Predicting Consumer Purchasing Decision in The Online Food Delivery Industry

TL;DR

This paper evaluates various machine learning models to predict consumer purchasing decisions in the online food delivery industry, demonstrating that the C4.5 decision tree achieves the highest accuracy of 91.67% on a Bangalore dataset.

Contribution

It introduces a comparative analysis of four predictive models for consumer decision-making in online food delivery, highlighting the superior performance of C4.5.

Findings

C4.5 model achieved 91.67% accuracy

All models performed similarly in prediction accuracy

Predictive models can effectively understand customer behavior

Abstract

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 helps online 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 providing 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%.