Look-alike modeling has been an important part of the media toolkit over the past decade, allowing brands to increase their audience pool by taking a core group of top-performing individuals, grouping them and using data and technology to find other individuals like them. Over the past several years, data management platforms (DMPs), third-party cookies and their associated data are becoming obsolete due to self-regulation by technology providers and legislation like CCPA and GDPR. The movement away from third-party cookies and third-party data overlays on cookies is causing total audience pools to drop in size as individuals have fewer associated identifiers (cookies to connect to). However, look-alike modeling can also help businesses leverage their first-party data to build robust large-scale segments for marketing and advertising purposes. Tealium's regional vice president of strategic partnerships for the Americas, Travis Cameron, explained that the value of being able to expand target populations based on data associated with a high-value segment will take on a different dimension.

Look-Alike modeling is one of the most popular methods for expanding the size of ad segments to provide advertisers increased reach. Facebook introduced Look Alike modeling to its platform in 2013 and several ad-tech providers offer a version of look-alike modeling natively within their products. However, as we will show in this post, Look Alike models in practice often result in brittle and inaccurate segments. A variety of other machine learning (ML) approaches -- including classification and uplift -- almost always yield superior performance compared to Look Alike models. Because of these limitations, we believe Look Alikes should be used sparingly, only when other techniques are unavailable.