INTEGRATING AI-BASED CUSTOMER ANALYTICS INTO INNOVATIVE RETAIL MARKETING STRATEGIES
DOI:
https://doi.org/10.5281/zenodo.18208590Keywords:
AI-based retail analytics; customer personalization; algorithmic bias reduction; digital readiness; customer satisfaction; predictive modeling; managerial decision-makingAbstract
Intensified competition and rapidly changing market dynamics pose increasing challenges for retail enterprises.
In the context of the digital economy, the adoption of artificial intelligence technologies significantly affects customer
experience and weakens the effectiveness of traditional marketing models. This study develops and empirically tests
a data-driven, AI-enabled customer analytics model aimed at enhancing retail process efficiency and strengthening
customer engagement. The proposed model, equipped with segmentation modules and personalization algorithms, is
evaluated using regression analysis based on survey data. The findings indicate that personalized marketing offers
demonstrate higher contextual relevance and are positively associated with customer satisfaction. Empirical results
confirm that AI-based marketing strategies outperform traditional approaches in terms of effectiveness. The study
contributes practical and measurable insights for fostering sustainable growth, improving competitiveness, and supporting
managerial decision-making in the retail sector
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