Luca Molteni

Consumption Patterns, Digital Technology and Music Downloading

Abstract As today’s digital technologies modify the ways in which cultural goods are consumed and produced, the analysis of consumption patterns becomes one of the most important activities for producers in the cultural industries. Information on consumers’ behaviour becomes a strategic resource with which to anticipate competitors and improve the fit between supply and demand. This article contains an empirical analysis on the music industry, where analysis of on-line survey results show that music downloading is not a unique phenomenon and consumers are approaching the digital environment in different ways. The presence of these differing consumption profiles entails a deep segmentation strategy, requiring that both sides of the strategy — from selection of artists to promotion and pricing policies — be addressed to deal with this segmentation. Managers working in the cultural industries will have to face fundamental changes associated with the shift to a world without physical artefacts, and will need to be able to predict emerging consumption profiles in advance and prepare mixed strategies to handle the period of transition.

FORECASTING WITH TWITTER DATA: AN APPLICATION TO USA TV SERIES AUDIENCE

Various researchers and analysts highlighted the potential of Big Data, and social networks in particular, to optimize demand forecasts in managerial decision processes in different sectors. Other authors focused the attention on the potential of Twitter data in particular to predict TV ratings. In this paper, the interactions between television audience and social networks have been analysed, especially considering Twitter data. In this experiment, about 2.5 million tweets were collected, for 14 USA TV series in a nine-week period through the use of an ad hoc crawler created for this purpose. Subsequently, tweets were classified according to their sentiment (positive, negative, neutral) using an original method based on the use of decision trees. A linear regression model was then used to analyse the data. To apply linear regression, TV series have been grouped in clusters; clustering is based on the average audience for the individual series and their coefficient of variability. The conclusions show and explain the existence of a significant relationship between audience and tweets.