Customer Xperience - Using Social Media Data to Drive Actionable Insights for Retail

• Autor(a) principal: Pedro Abrunhosa Martins
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/119118
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

Social media users produce substancial amounts of data on a daily basis. In this project, a solution is offered that allows for extraction, processing and analysis on this data in order to generate actionable insights for retail activities, including but not limited to: Pricing, Store Layout, Customer Experience, Targeted Campaigns, Allocation of stock, and Planning. For proof of concept, the Twitter platform was used as the primary data source. Data was selected based on queries so that only relevant data is extracted. User submitted text was processed using natural language processing (NLP) in order to extract entities and aspects of such entities, as well as relations between entities. Sentiment analysis and emotion detection is performed on the elements generated through NLP and context is understood around the user's submission. This is done by gathering data about the user as well as about the reception of the post by other users on the network. From this process, patterns start to emerge regarding subjects being talked about and sentiment polarity regarding those subjects. From these patterns actionable insights is extracted to drive the activities mentioned above