Proceedings ArticleDOI
Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach
Rainer Schlosser,Martin Boissier +1 more
- pp 705-714
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TLDR
Stochastic dynamic pricing models in competitive markets with multiple offer dimensions, such as price, quality, and rating are analyzed and it is demonstrated that the strategy is applicable even if the number of competitors is large and their strategies are unknown.Abstract:
Most online markets are characterized by competitive settings and limited demand information. Due to the complexity of such markets, efficient pricing strategies are hard to derive. We analyze stochastic dynamic pricing models in competitive markets with multiple offer dimensions, such as price, quality, and rating. In a first step, we use a simulated test market to study how sales probabilities are affected by specific customer behaviors and the strategic interaction of price reaction strategies. Further, we show how different state-of-the-art learning techniques can be used to estimate sales probabilities from partially observable market data. In a second step, we use a dynamic programming model to compute an effective pricing strategy which circumvents the curse of dimensionality. We demonstrate that the strategy is applicable even if the number of competitors is large and their strategies are unknown. We show that our heuristic can be tuned to smoothly balance profitability and speed of sales. Further, our approach is currently applied by a large seller on Amazon for the sale of used books. 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%.read more
Citations
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Journal ArticleDOI
Dynamic pricing under competition using reinforcement learning
TL;DR: This paper studies the performance of Deep Q-Networks and Soft Actor Critic in different market models, and shows that under certain conditions, RL algorithms can be forced into collusion by their competitors without direct communication.
Proceedings ArticleDOI
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
Ziqi Liu,Dong Wang,Qianyu Yu,Zhiqiang Zhang,Yue Shen,Jian Ma,Wenliang Zhong,Jinjie Gu,Jun Zhou,Shuang Yang,Yuan Qi +10 more
TL;DR: Wang et al. as mentioned in this paper presented a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing, which effectively models the correlations between the commercial objectives each merchant may achieve and the incentives under varying treatments.
Proceedings ArticleDOI
Data-Driven Inventory Management and Dynamic Pricing Competition on Online Marketplaces.
TL;DR: To study joint dynamic ordering and pricing competition on online marketplaces, an interactive simulation platform is built which deployed and compared different pricing and ordering strategies, from simple rule-based ones to highly sophisticated data-driven strategies which are based on state-of-the-art demand learning techniques and efficient dynamic optimization models.
Posted Content
Price Optimization in Fashion E-commerce.
TL;DR: A novel machine learning and optimization technique to find the optimal price point at an individual product level by using the concept of price elasticity of demand to get multiple demand values by varying the discount percentage.
Posted Content
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
Ziqi Liu,Dong Wang,Qianyu Yu,Zhiqiang Zhang,Yue Shen,Jian Ma,Wenliang Zhong,Jinjie Gu,Jun Zhou,Shuang Yang,Yuan Qi +10 more
TL;DR: This paper presents a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing and demonstrates the effectiveness of the proposed approach at Alipay.
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