Search

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains. [ABSTRACT FROM PUBLISHER]

Internet has become integral part of day-to-day business to almost everybody, this result in diversified interests of customers. So as to catering to this intrinsic need, any E-Commerce firm to survive must be of cutting edge and competitive edge. The providers should not only to stay abreast with technologies where the life cycle of a technology is at its bare minimum and further dwindling They should also entertain the customers through inventively fine tuning the delicate parameters of website. This involves evaluating the usage pattern and trails of the customer left as a log, deriving pattern from click stream etc. However, the cutting edge technology applied by the big healthcare E-Commerce industries like private Cloud utilization (John et al., Optimization and Computing, 2012), web content mining enables them to attract and retain innumerable number of customers even during peak hours. According to the research carried out in this paper, there are two distinct types of online business based on web content promoted towards buy, they are classified as exhaustive promote and partial promote. Typically exhaustive promote website perform even complex web mining operations for identifying and enticing the potential customers to buy various healthcare products based on various factors such as buying habits, interests etc. However for the partial promote in the observed cases, they are not even aware of the existence of such techniques. Based on the analysis performed on various renounced online websites, if 60% and above of the web content leads the customer to perform the 'buy, then it is exhaustive promote the rest is considered as partial promote. Moreover a huge gap is observed between Partial and exhaustive promote when it comes to the deployment of the web mining techniques. Consequently to understand the varying role of web mining in the online business successes, this paper models the web mining as a Game in Cournot Model. The results show that the model suits the economics behind the online businesses in both the cases and thus helps to identify or enhance the underlying web mining techniques towards business success. [ABSTRACT FROM AUTHOR]

Churn management is a fundamental concern for businesses and the emergence of the digital economy has made the problem even more acute. Companies' initiatives to handle churn and customers' profitability issues have been directed to more customer-oriented strategies. In this paper, we present a customer relationship management framework based on the integration of the electronic channel. This framework is constituted of four tools that should provide an appropriate collection, treatment and analysis of data. From this perspective, we pay special attention to some of the latest data mining developments which, we believe, are destined to play a central role in churn management. Relying on sensitivity analysis, we propose an analysis framework able to prefigure the possible impact induced by the ongoing data mining enhancements on churn management and on the decision-making process. [ABSTRACT FROM AUTHOR]