# Bid Optimization by Multivariable Control in Display Advertising

## TL;DR

This paper introduces a multivariable control approach for bid optimization in display advertising, specifically targeting conversion maximization under CPC constraints, and demonstrates its effectiveness through real-world data from Taobao.com.

## Contribution

It develops a feedback control-based multivariable system for bid optimization, transforming the problem into a linear programming framework and improving upon industry practices.

## Key findings

- The proposed method outperforms existing industry practices.
- Empirical validation shows significant improvements in conversion rates.
- The control system effectively manages KPI constraints in real-time bidding.

## Abstract

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.