Detecting Influencing Behaviour for Product-Service Design Through Big Data Intelligence in Manufacturing

Abstract : The opportunity to gain insights from social media user generated data has triggered the interest of many companies who see in this a chance to better understand their customers’ preferences and identify trends. However, the huge amount of such data is not always manageable. Identification of influencers for a specific industry and monitoring of their behaviour in social media could be proved of great importance towards the direction of reducing the amount of data for analysis and extracting more useful and targeted insights. In this context, the paper aims to present a platform that will provide data analysts and product-service designers with influencer identification functionalities per industry, topic and in time and will also visualise the correlation among influencers based on specific topics of interest. The platform was evaluated under a use case from the fashion industry.
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Michael Petychakis, Evmorfia Biliri, Angelos Arvanitakis, Ariadni Michalitsi-Psarrou, Panagiotis Kokkinakos, et al.. Detecting Influencing Behaviour for Product-Service Design Through Big Data Intelligence in Manufacturing. 17th Working Conference on Virtual Enterprises (PRO-VE), Oct 2016, Porto, Portugal. pp.361-369, ⟨10.1007/978-3-319-45390-3_31⟩. ⟨hal-01614620⟩

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