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AI Driven Audience Expansion by Recommending Lookalike Postal Codes and Domains

Author(s): Manogna Nadella, Nitin Vinayak Agrawal


Digital advertising heavily relies upon cookie-based solutions to identify and target the prospective audience. With third party cookies being deprecated in the near future, advertisers will have to look at alternate strategies such as geo contextual for targeting. This paper proposes cookie-less solutions for audience expansion by suggesting look-alike postal codes (for geo and audience targeting) and domains (for contextual targeting). These solutions could be used as a part of pre-campaign planning and use customer affinity and geo-contextual datasets along with a universal ad feed as historical learning. We have used deep autoencoders and self-organizing maps for low dimensional latent space representation and probabilistic methods for arriving at insights. A penalized similarity function is applied to this latent space to suggest look-alike postal codes and domains. To overcome the challenge of estimating how good our unsupervised learning models are, we also have come up with a framework to threshold the number of postal codes/domains suggested and validate the results.