Jacob Kamenir

Key Contact:

Jacob Kamenir
Senior Director of Business Development
Senior Director of Notice
Email Icon jkamenir@simpluris.com

Class action notice has significantly evolved over the last two decades. Courts have accepted that consumers are continuing to shift online and the need for digital notice campaigns has quickly evolved from a small niche to standard practice for many consumer class action settlements. This is especially true as time spent online continues to increase among all age groups. According to research conducted by MRI-Simmons, “97% of all adults are online and 85% of all adults use social media.” Therefore, it’s important to evaluate notice plans and the strategy behind them to ensure they are leveraging the latest technology and digital advertising best practices to reach class members.

Lookalike targeting uses machine learning to take a source audience and target new individuals that most closely resemble the source group. Machine learning focuses on the use of data and algorithms to imitate the way humans learn and analyze, and draws inferences from patterns in data. There are many characteristics a source group could share from behavior, demographics, and interests—lookalike targeting takes these all into account to build an additional, targeted audience for the notice ads. Using lookalike audiences alongside interest or demographic targeting can improve efficiency by prioritizing ads to potential class members most similar to the source group—which can be especially effective for large consumer classes where millions of consumers can be potential class members.

Many advertising platforms offer lookalike targeting, such as Facebook, Instagram, Twitter, LinkedIn, and programmatic advertising (website banners). There are a variety of ways to build a source audience for lookalike targeting, with the most common source audiences being class data and website activity. Class data matches unique identifiers such as names and email addresses to the profiles of users. Website activity uses tracking code placed on the settlement website to create a source audience based on previous website visitors.

Privacy is clearly a serious concern, so when using class data as a source audience, the data first goes through the well-established, secure process of “hashing”. Hashing is a type of cryptographic security method that irreversibly converts information into code that can only be used by the ad-servicing algorithm for this single, fixed purpose.

To use website activity to build a lookalike audience, direct notice or a general media campaign must have already generated some website visitors. A lookalike audience can then be created based on people who have visited the settlement website. For example, Meta, the company behind Facebook and Instagram, will use machine learning to analyze what similarities the website visitors have most in common, and will show notice ads to additional users on Facebook and Instagram who most closely share these similar characteristics.

Lookalike modeling has been widely adopted and approved by the courts for digital noticing and can be ran in conjunction with demographic, interest, and geotargeting to execute a comprehensive digital notice plan. Implementing these strategies contributes to a more defensible notice plan, which can minimize the possibility of having to re-notice the class and incur additional costs.

A notice expert can use lookalike targeting—and many other tools—to help you develop an effective, multilayered campaign to reach your class members. For more information or to schedule a consultation, please contact us.


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