A New Approach to Demand Modelling and Optimisation in Pricing Decision Support Systems

Pricing decision support systems have been developed in order to help retail companies optimise their prices. This paper aims to enhance the essential forecasting and optimisation techniques that underlie these systems. This is first done by applying the method of Dynamic Linear Models in order to provide more accurate forecasts compared with current models. We also present a new pricing optimisation technique which provides the highest guaranteed profit when a competitor's price can't be forecasted. Existing optimisers assume that a competitor never reacts to a change in own price but this assumption can't be trusted since competitor prices are never forecasted. This new approach is applied to the forecasting and optimisation of daily retail vehicle fuel pricing using real commercial data, showing its improved results in both cases.