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At present, mixed-revenue about the combination with subscription fee revenue and advertising revenue is the main mode of revenue for online content providers. How to balance the two aspects of revenue to get the optimal revenue is the focus of this study. This article considers consumers' price sensitive level and advertisement sensitive level. Based on these concepts, we build a more realtistic...
The special cost structure makes pricing information goods be a difficult task. How to estimate the reservation price of information goods is the main barrier of optimal pricing. This paper presented a reservation price estimation method based on fuzzy cognitive map, and then an optimal pricing model is built on the basis of multi-segment's reservation price.
Knowledge of customer reservation price aids manager in implementing many marketing strategies such as dynamic pricing, bundling sales, and target promotion etc. This paper investigated how cognition factors affected different customer segment's reservation price distribution. Utility of information products usually can not be quantitatively measured like that of physical products, and contrarily,...
Information technology has given e-retailers new capability of learning demand in real time. This paper investigates how to integrate this real time learning technology with Q-learning algorithm for the optimization of dynamic pricing in e-retailing setting. Especially, this paper studies the optimal dynamic pricing problem for seasonal and style products in e-retailing setting, and validate our approach...
In this paper, we considered a dynamic pricing problem for selling a given stock of perishable items during a finite sale season. We developed a partially observed Markov decision process model to study this problem. In particularly, belief states were adopted to deal with the uncertainty of demand. A Q-learning approach was designed to solve the problem of obtaining optimal dynamic pricing policy,...
In this paper, we investigate the use of Q-learning approach to the problem of determining dynamic prices for multi-products in an e-retailing setting. In particularly, this article is concerned with the representation and generalization of large state spaces in Q-learning problems. We proposed a Q-learning model that is based on the self-organizing map, and validate our approach in simulated test.
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