In sponsored search advertising, advertisers need to make a series of keyword
decisions. Among them, how to group these keywords to form several adgroups
within a campaign is a challenging task, due to 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 datasets collected from reports
and logs of search advertising campaigns. Experimental results illustrated that
our keywords grouping approach outperforms five baselines, and it can
approximately approach the optimum in a steady way. 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 in the situation with a large number of
keywords. Second, in keyword grouping decisions, the marginal profit does not
necessarily show the marginal diminishing phenomenon as the budget increases.
Such that, it's a worthy try for advertisers to increase their budget in
keywords grouping decisions, in order to obtain additional profit. Third, the
optimal keywords grouping solution is a result of multifaceted trade-off among
various advertising factors. In particular, assigning more keywords into
adgroups or having more budget won't certainly lead to higher profits. This
suggests a warning for advertisers that it's not wise to take the number of
keywords as the criterion for keywords grouping decisions.

本文提出了一种基于随机规划模型的关键词分组方法，考虑了点击率和转化率等随机变量，同时纳入预算约束和风险承受能力，使用分支界定算法解决该模型，实验证明该方法在实际的搜索广告活动中表现出色，并揭示了有关关键词分组决策的重要洞察。