Dynamics in Clickthrough and Conversion Probabilities of Paid Search Advertisements

Abstract

We develop a dynamic Bayesian model for clickthrough and conversion probabilities of paid search advertisements. These probabilities are subject to changes over time, due to e.g. changing consumer tastes or new product launches. Yet, there is little empirical research on these dynamics. Gaining insight into the dynamics is crucial for advertisers to develop effective search engine advertising (SEA) strategies. Our model deals with dynamic SEA environments for a large number of keywords: it allows for time-varying parameters, seasonality, data sparsity and position endogeneity. The model also discriminates between transitory and permanent dynamics. Especially for the latter case, dynamic SEA strategies are required for long-term profitability. We illustrate our model using a 2 year dataset of a Dutch laptop selling retailer. We find persistent time variation in clickthrough and conversion probabilities. The implications of our approach are threefold. First, advertisers can use it to obtain accurate daily estimates of clickthrough and conversion probabilities of individual ads to set bids and adjust text ads and landing pages. Second, advertisers can examine the extent of dynamics in their SEA environment, to determine how often their SEA strategy should be revised. Finally, advertisers can track ad performances to timely identify when keywords’ performances change.