Optimizing Delivery Time and Customer Satisfaction: A Data-Driven Analysis of Zomato’s Logistics Operations

This paper explores how Zomato, a leading Indian food delivery platform, can enhance its logistics performance to improve customer satisfaction. The study used a simulated dataset representing over 5,000 orders across major cities. Key findings indicate that traffic and weather significantly affect delivery durations, with congested traffic increasing times by 20–25 minutes and rain causing delays of up to 20%. A clear negative trend emerged between delivery delay and customer ratings. Late deliveries—especially those surpassing ETA—resulted in satisfaction drops. Using statistical and machine learning models, the research identified critical performance indicators and proposed interventions like dynamic route optimization, ETA recalibration, and zone-specific strategies. These could reduce delivery times by 15–20%, improve ETA accuracy by 40–50%, and raise satisfaction by up to 0.5 points. Overall, this data-backed framework not only advances logistics theory but also supports Zomato’s operational edge in India’s competitive food delivery space.