Bundling and pricing for information brokerage: customer satisfaction as a means to profit optimization

Traditionally, the study of on-line dynamic pricing and bundling strategies for information goods is motivated by the value-extracting or profit-generating potential of these strategies. In this paper we discuss the relatively overlooked potential of these strategies to on-line learn more about customers' preferences. Based on this enhanced customer knowledge an information broker can-- by tailoring the brokerage services more to the demand of the various customer groups-- persuade customers to engage in repeated transactions (i.e., generate customer lock-in). To illustrate the discussion, we show by means of a basic consumer model how, with the use of on-line dynamic bundling and pricing algorithms, customer lock-in can occur. The lock-in occurs because the algorithms can both find appropriate prices and (from the customers' perspective) the most interesting bundles. In the conducted computer experiments we use an advanced genetic algorithm with a niching method to learn the most interesting bundles efficiently and effectively

Editorial

When we look at successful sales processes occurring in practice, we find they combine two techniques which have been studied separately in the literature. Recommender systems are used to suggest additional products or accessories to include in the bundle under consideration, and multi-issue negotiation focuses on optimizing the precise configuration of the bundle and its price. In this paper, we pursue the automation of such interactive sales processes. 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, learnt by the shop in interactions with previous customers, with current data about the ongoing negotiation process with the current customer. We present a memory- and a model-based method for online learning customer preferences and discuss their pros and cons. The performance of our system is illustrated using extensive computer experiments involving simulated customers with highly non-linear preferences which the system has no trouble learning

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

Multi-attribute bilateral bargaining in a one-to-many setting

Negotiations are an important way of reaching agreements between selfish autonomous agents. In this paper we focus on one-to-many bargaining within the context of agent-mediated electronic commerce. We consider an approach where a seller negotiates over multiple interdependent attributes with many buyers individually. Bargaining is conducted in a bilateral fashion, using an alternating-offers protocol. In such a one-to-many setting, “fairness,” which corresponds to the notion of envy-freeness in auctions, may be an important business constraint. For the case of virtually unlimited supply (such as information goods), we present a number of one-to-many bargaining strategies for the seller, which take into account the fairness constraint, and consider multiple attributes simultaneously. We compare the performance of the bargaining strategies using an evolutionary simulation, especially for the case of impatient buyers and small premature bargaining break off probability. Several of the developed strategies are able to extract almost all the surplus; they utilize the fact that the setting is one-to-many, even though bargaining occurs in a bilateral fashion

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

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

An algorithm for on-line price discrimination

The combination of on-line dynamic pricing with price discrimination can be very beneficial for firms operating on the Internet. We therefore develop an on-line dynamic pricing algorithm that can adjust the price schedule for a good or service on behalf of a firm. This algorithm (a multi-variable derivative follower with adaptive step-sizes) is able to respond very quickly to changes in customers' demand. An additional advantage of the developed algorithm is that it does not require information about individual customers. Given the growing concern about customers' privacy this can be of great practical importance. Computational experiments (with different customer behavior models) indicate that our algorithm is able to successfully exploit the potential benefits of on-line price discrimination

Improving patient activity schedules by multi-agent Pareto appointment exchanging

We present a dynamic and distributed approach to the hospital patient scheduling problem: the multi-agent Pareto-improvement appointment exchanging algorithm, MPAEX. It respects the decentralization of scheduling authorities and is capable of continuously adjusting the different patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of th

A Fast Method for Learning Non-linear Preferences Online Using Anonymous Negotiation Data

In this paper, we consider the problem of a shop agent negotiating bilaterally with many customers about a bundle of goods or services together with a price. To facilitate the shop agent’s search for mutually beneficial alternative bundles, we develop a method for online learning customers’ preferences, while respecting their privacy. By introducing additional parameters, we represent customers’ highly nonlinear preferences as a linear model. We develop a method for learning the underlying stochastic process of these parameters online. As the conducted computer experiments show, the developed method has a number of advantages: it scales well, the acquired knowledge is robust towards changes in the shop’s pricing strategy, and it performs well even if customers behave strategically