# Smart Pacing for Effective Online Ad Campaign Optimization

arXiv: https://arxiv.org/abs/1506.05851 · HTML: https://mlsanity.com/paper/1506.05851/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2015.

## Built on by (most cited first, 14 of 14)

- [Bidding Agent Design in the LinkedIn Ad Marketplace](https://mlsanity.com/paper/2202.12472/) (2022)
- [A framework for massive scale personalized promotion](https://mlsanity.com/paper/2108.12100/) (2021)
- [Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions](https://mlsanity.com/paper/2207.14798/) (2022)
- [Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2309.02669/) (2023)
- [Dynamic Budget Throttling in Repeated Second-Price Auctions](https://mlsanity.com/paper/2207.04690/) (2022)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [Functional Optimization Reinforcement Learning for Real-Time Bidding](https://mlsanity.com/paper/2206.13939/) (2022)
- [Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint](https://mlsanity.com/paper/2402.03388/) (2024)
- [Feedback Control for Small Budget Pacing](https://mlsanity.com/paper/2509.25429/) (2025)
- [A Lightweight MPC Bidding Framework for Brand Auction Ads](https://mlsanity.com/paper/2603.07721/) (2026)
- [Constrained user-item allocation for e-commerce marketing campaigns](https://mlsanity.com/paper/2606.09623/) (2026)
- [DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation](https://mlsanity.com/paper/2606.14192/) (2026)
- [Less Traffic, Better Outcomes: Competition-Aware Request Dispatch in Real-Time Ad Exchanges](https://mlsanity.com/paper/2608.03705/) (2026)
- [When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions](https://mlsanity.com/paper/2609.33312/) (2026)

## Abstract (arXiv)

In targeted online advertising, advertisers look for maximizing campaign performance under delivery constraint within budget schedule. Most of the advertisers typically prefer to impose the delivery constraint to spend budget smoothly over the time in order to reach a wider range of audiences and have a sustainable impact. Since lots of impressions are traded through public auctions for online advertising today, the liquidity makes price elasticity and bid landscape between demand and supply change quite dynamically. Therefore, it is challenging to perform smooth pacing control and maximize campaign performance simultaneously. In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals. The implementation of the proposed approach in a real DSP system is also presented. Experimental evaluations on both real online ad campaigns and offline simulations show that our approach can effectively improve campaign performance and achieve delivery goals.