Mining Revenue-Maximizing Bundling Configuration

With greater prevalence of social media, there is an increas-ing amount of user-generated data revealing consumer pref-erences for various products and services. Businesses seek to harness this wealth of data to improve their marketing strategies. Bundling, or selling two or more items for one price is a highly-practiced marketing strategy. In this pa-per, we address the bundle configuration problem from the data-driven perspective. Given a set of items in a seller’s in-ventory, we seek to determine which items should belong to which bundle so as to maximize the total revenue, by mining consumer preferences data. We show that this problem is NP-hard when bundles are allowed to contain more than two items. Therefore, we describe an optimal solution for bundle sizes up to two items, and propose two heuristic solutions for bundles of any larger size. We investigate the effective-ness and the efficiency of the proposed algorithms through experimentations on real-life rating-based preferences data

Distributed Signaling Games

A recurring theme in recent computer science literature is that proper design of signaling schemes is a crucial aspect of effective mechanisms aiming to optimize social welfare or revenue. One of the research endeavors of this line of work is understanding the algorithmic and computational complexity of designing efficient signaling schemes. In reality, however, information is typically not held by a central authority, but is distributed among multiple sources (third-party "mediators"), a fact that dramatically changes the strategic and combinatorial nature of the signaling problem, making it a game between information providers, as opposed to a traditional mechanism design problem. In this paper we introduce {\em distributed signaling games}, while using display advertising as a canonical example for introducing this foundational framework. A distributed signaling game may be a pure coordination game (i.e., a distributed optimization task), or a non-cooperative game. In the context of pure coordination games, we show a wide gap between the computational complexity of the centralized and distributed signaling problems. On the other hand, we show that if the information structure of each mediator is assumed to be "local", then there is an efficient algorithm that finds a near-optimal ($5$-approximation) distributed signaling scheme. In the context of non-cooperative games, the outcome generated by the mediators' signals may have different value to each (due to the auctioneer's desire to align the incentives of the mediators with his own by relative compensations). We design a mechanism for this problem via a novel application of Shapley's value, and show that it possesses some interesting properties, in particular, it always admits a pure Nash equilibrium, and it never decreases the revenue of the auctioneer

A cloud driven dynamic pricing system for retail companies

This project develops a dynamic pricing framework over a cloud based architecture, being scalable and highly configurable, considering the great cardinality of the solution in terms of the analytic models to build and apply. This architecture was defined using AWS and Terraform, ensuring an easy deployment agnostic to the client's infrastructure. The dynamic optimization of the prices is achieved by combining the training of a sales prediction model and the execution of a discount combination optimizer. The framework tries to be as general as possible in order to be easily adaptable to any given client. We provide general interfaces that can be reimplemented if the default implementations are not suitable for a given project. We performed simulations with data from a real client from the fashion retail sector, and the results obtained were promising, suggesting an improvement in the company's revenue

Two Essays in Competitive Price Formation in Auctions

In this work, I look at two competitive auction settings where a profit maximizing seller chooses auctions as a vehicle to sell to strategic bidders. In both essays, the auctioneer's problem is the selection of the optimal auction format. In the first essay, the auctioneer has a single item to sell while in the second essay, there are two items. In this work, I use game theoretic methods to derive the best course of action for the buyer and use this to arrive at the best course of action for the auctioneer. In essay 1, I consider a hybrid (between English outcry and second price sealed bid) auction format where at any point in time, the identity of the highest bidder and the second highest price is known to all. I show that this format would generate higher revenues than the English outcry format if the bidders' valuations are interdependent. This is because of lesser risk of overpayment and winner's curse for the bidders in the hybrid auction and consequently, they are better off bidding their valuations earlier. Such behavior results in a quicker convergence of the outstanding price to the final price realized as the bidders can update their valuations with certainty. I test this claim by comparing objects auctioned in Yahoo! and eBay as eBay follows the hybrid action format while Yahoo! follows the English outcry format and do find that with interdependent object valuations revenue from the hybrid auction format is higher.In the second essay, I consider an auctioneer who has two items to sell. These could be complements or substitutes or independent products. Given a pool of strategic bidders, I investigate whether he is better off auctioning the items sequentially or as a bundle. To do so, I first solve the bidders' optimization problem and use the solution to arrive at the implications for the seller. I find that with a moderate number of bidders (N>4), it is optimal to bundle strong complements only. On the other hand, I find that bundling is optimal when the number of bidders is less than four

Business Models for ASP Marketplaces

ASP (Application Server Provider) marketplaces provide a fundamental alternative to the classical business model of software licensing. At this point, it is still unclear why and when customers prefer the ASP model over more traditional approaches. To make ASP more attractive, more knowledge about possible pricing and product strategies is needed. In this paper we describe different business models for ASP marketplaces. We first compare the cost structures of the classical licensing model with the new server-based approach. Then we illustrate how price and product differentiation may improve overall market efficiency. In particular, we show that by selling different software versions for different prices, ASP marketplaces may obtain near-optimal revenues with products that are relatively inexpensive, disaggregated, and customizable. Consumers can thus choose between a wide variety of product lines to fit their differing budgets and requirements

An Antitrust Rule for Software Integration

What is the proper legal standard for product integration involving software? Because software is subject to low marginal costs, network effects, and rapid technological innovation, the Supreme Court\u27s existing antitrust rules on tying arrangements, which evolved from industries not possessing such characteristics, are inappropriate. In this Article, I ask why firms integrate software products. Next, I review the Supreme Court\u27s tying decisions in Jefferson Parish and Eastman Kodak. I propose an approach to judging the lawfulness of product integration in technologically dynamic markets that supplements the Supreme Court\u27s current standard with four additional steps in cases of tying of computer software. Thereafter, I examine the D.C. Circuit\u27s approach to software integration, which arose from that court\u27s 1998 interpretation, in Microsoft II, of an antitrust consent decree between the US. Department of Justice and Microsoft Corporation. I argue that the D.C. Circuit\u27s rule has general applicability and should be recognized as the appropriate standard for software integration under antitrust law. I show how my approach imparts greater clarity to the D.C. Circuit\u27s rule. I examine the competing product integration rule proposed in 2000 by Professor Lawrence Lessig as amicus curiae in the government\u27s subsequent antitrust case against Microsoft, concerning the integration of Internet Explorer and Windows 98. My approach enables Professor Lessig\u27s analysis to be reconciled with the D.C. Circuit\u27s rule, but Professor Lessig\u27s rule, on its own, would contain serious shortcomings. Thereafter, I evaluate Judge Thomas Penfield Jackson\u27s April 2000 findings of law on the integration of Internet Explorer and Windows 98. I conclude that Judge Jackson\u27s approach, in contrast to the D.C. Circuit\u27s rule as refined by my approach, would harm consumers in the technologically dynamic market for computer software