Monetization of High-Technology Products and Services




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This dissertation consists of three main chapters that exploit techniques from the mechanism design literature to address monetization challenges that arise in technology markets. Chapter 2 focuses on the monetization of impressions (i.e., opportunities to display ads) that arrive at mobile ad-exchanges and are sold to advertisers in real time through an auction mechanism. The traditional mechanism selects a single advertiser whose ad is displayed over the entire duration of an impression, i.e., throughout the user’s visit. We argue that such a mechanism leads to an allocative inefficiency, as displaying only the winning ad throughout the lifetime of an impression precludes the exchange from exploiting the opportunity to obtain additional revenue from advertisers whose willingness-to-pay becomes higher during the lifetime of that impression. Our goal in this chapter is to address this efficiency loss by offering mechanisms in which multiple ads can be displayed sequentially over the lifetime of the impression. We consider two plausible settings – one, where each auction is individually rational for the advertisers and the other, where advertisers are better off relative to the traditional mechanism over the long run – and derive an optimal (i.e., revenue-maximizing for the ad-exchange) mechanism for each setting. To efficiently compute the payment rule, the optimal mechanism for the former setting uses randomized payments. Under this mechanism, while the ad-exchange always benefits relative to the traditional mechanism, the advertisers could either gain or lose – we demonstrate both these possibilities. The optimal mechanism for the latter setting is a “mutually-beneficial” mechanism in that it guarantees a win-win for both the parties relative to the traditional mechanism, over the long run. Happily, for both the mechanisms, the allocation of ads and the payments from the advertisers are efficiently computable, thereby making them amenable to real-time bidding. In Chapter 3, we study monetization challenges that arise in data markets. A buyer interested in purchasing a dataset has private valuations in two aspects – her ideal record that she values the most, and the rate at which her valuation for the records in the dataset decays as they differ from her ideal record. The seller allows individual buyers to filter the dataset and select the records that are of interest to them. The multi-dimensional private information of the buyers coupled with the endogenous selection of records makes the seller’s problem of optimally pricing the dataset a challenging one. We formulate a tractable model and successfully exploit its special structure to obtain optimal and near-optimal data-selling mechanisms. Specifically, we provide insights into the conditions under which a commonly used mechanism – namely, a price-quantity schedule – is optimal for the data-seller. When the conditions leading to the optimality of a price-quantity schedule do not hold, we show that the optimal price-quantity schedule offers an attractive worst-case guarantee relative to an optimal mechanism. We continue the study of data monetization in Chapter 4 and explore near-optimal mechanisms. In particular, we obtain an approximation scheme for pricing datasets that can guarantee a revenue which is arbitrarily close to the optimal revenue. We also demonstrate how the data-seller can exploit buyers’ preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.



High technology industries, Mechanical movements, Machine design, Mechanical efficiency


©2020 Sameer Mehta. All rights reserved.