Optimal Keywords Grouping In Sponsored Search Advertising Under Uncertain Environments

In sponsored search advertising, advertisers need to make a series of keyword decisions. Grouping these keywords to form several adgroups within a campaign is a challenging task because of the highly uncertain environment of search advertising. This paper proposes a stochastic programming model for keywords grouping, taking click-through rate and conversion rate as random variables, with consideration of budget constraints and advertisers' risk-tolerance. A branch-and-bound algorithm is developed to solve our model. Furthermore, we conduct computational experiments to evaluate the effectiveness of our model and solution, with two real-world data sets collected from reports and logs of search advertising campaigns. Experimental results illustrated that our keywords grouping approach outperforms five baselines, and it can approximately and steadily approach the optimal solution. This research generates several interesting findings that illuminate critical managerial insights for advertisers in sponsored search advertising. First, keywords grouping does matter for advertisers, especially with a large number of keywords. Second, in keywords grouping decisions, the marginal profit does not necessarily show the marginal diminishing phenomenon as the budget increases. Therefore, advertisers should try to increase their budget in keywords grouping decisions to garner additional profit. Third, the optimal keywords grouping solution is the result of a multifaceted trade-off among various advertising factors. In particular, assigning more keywords into adgroups or having a larger budget will not definitely lead to higher profits. This study suggests a warning for advertisers: It is not wise to use the number of keywords as a single criterion for keywords grouping decisions.