a greater influence on the audience's effectiveness than the size of this sample group. In Facebook, the minimal lookalike seed size is 100 users from the same country. Facebook generally recommends creating a seed from an audience of 1,000 to 50,000 users.
Lookalike audiences might have limited effects on small companies or startups because of the small sample size of their existing audience, which would inevitably lead to insufficient data drawn from the current audience and interference from outliers. Namely, there would be no high bounce rate with these companies' websites.
Examples of seeds.
Marketers use many data sources to create lookalike seeds. Some examples of eCommerce lookalike seeds include:
Methodology.
Facebook, as an example, takes three steps to build a lookalike audience:
Debate.
One study has shown that the tool of lookalike audiences, to some degrees, does well in generally advertising results. It is also listed as an important trend of pay-per-click (PPC) by Delhi School of Internet Marketing. However, debates over such a third party behavioral targeting being used for digital marketing hasn't stopped either, because using the data of customers is against online privacy settings.
In 2019, limitations were put in place by Facebook to stop discriminatory targeting of audiences according to zip code, income levels and demographics (age and gender).