In this paper we describe a new class of systems called intelligent tactical decision support systems which enable firms to make superior pricing decisions within a dynamic competitive environment. The paper outlines the unique aspects of such systems in relation to commercially available systems. These aspects are seen to be their ability to process and use knowledge on the one hand and information external to the organization on the other. The systems use nonlinear models, optimization, and learning algorithms to provide decision support capabilities for the generic price-setting problem. The general theme of the paper is to see the systems described within this paper as the support tools for a generic price setting process. The description of the systems start with a generic one for pricing decision support in any consumer market and is then specialized to various markets each of which could benefit from a specific variant of the generic pricing technology which has been adapted to that particular industry. The adaptations from the generic pricing system to the systems for petrol pricing, retail pricing, and telecommunications taxation, have been explained. These three examples show a variety of price-setting situations ranging from near commodity pricing, as in the case of petrol, to pricing of very sophisticated services, as in the case of mobile telephony. Field experiments with these systems show that they perform significantly better than unassisted human decision-makers. This is seen from controlled experiments where these systems have demonstrated significant profit uplift when applied to the experimentation sites as compared to the "control" sites where the systems have not been applied.
