With more data than ever before on a customer's journey, increased adoption of platforms (like customer data platforms and data management platforms) to centralize and analyze that data, and growing ubiquity of ML tools and techniques, lookalike modeling is breathing new life into old channels. Customer-centric businesses have long recognized that the best way to acquire new visitors is to focus on users who resemble their existing customers (or better yet, high-value customers). For digital marketers looking to drive traffic and conversions, this means identifying and purchasing media against audiences based on a small number of static demographic attributes. Your recent site visitors are statistically more likely to be females, aged 29-35? Perfect—serve display advertisements to similar audiences elsewhere on the web.

Lookalike modeling is a trusty tool in the digital marketer's arsenal—and it’s quickly becoming indispensable to other channels as well. The convergence of ad tech and CRM has made it possible to build lookalike audiences of unprecedented sophistication. AI and machine learning can add even more sophistication to the process, including contextual, geo-location, social, and perhaps even emotional data.

With a single source of customer data spanning online and offline engagement, a brand can unify disparate signals of purchase intent from many customer touch points, including onsite and transaction behavior, email engagement, offline purchases, app usage, call center contacts, product reviews and more. This provides a rich and highly accurate view of the customer. 

The problem is that demographic segment-based targeting, while enabling advertisers to reach audiences at scale, isn’t a great proxy for relevancy. Women aged 29-35 are a diverse demographic, only a subset of whom are likely to be interested in a brand’s offering. As a result, performance can tend to show a steep drop-off as audience size increases. Enter lookalike modeling, a form of statistical analysis that uses machine learning to process vast amounts of data and seek out hidden patterns across pools of users. Lookalike modeling works by identifying the composition and characteristics of a ‘seed’ audience, and identifying other users who show similar attributes or behaviors. By analyzing not just demographic but behavioral similarities—e.g., users who have demonstrated similar browsing patterns—lookalike modeling enables advertisers to leverage powerful and complex data signals to find the perfect audience.