By: |
K. Sudhir (Cowles Foundation and Yale School of Management);
Seung Yoon Lee (Yale School of Management);
Subroto Roy (Dept. of Marketing, University of New Haven) |
Abstract: |
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 marketing |
JEL: |
L31 M31 M37 C93 |
Date: |
2021–09 |
URL: |
http://d.repec.org/n?u=RePEc:cwl:cwldpp:2302r&r= |