Optimizing Marketing Campaigns Using Random Forest and A/B Testing

[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n[if 2704 equals=”Yes”]n

n

Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

n[/if 2704]n

n

Year : 2025 [if 2224 equals=””]29/08/2025 at 11:06 AM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 01 09

n

n

nn

n

n

n

    By

    n

    [foreach 286]n

    n

    Murugeshwari P., Yuvaraj R., Yogeshwaran M., Prasanna Kumar U.,

    n t

  • n

    n[/foreach]

    n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

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

    2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

n

n

nMarketing 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.nn

n

n

n

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

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Artificial Intelligence Research & Advances ]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Artificial Intelligence Research & Advances (joaira)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article:
nMurugeshwari P., Yuvaraj R., Yogeshwaran M., Prasanna Kumar U.. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Optimizing Marketing Campaigns Using Random Forest and A/B Testing[/if 2584]. Journal of Artificial Intelligence Research & Advances. 07/08/2025; 12(03):01-09.

n

How to cite this URL:
nMurugeshwari P., Yuvaraj R., Yogeshwaran M., Prasanna Kumar U.. [if 2584 equals=”][226 striphtml=1][else]Optimizing Marketing Campaigns Using Random Forest and A/B Testing[/if 2584]. Journal of Artificial Intelligence Research & Advances. 07/08/2025; 12(03):01-09. Available from: https://journals.stmjournals.com/joaira/article=07/08/2025/view=0

nn

n

n[if 992 equals=”Open Access”]Full Text PDF[/if 992]n

n

n[if 992 not_equal=”Open Access”]n

n

n[/if 992]n

nn

nnn

n[if 379 not_equal=””]nn

Browse Figures

n

n

n[foreach 379]

figures

[/foreach]n

n

n

n[/if 379]

n

n

n

n

n

References n

n[if 1104 equals=””]n

  1. Shihab SH, Afroge S, Mishu SZ. RFM based market segmentation approach using advanced k-means and agglomerative clustering: a comparative study. In 2019 IEEE International conference on electrical, computer and communication engineering (ECCE). 2019 Feb 7; 1–4.
  2. Smaili MY, Hachimi H. New RFM-D classification model for improving customer analysis and response prediction. Ain Shams Eng J. 2023 Dec 1; 14(12): 102254.
  3. Alsayat A. Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca. Neural Comput Appl. 2023 Feb; 35(6): 4701–22.
  4. Bratina D, Faganel A. Using supervised machine learning methods for RFM segmentation: a casino direct marketing communication case. Market-Tržište. 2023 Jun 9; 35(1): 7–22.
  5. Bahari TF, Elayidom MS. An efficient CRM-data mining framework for the prediction of customer behaviour. Procedia Comput Sci. 2015 Jan 1; 46: 725–31.
  6. Lewaa I, Hafez MS, Ismail MA. Data integration using statistical matching techniques: A review. Stat J IAOS. 2021 Nov; 37(4): 1391–410.
  7. Lewaa I, Hafez MS, Ismail MA. Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. J Stat Appl Probab. 2023; 12(1): 247–65.
  8. Rahim MA, Mushafiq M, Khan S, Arain ZA. RFM-based repurchase behavior for customer classification and segmentation. J Retail Consum Serv. 2021 Jul 1; 61: 102566.
  9. Arefin S, Parvez R, Ahmed T, Jahin F, Sumaiya F, Ahsan M. Enhancing bank consumer retention: a comprehensive analysis of churn prediction using machine-learning and deep learning techniques. In International Conference on Advances in Information Communication Technology & Computing. Singapore: Springer Nature Singapore; 2024 Apr 29; 265–281.
  10. He X, Li C. The research and application of customer segmentation on e-commerce websites. In 2016 IEEE 6th International Conference on Digital Home (ICDH). 2016 Dec 2; 203–208.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n[if 1746 equals=”Retracted”]n

n

n

n

[/if 1746]n[if 4734 not_equal=””]

n

n

n

[/if 4734]n

n

Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 24/03/2025
Accepted 12/05/2025
Published 07/08/2025
Retracted
Publication Time 136 Days

n

n

106

nn

n

Login

n
My IP
n

PlumX Metrics

nn

n

n

n[if 1746 equals=”Retracted”]n

[/if 1746]nnn

nnn”}]