Publication Date: September 2021
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 assess if and how seeding by others’ journey stages impact clickthrough (upstream behavior desirable for brand marketing) and donation (downstream behavior desirable in performance marketing). Overall, we ﬁnd that lookalike targeting using other’s journeys can be eﬀective-third parties can indeed identify factors unobserved to the advertiser merely from others’ journey stage to improve targeting. Further, while it is suﬀicient to seed on upstream journey stages for brand marketing, seeding on more downstream stages improves performance marketing outcomes. Second, we assess the eﬀectiveness of expanding the target audience with lower match ranks between seed and lookalikes. The drop in eﬀectiveness with lower match rank range is much greater for performance marketing (donation) than for brand marketing (click-through). However, performance marketers can alleviate the reduction in ad eﬀectiveness for low match ranks by making targeting more salient; but increasing salience has little impact for high match rank. Overall, by increasing salience, performance marketers can make acquisition cost comparable for high and low match ranks.
Keywords: Digital advertising, Targeting, Algorithmic targeting, Lookalike targeting, Nonprofit marketing