Publication Date: September 2021
Revision Date: June 2022
Lookalike targeting is a widely used model-based ad targeting approach that uses a seed database of individuals to identify matching “lookalikes” for targeted customer acquisition. An advertiser has to make two key choices: (1) who to seed on and (2) seed-match rank range. First, we ﬁnd that seeding on others’ journey stage can be eﬀective in new customer acquisition; despite the cold start nature of customer acquisition using Lookalike audiences, third parties can indeed identify factors unobserved to the advertiser that move individuals along the journey and can be correlated with the lookalikes. Further, while journey-based seeding adds no incremental value for brand marketing (click-through), seeding on more downstream stages improves performance marketing (donation) outcomes. Second, we evaluate audience expansion strategies by lowering match ranks between the seed and lookalikes to increase acquisition reach. The drop in eﬀectiveness with lower match rank range is much greater for performance marketing than for brand marketing. Performance marketers can alleviate the problem by making the ad targeting explicit, and thus increase perceived relevance; however, it has no incremental impact for higher match lookalikes. Increasing perceived targeting relevance makes acquisition cost comparable for both high and low match ranks.
Keywords: Digital advertising, Targeting, Algorithmic targeting, Lookalike targeting, Nonprofit marketingSee CFDP Version(s): CFDP 2302