Negotiating over Bundles and Prices Using Aggregate Knowledge

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers.Comment: 15 pages, 7 eps figures, Springer llncs documentclass. Extended version of the paper published in "E-Commerce and Web Technologies," Kurt Bauknecht, Martin Bichler and Birgit Pr\"{o}ll (eds.). Springer Lecture Notes in Computer Science, Volume 3182, Berlin: Springer, p. 218--22

Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have_nonlinear_ preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.Comment: 10 pages, 5 eps figures, ACM Proceedings documentclass, Published in "Proc. 6th Int'l Conf. on Electronic Commerce ICEC04, Delft, The Netherlands," M. Janssen, H. Sol, R. Wagenaar (eds.). ACM Pres

Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have emph{nonlinear} preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmar