Exploring the Impact of Big Data on Social Media Marketing Practices Among Major UK Retailers

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.

Authors

Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-03-14
DOI
https://doi.org/10.64388/irev9i9-1714891
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the Impact of Big Data on Social Media Marketing Practices Among Major UK Retailers

Favour Oluchi Amede
Iconic Research and Engineering Journals
Digital Marketing and Social Media
article

Exploring the Impact of Big Data on Social Media Marketing Practices Among Major UK Retailers

Favour Oluchi Amede
article en

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.

Iconic Research and Engineering JournalsVol. 9(9)
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Digital Marketing and Social Media
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.