A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising

TL;DR

This paper introduces a mathematical model that seamlessly combines programmatic guaranteed contracts with real-time bidding in display advertising, optimizing revenue through dynamic pricing and allocation strategies.

Contribution

It proposes a novel dynamic pricing and allocation model that integrates guaranteed contracts with RTB, considering advertiser risk aversion and market competition levels.

Findings

Optimal guaranteed prices are dynamic and non-decreasing over time.

Lower guaranteed prices in less competitive markets boost advance purchases.

Higher guaranteed prices in competitive markets increase guaranteed revenue.

Abstract

There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher's revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser's purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed contracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.