Manogna Nadella

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. In this paper, we have proposed 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 developed a framework to threshold the number of postal codes/domains suggested and validated the results.