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

Abstract

Traditionally, the study of online dynamic pricing and bundling strategies for information goods is motivated by the value-extracting or profit-generating potential of these strategies. Here we discuss the relatively overlooked potential of these strategies to online learn more about customer's 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 online 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 customer's 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, brokerage; recommender systems.

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