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Open Journal of Business Entrepreneurship and Marketing

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Enhancing Digital Marketing Strategies in the Food Delivery Business through AI-Driven Ensemble Machine Learning Techniques
Authors
Adib Hossain 1 ,  Afia Fairooz Tasnim 2 ,  Fariha Akhter 3 ,  Mst Masuma Akter Semi 4 ,  Rayhan Khan 5 ,  Rukshanda Rahman 6 ,  Sumaiya Yeasmin 7 ,  Anseena Anees Sabeena 8
Author's Details

Name: Adib Hossain

Email: adibhossain998@gmail.com

Department: Department of Business Analytics

Affiliation Number: 1

Address: Indiana, USA

https://doi.org/10.63471/ojbem_25002
Affiliations

1 Department of Business Analytics , Trine University , Indiana, USA

2 Department of Public Health , California State University Long Beach, CA 90840, USA

3 Department of Nursing , 855 N Vermont Ave, Los Angeles, CA 9002, USA

4 Department of Education, Westcliff University, 400 Irvine, CA 92614, USA

5 Department of Special Education and Counseling , California State University , Long Beach, CA 90840, Los Angeles, USA

6 Department of Computer Science , Westcliff University, 400 Irvine, CA 92614, USA

7 Department of Psychology , St.Francis College, USA

8 Department of Business Administration , Westcliff University , 400 Irvine, CA 92614, USA

Abstract
The digital marketing for food delivery business is the focus of this study, which investigates the use of ensemble machine learning (ML) approaches. The study's overarching goal is to pave the way for artificial intelligence (AI)-based recommendations by analyzing consumer data with the hope of discovering consumer preferences and predicting behavior. In order to improve the accuracy of predictions, the ensemble method combines the results of decision trees, naïve Bayes, and closest neighbor algorithms. Both the decision tree and nearest neighbor algorithms were able to obtain perfect predictions with zero error and 100% accuracy, as seen in the accuracy matrix charts. On the other hand, the naïve Bayes method was able to accurately identify labels in all classes with a minimal error rate of 0.028 and a high accuracy of 97.175%. With a success rate of over 90%, the majority vote method allows models to be integrated using less than 50% of the randomized data, which minimizes customer dissatisfaction. When taken as a whole, these ML algorithms greatly improve the efficiency and efficacy of food delivery business digital marketing campaigns by cutting down on wasted time and money.

Keywords: 

Digital marketing, Food delivery business, Machine learning, Artificial intelligence, Accuracy

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This article is Open Access CC BY-NC
Article Information
Article Type
Research Paper
Submitted
10 September, 2025
Revised
02 October, 2025
Accepted
17 October, 2025
Online First
25 October, 2025
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