Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization

TL;DR

This paper introduces a constrained optimization model for bid recommendation in digital advertising, leveraging historical auction data to meet target CPA goals and improve campaign performance.

Contribution

It presents a novel bid optimization engine that solves a formalized constrained optimization problem using non-parametric learning from rich historical auction data.

Findings

Outperforms baseline methods on real-world campaigns

Effective in handling incomplete auction logs and bid behavior fluctuations

Achieves better control over CPA and improves revenue

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 one that can reach to target cost-per-acquisition (tCPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired tCPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem, which leverages the bid landscape model learned from rich historical auction data using non-parametric learning. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors, which essentially deals with the data challenges commonly faced by bid landscape modeling: incomplete logs in auctions, and uncertainty due to the variation and fluctuations in advertising bidding behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and has been applied into a wide range of scenarios for performance improvement and revenue liftup.