A Case Study for the Benefits of Cluster Analysis of Social Media Data and Retailer Sales for Twitter and A UK Based Department Store

Tommy Hamm and Enda Fallon (Athlone Institute of Technology, Ireland); Paul Connolly (The NPD Group, Inc, Ireland); Kieran Flanagan (Athlone Institute of Technology & The NPD Group, Ireland)

Due to the continuous growth of online interaction, social media is becoming increasingly useful in understanding trends in human behavior both locally and globally. On average there are approximately 6,000 tweets posted on Twitter every second, equating to approximately 500 million tweets per day. This wealth of information shared publicly can be hugely beneficial in gaining insights into reactions and implications caused by social, environmental, or financial events. The information has the potential to be particularly useful to retailers in terms of market research and sales forecasting when used along with some of the latest data analysis and Artificial Intelligence (AI) tools. The goal of this study is to utilize data from the Twitter platform, shared by the public, to extract what benefits and insights can be gained by analyzing the correlation between external KPIs, extracted from non-UK based geographical social media data, and sales recorded in the UK based luxury retailer at the corresponding time.

Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V23

Published: no date/time given

DOI: 10.5013/IJSSST.a.23.02.02