Tackling Cannibalization Problems for Online Advertisement

Abstract

Market cannibalization is inevitable when there are two or more competing marketing approaches to the same customer base. The cannibalization problem has been discussed in the context of search advertising of individual advertisers, whereas in this paper we discuss the problem that advertising platform companies face in dealing with multiple advertisers. In online advertising, they must properly serve ads with varying mass appeal to users with various interests. For them, it is important to maximize the value of the ads for advertisers and also for the platform. To do so, they deploy user models to serve ads. However, shortsighted models could lead to a decrease in overall performance in an attempt to improve certain ads' performance while slightly impairing the rest. We consider this phenomenon from the perspective of cannibalization and confirm the existence of a cannibalization problem in optimizing the delivery of ads in minor categories. To resolve this problem, we propose new methods, apply them to an ad delivery system, and conduct an A/B test. Our methods overcame the cannibalization problem and increased revenue by + 0.6% compared with the baseline method.