ARTIFICIAL INTELLIGENCE-DRIVEN DIGITAL MARKETING AND CUSTOMER ENGAGEMENT IN DARK STORE RETAIL MODELS
Abstract
The fusion of artificial intelligence (AI) and quick-commerce has reshaped the way urban retailers attract, serve, and keep their customers. Dark stores, which are non-customer-facing micro-fulfilment centres built solely for picking online orders, now sit at the heart of ultra-fast delivery, yet their marketing and engagement logic departs sharply from that of conventional retail. This paper investigates how AI-enabled digital marketing tools, namely personalization engines, conversational agents, predictive demand analytics, and dynamic pricing, influence customer engagement in dark store retail settings. Using a sequential explanatory mixed-methods design, the study pairs a structured consumer survey with semi-structured interviews of quick-commerce practitioners, drawing on evidence published between 2020 and 2026. The results suggest that AI-based personalization and conversational interfaces markedly strengthen engagement and conversion, while predictive analytics safeguards the product availability that sustains consumer trust in the dark store promise of speed and freshness. At the same time, privacy concerns, narrow unit economics, and limited operational transparency temper these gains. The paper offers practical guidance for quick-commerce operators and outlines a research agenda for ethical and sustainable AI marketing across hyper-local fulfilment networks.
References
Alharbi, S., & Alotaibi, M. (2025). Exploring the impact of AI-driven personalization on consumer engagement in digital marketing. Argumenta Oeconomica / Management. https://www.management-poland.com/Exploring-the-Impact-of-AI-Driven-Personalization-on-Consumer-Engagement-in-nDigital,200968,0,2.html
Almeida, R., & Santos, P. (2025). AI-powered personalization in marketing: Balancing engagement and privacy. Journal of Productivity. https://journal.ppipbr.com/index.php/productivity/article/view/650
Alvarez & Marsal. (2023). In-city warehousing: The emerging backbone for quick commerce. https://www.alvarezandmarsal.com/sites/default/files/2023-04/In-City%20Warehousing%20-%20Urban%20Logistics%20Report_2.pdf
Andreas, M. (2024). A systematic review on dark stores in quick commerce. Delhi Business Review, 26(2). https://www.delhibusinessreview.org/V26n2/dbr_v26n2g.pdf
ASRC. (2025). Artificial intelligence-enhanced predictive analytics for demand forecasting in retail supply chains. ASRC Procedia: Global Perspectives in Science and Scholarship. https://global.asrcconference.com/index.php/asrc/article/view/43
David, R., Gund, P., & Daniel, A. (2024). Dark stores as enablers of last-mile logistics in quick commerce. Delhi Business Review, 26(2). https://www.delhibusinessreview.org/V26n2/dbr_v26n2g.pdf
Frontiers. (2026). Assessing artificial intelligence’s impact on e-customer loyalty. Frontiers in Artificial Intelligence. https://pmc.ncbi.nlm.nih.gov/articles/PMC12075180/
Ganapathy, S., & Gupta, R. (2023). The growth of e-grocery and quick-commerce platforms during the pandemic. Delhi Business Review, 26(2). https://www.delhibusinessreview.org/V26n2/dbr_v26n2g.pdf
Hossain, M., et al. (2024). E-commerce product recommendation system based on machine learning. arXiv. https://arxiv.org/abs/2407.21026
HRMARS. (2024). A review on generative AI in digital marketing: Transforming customer engagement and firm performance. Human Resource Management Academic Research Society. https://hrmars.com/papers_submitted/28297/
IJFMR. (2026). Adoption of quick commerce in the food sector. International Journal for Multidisciplinary Research. https://www.ijfmr.com/papers/2026/1/67942.pdf
IJRPR. (2026). Impact of AI-based chatbots on customer satisfaction in retail. International Journal of Research Publication and Reviews. https://ijrpr.com/uploads/V7ISSUE2/IJRPR59740.pdf
Klein, K., & Martinez, L. (2023). AI-based chatbots in conversational commerce and their effects on product personalization. Electronic Markets. https://pmc.ncbi.nlm.nih.gov/articles/PMC10206356/
Kumar, A., & Sharma, V. (2025). AI in digital marketing: Enhancing customer engagement and brand performance. International Journal of Advanced Production Technology. https://ijapt.org/index.php/journal/article/view/473
Loukili, M., et al. (2023). Recommendation systems in e-commerce platforms using association rule mining. International Journal of Creative Research Thoughts. https://www.ijcrt.org/papers/IJCRT26A4186.pdf
Moriuchi, E., et al. (2024). Consumer engagement in chatbots and voicebots: A multiple-experiment approach in online retailing. Journal of Retailing and Consumer Services. https://www.sciencedirect.com/science/article/abs/pii/S0969698924000249
Schorung, M. (2023). Quick commerce: Will the disruption of the food retail industry happen? Investigating the quick commerce supply chain and the impacts of dark stores. Logistics City Chair, University Gustave Eiffel. https://shs.hal.science/halshs-04098596
Schorung, M., et al. (2023). Dark stores in the City of Light: Geographical and transportation impacts of quick commerce in Paris. Journal of Transport Geography. https://www.sciencedirect.com/science/article/abs/pii/S0739885923000732
Scribd Research. (2026). Dark stores: Impact on urban shopping behavior. https://www.scribd.com/document/837197661/Brmmr-Final
Wang, Y., & Li, J. (2025). Personalized recommendation system of e-commerce using graph attention networks. Applied Artificial Intelligence. https://www.tandfonline.com/doi/full/10.1080/08839514.2025.2487417
Zhou, H., & Chen, X. (2021). Neural networks in predicting consumer behavior and market trends. American Journal of Artificial Intelligence and Neural Networks. https://australiansciencejournals.com/ajainn/article/view/345
