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. brokerage; recommender systems.