# Optimized Cost per Click in Taobao Display Advertising

**Authors:** Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, Kun Gai

arXiv: 1703.02091 · 2019-01-30

## TL;DR

This paper introduces an optimized CPC bidding strategy for Taobao's display advertising system that dynamically adjusts bids to better match traffic quality, improving revenue and user experience.

## Contribution

The paper presents a novel bid optimization method called OCPC that enhances traffic matching and overall platform performance in real-world online advertising.

## Key findings

- Significantly improved advertising revenue in production tests.
- Enhanced matching precision between bids and traffic quality.
- Better user experience and traffic allocation efficiency.

## Abstract

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.

## Full text

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## Figures

16 figures with captions in the complete paper: https://tomesphere.com/paper/1703.02091/full.md

## References

27 references — full list in the complete paper: https://tomesphere.com/paper/1703.02091/full.md

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Source: https://tomesphere.com/paper/1703.02091