  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.
