Detecting Influencing Behaviour for Product-Service Design Through Big Data Intelligence in Manufacturing
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.
KeywordsInfluencer Big data Social media Business intelligence Manufacturing
This work has been funded by the European Commission through the FoF-RIA Project PSYMBIOSYS: Product-Service sYMBIOtic SYStems (No. 636804). The authors wish to acknowledge the Commission and all the PSYMBIOSYS project partners for their contribution.
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