Predicting price-tag for customized goods

We discuss a dynamic pricing model which will aid automobile manufacturer in choosing the right price for customer segment. Though there is oligopoly market structure, the customers get "locked" into a particular technology/company which virtually makes the situation akin to a monopoly. There are associated network externalities and positive feedback. The key idea in monopoly pricing lies in extracting the customer surplus by exploiting the respective elasticities of demand. We present a Walrasian general equilibrium approach to determine the segment price. We compare the prices obtained from optimization model with that from Walrasian dynamics. The results are encouraging and can serve as a critical factor in Customer Relationship Management (CRM) and thereby effectively manage the lock-in.

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

Patent Price Dynamics in the Context of Patent Age and Patent Latent Variables

The decision to patent a technology is a difficult one to make for the top management of any organization. The expected value that the patent might deliver in the market is an important factor that impacts this judgement. Earlier researchers have suggested that patent prices are better indicators of value of a patent and that auction prices are the best way of determining value. However, the lack of public data on pricing has prevented research on understanding the dynamics of patent pricing. Our paper uses singleton patent auction price data of Ocean Tomo LLC to study the prices of patents. We describe price characteristics of these patents. The price of these patents was correlated with their age, and a significant correlation was found. A price - age matrix was developed and we describe the price characteristics of patents using four quadrants of the matrix, namely young and old patents with low and high prices. We also found that patents owned by small firms get transacted more often and inventor owned patents attracted a better price than assignee owned patents.

Dynamics between patent latent variables and patent price

This paper focuses on studying the relationship between patent latent variables and patent price. From the existing literature, seven patent latent variables, namely age, generality, originality, foreign filings, technology field, forward citations, and backward citations were identified as having an influence on patent value. We used Ocean Tomo's patent auction price data in this study. We transformed the price and the predictor variables (excluding the dummy variables) to its logarithmic value. The OLS estimates revealed that forward citations and foreign filings were positively correlated to price. Both the variables jointly explained 14.79% of the variance in patent pricing. We did not find sufficient evidence to come up with any definite conclusions on the relationship between price and the variables such as age, technology field, generality, backward citations and originality. The Heckman two-stage sample selection model was used to test for selection bias. (C) 2011 Elsevier Ltd. All rights reserved.

The dynamics between forward citations and price of singleton patents

In recent years, business practitioners are seen valuing patents on the basis of the market price that the patent can attract. Researchers have also looked into various patent latent variables and firm variables that influence the price of a patent. Forward citations of a patent are shown to play a role in determining price. Using patent auction price data (of Ocean Tomo now ICAP patent brokerage), we delve deeper into of the role of forward citations. The successfully sold 167 singleton patents form the sample of our study. We found that, it is mainly the right tail of the citation distribution that explains the high prices of the patents falling on the right tail of the price distribution. There is consistency in the literature on the positive correlation between patent prices and forward citations. In this paper, we go deeper to understand this linear relationship through case studies. Case studies of patents with high and low citations are described in this paper to understand why some patents attracted high prices. We look into the role of additional patent latent variables like age, technology discipline, class and breadth of the patent in influencing citations that a patent receives.