Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes

TL;DR

This paper introduces a bid recommendation method in computational advertising that leverages concavity changes in click prediction curves to optimize ROI, demonstrating significant improvements in revenue, clicks, and ROI.

Contribution

It proposes a novel approach to bid recommendation by identifying concavity changes in click prediction curves and solving a constrained optimization problem for better ROI.

Findings

Significant revenue increase in real-world scenarios

Enhanced click volume and advertiser ROI

Effective identification of concavity turning points

Abstract

In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity changes in click prediction curves. The recommended bid is derived based on the turning point from significant increase (i.e. concave downward) to slow increase (convex upward). Parametric learning based method is applied by solving the corresponding constraint optimization problem. Empirical studies on real-world advertising scenarios clearly demonstrate the performance gains for business metrics (including revenue increase, click increase and advertiser ROI increase).