Lookalike modeling breathing new life into old channels

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Lookalike modeling isn’t new. It’s been a mainstay of the ad tech industry for years, used to help advertisers expand digital audiences while maintaining relevancy of targeting. The principle is simple. Brands want to attract new visitors to their site. What better way to do this than to identify prospects who resemble existing visitors (or customers)?

What is new is the dazzling variety of ways in which digital marketers are deploying lookalike modeling techniques to enhance the return on investment across marketing channels — both online and offline.

With more data than ever before on user journeys and behaviors, increased adoption of platforms (like customer data platforms and data management platforms) to centralize and analyze that data, and growing ubiquity of machine learning tools and techniques, lookalike modeling is breathing new life into old channels.

What is lookalike modeling?

Customer-centric businesses have long recognized that the best way to acquire new visitors is to focus on users who resemble their existing visitors (or better yet, high-value customers). For digital marketers looking to drive traffic and conversions, this long meant 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 18-29? Perfect — serve display advertisements to similar audiences elsewhere on the web!

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 18-29 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 (for example, a group of recent site visitors or high-value customers), 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.

Why does it matter?

Lookalike modeling is a trusty tool in the digital media arsenal — and it’s quickly becoming indispensable to other channels as well. The convergence of ad tech and CRM (customer relationship management) — powered by platforms that enable advertisers to go well beyond cookies and CRM professionals to gain visibility into the digital journeys of known users — has made it possible to build lookalike audiences of unprecedented sophistication.

[Read the full article on MarTech Today.]


Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.


About The Author

Jordan Elkind heads the product team at Custora, an advanced customer analytics platform for e-commerce retailers. Prior to joining Custora, he earned an MBA from Wharton and worked in marketing analytics at Citi Cards.


 

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