Bidding Strategies on Adgroup and Keyword Levels in Search Engine Advertising: A Comparison Study

Abstract

This research aims to explore bidding strategies on two different levels (i.e., adgroup and keyword) in search engine advertising (SEA). With consideration of uncertainty in advertising performance, we build a stochastic bidding model that can be applied to adgroup and keyword levels. Then we develop an integrated strategy to seek out a feasible solution based on the tradeoff between the expected profit and advertiser's computational cost (or operational time). Using a panel dataset collected from field reports and logs of search advertising campaigns, we conduct computational experiments to evaluate the performance of our models. Experimental results show that 1) bidding on the keyword level leads to higher profit with higher variability, compared to that on the adgroup level; 2) the integrated strategy of optimal bidding can help advertisers obtain the highest profit under different constraints of computational costs; 3) for adgroups and keywords with better performance indexes, bidding prices are higher, and increase faster with the budget; 4) as the computational cost increases, the marginal profit initially increases sharply and then decreases after a certain point.