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 customerexperience and weakens the effectiveness of traditional marketing models. This study develops and empirically testsa data-driven, AI-enabled customer analytics model aimed at enhancing retail process efficiency and strengtheningcustomer engagement. The proposed model, equipped with segmentation modules and personalization algorithms, isevaluated using regression analysis based on survey data. The findings indicate that personalized marketing offersdemonstrate higher contextual relevance and are positively associated with customer satisfaction. Empirical resultsconfirm that AI-based marketing strategies outperform traditional approaches in terms of effectiveness. The studycontributes practical and measurable insights for fostering sustainable growth, improving competitiveness, and supportingmanagerial decision-making in the retail sector
| Mualliflar | Ostonaqulova , Gulsaraxon |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2026-01-01 |
| Jild | 2 |
| Son | 1 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.18208590 |
DOI: 10.5281/zenodo.18208590 · Maqolaning asl sahifasi
AI-based retail analytics; customer personalization; algorithmic bias reduction; digital readiness; customer satisfaction; predictive modeling; managerial decision-making
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