While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect advertisers' purchase between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers' purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both guaranteed contracts and auctions can increase the publisher's expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.
 We consider the problem of pricing guaranteed contracts in online display advertising. This problem has two key characteristics that when taken together distinguish it from related offline and online pricing problems: (1) the guaranteed contracts are sold months in advance, and at various points in time, and (2) the inventory that is sold to guaranteed contracts - user visits - is very high-dimensional, having hundreds of possible attributes, and advertisers can potentially buy any of the very large number (many trillions) of combinations of these attributes. Consequently, traditional pricing methods such as real-time or combinatorial auctions, or optimization-based pricing based on self- and cross-elasticities are not directly applicable to this problem. We hence propose a new pricing method, whereby the price of a guaranteed contract is computed based on the prices of the individual user visits that the contract is expected to get. The price of each individual user visit is in turn computed using historical sales prices that are negotiated between a sales person and an advertiser, and we propose two different variants in this context. Our evaluation using real guaranteed contracts shows that the proposed pricing method is accurate in the sense that it can effectively predict the prices of other (out-of-sample) historical contracts. A new advertisement option that allows an advertiser to pay a fixed CPM CPC to purchase impressions or clicks. The fixed payment can be different to the underlying ad format.The proposed option can be priced under the lattice framework for both SV and GBM underlying models.The studied model is validated by two advertising datasets. Advertisement (abbreviated ad) options are a recent development in online advertising. Simply, an ad option is a first look contract in which a publisher or search engine grants an advertiser a right but not obligation to enter into transactions to purchase impressions or clicks from a specific ad slot at a pre-specified price on a specific delivery date. Such a structure provides advertisers with more flexibility of their guaranteed deliveries. The valuation of ad options is an important topic and previous studies on ad options pricing have been mostly restricted to the situations where the underlying prices follow a geometric Brownian motion (GBM). This assumption is reasonable for sponsored search; however, some studies have also indicated that it is not valid for display advertising. In this paper, we address this issue by employing a stochastic volatility (SV) model and discuss a lattice framework to approximate the proposed SV model in option pricing. Our developments are validated by experiments with real advertising data: (i) we find that the SV model has a better fitness over the GBM model; (ii) we validate the proposed lattice model via two sequential Monte Carlo simulation methods; (iii) we demonstrate that advertisers are able to flexibly manage their guaranteed deliveries by using the proposed options, and publishers can have an increased revenue when some of their inventories are sold via ad options.  Many online advertising slots are sold through bidding mechanisms by publishers and search engines. Highly affected by the dual force of supply and demand, the prices of advertising slots vary significantly over time. This then influences the businesses whose major revenues are driven by online advertising, particularly for publishers and search engines. To address the problem, we propose to sell the future advertising slots via option contracts (also called ad options). The ad option can give its buyer the right to buy the future advertising slots at a prefixed price. The pricing model of ad options is developed in order to reduce the volatility of the income of publishers or search engines. Our experimental results confirm the validity of ad options and the embedded risk management mechanisms. Display advertising is a $25 billion business with a promising upward revenue trend. In this paper, we consider an online display advertising setting in which a web publisher posts display ads on its website and charges based on the cost-per-click (CPC) pricing scheme while promising to deliver a certain number of clicks to the ads posted. The publisher is faced with uncertain demand for advertising slots and uncertain traffic to its website as well as uncertain click behavior of visitors. We formulate the problem as a novel queueing system, where the slots correspond to service channels with the service rate of each server inversely related to the number of active servers. We obtain the closed-form solution for the steady-state probabilities of the number of ads in the publisher's system. We determine the publisher's optimal price to charge per click and show that it can increase in the number of advertising slots and the number of promised clicks. We show that the common heuristic used by many web publishers to convert between the cost-per-click and cost-per-impression pricing schemes using the so-called click-through-rate can be misleading as it may incur web publishers substantial revenue loss. We provide an alternative explanation for the phenomenon observed by several publishers that the click-through-rate tends to drop when they switch from the cost-per-click to cost-per-impression pricing scheme.