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Risk-aware dynamic reserve prices of programmatic guarantee in display advertising

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conference contribution
posted on 2024-02-07, 18:37 authored by Bowei Chen

Display advertising is one important online advertising type where banner advertisements (shortly ad) on websites are usually measured by how many times they are viewed by online users. There are two major channels to sell ad views. They can be auctioned off in real time or be directly sold through guaranteed contracts in advance. The former is also known as real-time bidding (RTB), in which media buyers come to a common marketplace to compete for a single ad view and this inventory will be allocated to a buyer in milliseconds by an auction model. Unlike RTB, buying and selling guaranteed contracts are not usually programmatic but through private negotiations as advertisers would like to customise their requests and purchase ad views in bulk. In this paper, we propose a simple model that facilitates the automation of direct sales. In our model, a media seller puts future ad views on sale and receives buy requests sequentially over time until the future delivery period. The seller maintains a hidden yet dynamically changing reserve price in order to decide whether to accept a buy request or not. The future supply and demand are assumed to be well estimated and static, and the model's revenue management is using inventory control theory where each computed reverse price is based on the updated supply and demand, and the unsold future ad views will be auctioned off in RTB to the meet the unfulfilled demand. The model has several desirable properties. First, it is not limited to the demand arrival assumption. Second, it will not affect the current equilibrium between RTB and direct sales as there are no posted guaranteed prices. Third, the model uses the expected revenue from RTB as a lower bound for inventory control and we show that a publisher can receive expected total revenue greater than or equal to those from only RTB if she uses the computed dynamic reserves prices for direct sales.

History

School affiliated with

  • School of Computer Science (Research Outputs)

Publisher

IEEE

Date Submitted

2017-02-15

Date Accepted

2016-01-01

Date of First Publication

2016-12-12

Date of Final Publication

2016-12-12

Event Name

2016 IEEE The 16th International Conference on Data Mining Workshops

Event Dates

12 - 15 December 2016

Date Document First Uploaded

2017-02-14

ePrints ID

26416

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