An Interactive Platform to Simulate Dynamic Pricing Competition on Online Marketplaces

Serth, Sebastian; Podlesny, Nikolai J.; Bornstein, Marvin; Lindemann, Jan; Latt, Johanna; Selke, Jan; Schlößer, Rainer; Boissier, Martin; Uflacker, Matthias

doi:10.1109/edoc.2017.17

Cited by 12 publications

(1 citation statement)

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“…As the evaluation of complex competing pricing strategies is also not analytically tractable, it is impossible to give performance guarantees of a specific strategy -even if the underlying customer behavior as well as the competing strategies are known. Hence, simulation studies have to be used to compare the performance of dynamic pricing strategies and to analyze their strategic interaction in different oligopoly settings, see, e.g., Kephart et al (2000) or Serth et al (2017).…”

Section: Motivationmentioning

confidence: 99%

Dealing with the dimensionality curse in dynamic pricing competition: Using frequent repricing to compensate imperfect market anticipations

Schlößer

Boissier

2018

Computers & Operations Research

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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%.

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Section: Motivationmentioning

confidence: 99%