  Most sales applications are characterized by competition and limited demand
information. For successful pricing strategies, frequent price adjustments as
well as anticipation of market dynamics are crucial. Both effects are
challenging as competitive markets are complex and computations of optimized
pricing adjustments can be time-consuming. We analyze stochastic dynamic
pricing models under oligopoly competition for the sale of perishable goods. To
circumvent the curse of dimensionality, we propose a heuristic approach to
efficiently compute price adjustments. To demonstrate our strategy's
applicability even if the number of competitors is large and their strategies
are unknown, we consider different competitive settings in which competitors
frequently and strategically adjust their prices. For all settings, we verify
that our heuristic strategy yields promising results. We compare the
performance of our heuristic against upper bounds, which are obtained by
optimal strategies that take advantage of perfect price anticipations. We find
that price adjustment frequencies can have a larger impact on expected profits
than price anticipations. Finally, our approach has been applied on Amazon for
the sale of used books. We have used a seller's historical market data to
calibrate our model. Sales results show that our data-driven strategy
outperforms the rule-based strategy of an experienced seller by a profit
increase of more than 20%.
