Exploring the Impact of Big Data on Social Media Marketing Practices Among Major UK Retailers
Abstract
This study explores the impact of big data analytics on social media marketing practices among major UK retailers, focusing on platforms such as Facebook, Instagram, Twitter, and YouTube. The research examines how these companies leverage big data to optimise marketing strategies, enhance customer engagement, and improve return on investment (ROI). Employing a quantitative survey methodology integrated with semi-structured interviews with marketing professionals in the tech and retail sectors, the study adopts a positivist philosophy with a deductive approach underpinned by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory. Key findings reveal a strong positive correlation (r = 0.65) between big data usage and customer engagement, with regression analysis showing that big data analytics explains 45% of the variance in marketing ROI (β = 0.68, p < 0.001). Case studies of ASOS, Next, and Boots demonstrate practical applications including sentiment analysis, predictive analytics, and cross-platform data integration. The study also highlights significant challenges in data privacy and ethical considerations, suggesting that future adoption of big data analytics in marketing requires a balance between technological advancement and consumer trust. Recommendations include investing in advanced analytics tools, staff training, and cultivating a data-driven culture to maintain competitiveness.
Keywords
Big Data Analytics, Customer Engagement, Return on Investment, Social Media Marketing, UK Retail
Citations
IRE Journals:
Favour Oluchi Amede "Exploring the Impact of Big Data on Social Media Marketing Practices Among Major UK Retailers" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 976-983 https://doi.org/10.64388/IREV9I9-1714891
IEEE:
Favour Oluchi Amede
"Exploring the Impact of Big Data on Social Media Marketing Practices Among Major UK Retailers" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1714891
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