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Internet has been widely recognized as a new data source that can be used either to compile new statistics, or to enhance the traditional ones in several fields of official statistics. Considering that online commerce has a rapid growing share in the overall household's consumption expenditures behavior broke down by distribution/transaction channel, price statistics is one of the research areas in official statistics which benefits greatly from this new data source. This paper provides a description of the Romanian National Institute of Statistics experience regarding the use of Internet as a data source and an exercise in compiling an experimental consumer price index (CPI) based on Internet data. Aim the pilot project was to investigate whether alternative data collection methods for price statistics can be introduced and enhance the statistical production system in the near future and, most important, it was a great firsthand opportunity to identify methodological challenges which are inherent to Big Data sources from the official statistics point of view. The tool chain is built on top of the traditional methodology used for CPI, enhanced by new features such as simple clustering technique for treating high volatility present in the collected data using a distance-based method for classification similar products. [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]