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Negotiating over Bundles and Prices Using Aggregate Knowledge

Listed author(s):
  • Koye Somefun
  • Tomas Klos

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 design a system for a shop, to negotiate 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. Our system 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. Both sources of data are used to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the system suggests the most promising of those alternatives and, depending on the customer's response, continues negotiating about the alternative bundle, or proposes another alternative. Extensive computer simulations show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, compared to a benchmark. Additionally, our system's performance is shown to be robust to a variety of changes in customers' negotiation strategies

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 218.

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Date of creation: 11 Aug 2004
Handle: RePEc:sce:scecf4:218
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