Making sense of consumers' tweets: sentiment outcomes for fast fashion retailers through big data analytics

Pantano, Eleonora, Giglio, Simona and Dennis, Charles ORCID: https://orcid.org/0000-0001-8793-4823 (2019) Making sense of consumers' tweets: sentiment outcomes for fast fashion retailers through big data analytics. International Journal of Retail & Distribution Management, 47 (9) . pp. 915-927. ISSN 0959-0552 [Article] (doi:10.1108/IJRDM-07-2018-0127)

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Abstract

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 aim of this research is to help 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 towards the different retailers, while providing useful guidelines to systematically making sense of consumers’ tweets and enhancing marketing intelligence.
Practical Implications- Our research provides an effective and systemic approach to (i) accessing the rich data set on consumers’ experiences based the massive number of contents that consumers generate and share online, and (ii) investigating this massive amount of data to achieve insights able to impact on retailers’ marketing intelligence.
Originality/Value- To best of our 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.

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