A dynamic pricing model for unifying programmatic guarantee and
real-time bidding in display advertising
release_2qjlukq7gze7hgnmfcszscm4fu
by
Bowei Chen and Shuai Yuan and Jun Wang
2015
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.
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