Automatically Mapping Ad Targeting Criteria between Online Ad Platforms

Abstract

Targeting criteria in online advertising differ across platforms and frequently change. Because advertisers are increasingly taking a multi-channel approach to online marketing, there is a need to automatically map the targeting criteria between ad platforms. In this research, we test two algorithmic approaches  Word2Vec and WordNet  for mapping ad targeting criteria between Google Ads and Facebook Ads. The results show that Word2Vec outperforms WordNet in finding matches (97.5% vs. 63.6%), covering different criteria (20.0% vs. 13.5%), and having higher similarity scores. However, WordNet outperforms Word2Vec in expert evaluation (Mean Opinion Score = 3.05 vs. 2.46), implying that algorithmic performance metrics may not correlate with expert ratings. Overall, due to specific requirements for mapping ad targeting criteria, automatic means do not (at least yet) offer a satisfactory solution for replacing human judgment.