AI-Driven Marketing Strategies for Fresh Food E-Commerce: Delphi-Bsed Approach to Distribution and Logistics Optimization

Purpose: This study aims to explore how artificial intelligence can optimize marketing strategies in the fresh food e-commerce sector. With a focus on perishability, transparency, and consumer trust, the research investigates AI applications that strengthen distribution and logistics, ultimately enhancing competitiveness and sustainability in a rapidly expanding digital marketplace. Research design, data and methodology: The study employs the Delphi method to gather structured expert insights across multiple survey rounds. A panel of industry and academic experts evaluated potential AI-driven strategies, while agreement indices and the Content Validity Ratio (CVR) were applied to ensure consensus and validity. This iterative process identified strategies with strong expert support. Results: Findings indicate that AI-driven approaches such as real-time demand forecasting, automated inventory management, and last-mile delivery optimization were rated highly essential by experts. High consensus and CVR values confirmed their validity. These strategies provide actionable solutions for improving marketing effectiveness, reducing waste, and strengthening operational efficiency in fresh food e-commerce. Conclusions: The study concludes that integrating AI into fresh food e-commerce requires strategies validated through both consensus and CVR, ensuring relevance and reliability. While expert-based methods present limitations, the research provides a robust foundation. Future studies should test these strategies empirically, expanding practical applications across global contexts in distribution and logistics.