Analyzing Customer Preferences in the Food Delivery Industry Using Big Data

This research explores the food delivery industry’s digital transformation and its potential through big data analytics. The attributes under scrutiny include restaurant identification, geographical metadata, city-based information, restaurant nomenclature, pricing data, customer ratings, total review counts, cuisine categorizations, physical location data, and delivery duration statistics. The research outcomes bear substantial relevance to data-driven decision- making for restaurateurs, digital food delivery platforms, and policymakers, providing actionable insights for operational enhancements and informed strategies. Additionally, these findings elucidate dynamic industry trends, offering a foundational framework for further computational research in this rapidly evolving domain thorough investigation of consumer preferences and the elements that go into a satisfying dining experience is made possible by the addition of customer ratings, the total number of reviews, and cuisine classifications. The research expands its focus to consider the time-related aspects by exploring how industry patterns change over time. The outcomes of this study are highly relevant for making informed, data-driven decisions in the food delivery industry. These insights can guide policymakers, digital food delivery companies, and restaurant owners in crafting effective strategies and enhancing their operations.