Small language models in big data marketing analytics: Addressing bias, accuracy and ethical challenges

Usiagwu, Michael Ehiedu and Muez, Lawal and Chinonso, Johnson (2025) Small language models in big data marketing analytics: Addressing bias, accuracy and ethical challenges. International Journal of Science and Research Archive, 14 (2). pp. 889-902. ISSN 2582-8185

Small Language Models (SLMs) are gaining prominence in big data marketing analytics due to their efficiency and scalability. This study evaluates the role of SLMs, emphasizing their benefits in tasks such as sentiment analysis and customer segmentation while addressing limitations, including biases, accuracy constraints, and ethical considerations. Using a mixed-methods approach, the research integrates experimental testing, literature reviews, and expert interviews to compare SLMs with larger models, focusing on performance, bias mitigation, and ethical compliance. The findings underscore the need for strategies to reduce biases, improve transparency, and ensure ethical deployment, enabling SLMs to be leveraged effectively in marketing analytics