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The aim of this paper is to understand consumers' perception of luxury hotel brands. To this end, the research evaluates consumers' "big" visual data on TripAdvisor through a machine learning approach. Results shed light on the significant part of non-textual elements of the hotel experience such as pictures, which cannot be explored through traditional methods as content analysis. In particular, the analysis of 7,395 consumers' pictures leads to the identification of the attributes that had the higher impact on their experience. These attributes emerged as specific features of interior elements of the hotels (rooms and restaurant).Finally, the study shows how big data analytics and machine learning algorithms can (i) help monitoring social media and understand consumers perception of luxury hotels through the new analysis of visual data, and (ii) turn into better brand management strategies for luxury hotel managers.

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Purpose Consumers online interactions, posts, rating and ranking, reviews of products/attractions/restaurants and so on lead to a massive amount of data that marketers might access to improve the decision-making process, by impacting the competitive and marketing intelligence. The purpose of this paper is to develop understanding of consumers online-generated contents in terms of positive or negative comments to increase marketing intelligence. Design/methodology/approach The research focuses on the collection of 9,652 tweets referring to three fast fashion retailers of different sizes operating in the UK market, which have been shared among consumers and between consumer and firm, and subsequently evaluated through a sentiment analysis based on machine learning. Findings Findings provide the comparison and contrast of consumers’ response toward the different retailers, while providing useful guidelines to systematically making sense of consumers’ tweets and enhancing marketing intelligence. Practical implications The research provides an effective and systemic approach to accessing the rich data set on consumers’ experiences based the massive number of contents that consumers generate and share online and investigating this massive amount of data to achieve insights able to impact on retailers’ marketing intelligence. Originality/value To best of the authors’ knowledge, while other authors tried to identify the effect of positive or negative online comments/posts/reviews, the present study is the first one to show how to systematically detect the positive or negative sentiments of shared tweets for improving the marketing intelligence of fast fashion retailers.