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

Abstract

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. Consumers arrive at search engines with diverse interests, and their search context may vary even when they are searching using the same keyword. 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 examine the effect of contextual ambiguity on keyword performance using a hierarchical Bayesian approach that allows for topic-specific effects and nonlinear position effects, and jointly models click-through rate (CTR) and ad position (rank). We validate our study using a novel data set from a major search engine that contains information on consumer click activities for 2,625 distinct keywords across multiple product categories from 10,000 impressions. We find that consumer click behavior varies significantly across keywords, and such variation can be partially explained by keyword category and the contextual ambiguity of keywords. Specifically, higher contextual ambiguity is associated with higher CTR on top-positioned ads, but also a faster decay in CTR with screen 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.