Abstract

In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity that can reach to cost-per-acquisition (CPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired CPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the constrained optimization problem. The proposed model can naturally recommend the bid that meets the advertisers’ expectations by making inference over history auction behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and can be applied into a wide range of scenarios for performance improvement and revenue liftup