Optimizing Marketing Campaigns Using Random Forest and A/B Testing

Year : 2025 | Volume : 12 | Issue : 03 | Page : 01 09
    By

    Murugeshwari P.,

  • Yuvaraj R.,

  • Yogeshwaran M.,

  • Prasanna Kumar U.,

  1. Professor, Artificial Intelligence and Data Science, Karpagam College of Engineering (Anna University) Coimbatore, Tamil Nadu, India
  2. Student, Artificial Intelligence and Data Science, Karpagam College of Engineering (Anna University) Coimbatore, Tamil Nadu, India
  3. Student, Artificial Intelligence and Data Science, Karpagam College of Engineering (Anna University) Coimbatore, Tamil Nadu, India
  4. Student, Artificial Intelligence and Data Science, Karpagam College of Engineering (Anna University) Coimbatore, Tamil Nadu, India

Abstract

Marketing initiatives play a vital role in driving business growth by reaching targeted consumer segments through tailored strategies across multiple channels. The success of these initiatives is influenced by various factors, including the type and duration of the campaign, the characteristics of the target audience, the communication channels employed, and the overall efficiency of each strategy. These factors collectively impact key performance metrics such as conversion rates, customer acquisition costs, and return on investment (ROI). This study explores a comprehensive dataset containing parameters such as Campaign ID, Company Name, Campaign Type, Target Audience, Campaign Duration, Channel Used, Conversion Rate, Acquisition Cost, ROI, and Location. By applying machine learning techniques, specifically the Random Forest algorithm, this research aims to uncover hidden patterns and actionable insights from the data. The primary objectives include identifying the most cost-effective marketing channels, understanding how audience segmentation influences campaign outcomes, and determining the optimal configurations for successful campaigns. The Random Forest model is particularly suited for capturing complex, nonlinear relationships in the data and producing reliable predictions. These insights can be used to refine marketing strategies, enhance decision-making processes, allocate resources more efficiently, and ultimately maximize the overall impact and profitability of marketing campaigns.

Keywords: Marketing campaign optimization, Random forest algorithm, audience segmentation, ROI prediction, data driven insights, A/B testing

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Murugeshwari P., Yuvaraj R., Yogeshwaran M., Prasanna Kumar U.. Optimizing Marketing Campaigns Using Random Forest and A/B Testing. Journal of Artificial Intelligence Research & Advances. 2025; 12(03):01-09.
How to cite this URL:
Murugeshwari P., Yuvaraj R., Yogeshwaran M., Prasanna Kumar U.. Optimizing Marketing Campaigns Using Random Forest and A/B Testing. Journal of Artificial Intelligence Research & Advances. 2025; 12(03):01-09. Available from: https://journals.stmjournals.com/joaira/article=2025/view=225010


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 24/03/2025
Accepted 12/05/2025
Published 07/08/2025
Publication Time 136 Days
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