Predicting Consumer Purchasing Decisions in an Online Food Delivery Industry


Batool Madani, Hussam AlShraideh, American University of Sharjah, UAE


This transformation of food delivery businesses to online platforms has gained high attention in recent years. This due to the availability of customizing ordering experiences, easy payment methods, fast delivery, and others. The competition between online food delivery providers has intensified to attain a wider range of customers. Hence, they should have a better understanding of their customers’ needs and predict their purchasing decisions. Machine learning has a significant impact on companies’ bottom line. They are used to construct models and strategies in industries that rely on big data and need a system to evaluate it fast and effectively. Predictive modeling is a type of machine learning that uses various regression algorithms, analytics, and statistics to estimate the probability of an occurrence. The incorporation of predictive models helpsonline food delivery providers to understand their customers. In this study, a dataset collected from 388 consumers in Bangalore, India was provided to predict their purchasing decisions. Four prediction models are considered: CART and C4.5 decision trees, random forest, and rule-based classifiers, and their accuracies in prodividing the correct class label are evaluated. The findings show that all models perform similarly, but the C4.5 outperforms them all with an accuracy of 91.67%.


Food Delivery Industry, Purchasing Prediction, Machine Learning, Decision Trees, Random Forest, Rule-Based Classifier.

Full Text  Volume 11, Number 15