Examining the Impact of Contextual Ambiguity on Search Advertising Keyword Performance: A Topic Model Approach

In this paper, we explore how the contextual ambiguity of a search can affect a keyword’s performance. The context of consumer search is often unobserved and the prediction of it can be nontrivial. Consumer search contexts may vary even when consumers are searching for the same keyword. Due to the ambiguity of a keyword, a large portion of the ads displayed may fall outside a particular consumer’s interest, potentially leading to low click-through rates on search engines. In our study, we propose an automatic way of examining keyword contextual ambiguity based on probabilistic topic models from machine learning and computational linguistics. We quantify the effect of contextual ambiguity on keyword click-through performance using a hierarchical Bayesian model that allows for topic-specific effect and nonlinear position effect. We validate our study using a novel dataset from a major search engine that contains information on consumer click activities for 12,790 distinct keywords across multiple product categories from over 4.6 million impressions. We find that consumer click behaviors vary significant across keywords, and keyword category and the contextual ambiguity of the keywords significantly affect such variation. Specifically, higher contextual ambiguity can lead to a higher click-through rate (CTR) on top-positioned ads, but the CTR tends to decay faster with position. Therefore, the overall effect of contextual ambiguity on CTR varies across positions. Our study has the potential to help advertisers design keyword portfolios and bidding strategy by extracting contextual ambiguity and other semantic characteristics of keywords based on large-scale analytics from unstructured data. It can also help search engines improve the quality of displayed ads in response to a consumer search query.