Abstract

The exponential increase in data today derives from the big amount of interactions among consumers that spreads in social media, using mobile devices, IT, localization, historical data in purchase, data owned by companies, behavioral insights and so on through connected online devices and mobile devices in which all actions, interactions, shares and reactions can be easily recorded and analyze. Consumers are traditionally considered as a single person or a single company: business are made by people. Behavioral, economical, psychological considerations are sometimes equivalent in consumption choices. Considering that consumers have become incessant generators of they continuously provide de-structured behavioral data and information. Marketing automation and predictive analysis might support the business growth towards a better comprehension of consumers: data shows therefore the potential utility to assume and implement aware marketing strategies. Researchers and managers will access data in order to evaluate alternative choices in consumers. In the marketing intelligence, with reference to the business intelligence applications marketing-oriented, market and consumer data are collected and processed in analyzes that support decision making. Available data mining techniques allow to reach the objective of study, extracting or detecting models to predict consumer behavior by drawing on large database. Considering the large amount of data, the paper investigate the currently and future scenarios, the best tools available in order to discover previously unknown and potentially useful relationships, patterns and information, within large databases based on the proposed framework.

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