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Use of Social Media Data as a Lead Indicator to Predict Retail Sales Performance (302969)

Keywords: Big Data, unstructured data, classification, customer sentiment, retail sales, social media data

Big Data in the form of unstructured social media data is making a big impact in the retail sector. Although recent years have seen increased use of social media data to inform business in the form of qualitative inputs, there is limited work that has spelled out a systematic procedure of using social media data in quantitative analysis. This paper will attempt to fill this gap by showing the practical aspects and theoretical concerns of building analytical models using both structured and unstructured data. The goals of this paper are modest. First, it provides a brief overview of Big Data—what it is and how it is being used in the retail sector. Second, it offers a classification method that converts and quantifies social media text data into discrete customer sentiment data. Third, this paper describes several statistical models, including time series and latent growth curve models, to examine the role of customer sentiment in the prediction of retail sales performance. In addition to the sentiment variables, demographic and econometrics variables are used to account further for variations in retail sales performance.