Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis | Journal of Data Science and Intelligent Systems

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis | Journal of Data Science and Intelligent Systems </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/7/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Data Science and Intelligent Systems"/> <meta name="citation_journal_abbrev" content="JDSIS"/> <meta name="citation_issn" content="2972-3841"/> <meta name="citation_author" content="Israa Lewaaelhamd"/> <meta name="citation_author_institution" content="Department of Business Administration, The British University in Egypt, Egypt"/> <meta name="citation_title" content="Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024"/> <meta name="citation_volume" content="2"/> <meta name="citation_issue" content="1"/> <meta name="citation_firstpage" content="29"/> <meta name="citation_lastpage" content="36"/> <meta name="citation_doi" content="10.47852/bonviewJDSIS32021293"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1293"/> <meta name="citation_abstract" xml:lang="en" content="Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques, customer churn prediction is a prominent one. In order to forecast customer switching, data scientists employ a variety of demographic, social, transactional, and behavioral variables and attributes. Unfortunately, many businesses in the United Kingdom still lack the comprehensive and adaptable consumer data required to perform accurate analyses. As a result, they often rely heavily on data produced by enterprise resource planning systems, which is primarily transactional in nature. Consequently, businesses are often limited to modeling and forecasting on transactional data alone and are unlikely to invest significantly in marketing research or other customer-related sources. Businesses are often limited to performing modeling and forecasting on transactional data that are most often not based on advanced techniques like recency, frequency and monetary (RFM) and ML. So, the major objective of the current work is to provide a mix of ML and RFM analysis techniques for churn prediction using mostly transactional data. The dataset was taken from the dataset search website containing online retail datasets. Every customer's RFM scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a limited time. Through this paper, different techniques are compared. We used K-means and DBSCAN clustering. By the end of this paper, it may be inferred that the act of dividing customers into six distinct clusters is a more practical and straightforward approach. 聽 Received: 29 June 2023 | Revised: 21 August 2023 | Accepted: 6 September 2023 聽 Conflicts of Interest The author declares that she has no conflicts of interest to this work. 聽 Data Availability Statement The data that support the findings of this study are openly available in [Google Drive] at https://drive.google.com/file/d/1qme8WeYkmXWfWkLa87jG37owWPCsNY65/view?usp=drive_web"/> <meta name="citation_keywords" xml:lang="en" content="RFM analysis"/> <meta name="citation_keywords" xml:lang="en" content="statistical approaches"/> <meta name="citation_keywords" xml:lang="en" content="data analysis"/> <meta name="citation_keywords" xml:lang="en" content="machine learning models"/> <meta name="citation_keywords" xml:lang="en" content="artificial intelligence"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/jdsis/article/download/1293/622"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Israa Lewaaelhamd"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-09-08"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-06-29"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-01-18"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-07"/> <meta name="DC.Description" xml:lang="en" content="Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques, customer churn prediction is a prominent one. In order to forecast customer switching, data scientists employ a variety of demographic, social, transactional, and behavioral variables and attributes. Unfortunately, many businesses in the United Kingdom still lack the comprehensive and adaptable consumer data required to perform accurate analyses. As a result, they often rely heavily on data produced by enterprise resource planning systems, which is primarily transactional in nature. Consequently, businesses are often limited to modeling and forecasting on transactional data alone and are unlikely to invest significantly in marketing research or other customer-related sources. Businesses are often limited to performing modeling and forecasting on transactional data that are most often not based on advanced techniques like recency, frequency and monetary (RFM) and ML. So, the major objective of the current work is to provide a mix of ML and RFM analysis techniques for churn prediction using mostly transactional data. The dataset was taken from the dataset search website containing online retail datasets. Every customer's RFM scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a limited time. Through this paper, different techniques are compared. We used K-means and DBSCAN clustering. By the end of this paper, it may be inferred that the act of dividing customers into six distinct clusters is a more practical and straightforward approach. 聽 Received: 29 June 2023 | Revised: 21 August 2023 | Accepted: 6 September 2023 聽 Conflicts of Interest The author declares that she has no conflicts of interest to this work. 聽 Data Availability Statement The data that support the findings of this study are openly available in [Google Drive] at https://drive.google.com/file/d/1qme8WeYkmXWfWkLa87jG37owWPCsNY65/view?usp=drive_web"/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="1293"/> <meta name="DC.Identifier.pageNumber" content="29-36"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJDSIS32021293"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1293"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 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<div class="pkp_structure_main" role="main"> <a id="pkp_content_main"></a> <div class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://ojs.bonviewpress.com/index.php/jdsis/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/jdsis/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/68"> Vol. 2 No. 1 (2024) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Research Articles </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Israa Lewaaelhamd </span> <span class="affiliation"> Department of Business Administration, The British University in Egypt, Egypt </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0002-3471-9609" target="_blank"> https://orcid.org/0000-0002-3471-9609 </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJDSIS32021293"> https://doi.org/10.47852/bonviewJDSIS32021293 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> RFM analysis, statistical approaches, data analysis, machine learning models, artificial intelligence </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Machine learning (ML) encompasses a diverse array of both supervised and unsupervised techniques that facilitate prediction, classification, and anomaly detection. Among the many fields of application for such techniques, customer churn prediction is a prominent one. In order to forecast customer switching, data scientists employ a variety of demographic, social, transactional, and behavioral variables and attributes. Unfortunately, many businesses in the United Kingdom still lack the comprehensive and adaptable consumer data required to perform accurate analyses. As a result, they often rely heavily on data produced by enterprise resource planning systems, which is primarily transactional in nature. Consequently, businesses are often limited to modeling and forecasting on transactional data alone and are unlikely to invest significantly in marketing research or other customer-related sources. Businesses are often limited to performing modeling and forecasting on transactional data that are most often not based on advanced techniques like recency, frequency and monetary (RFM) and ML. So, the major objective of the current work is to provide a mix of ML and RFM analysis techniques for churn prediction using mostly transactional data. The dataset was taken from the dataset search website containing online retail datasets. Every customer's RFM scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a limited time. Through this paper, different techniques are compared. We used K-means and DBSCAN clustering. By the end of this paper, it may be inferred that the act of dividing customers into six distinct clusters is a more practical and straightforward approach.</p> <p>聽</p> <p><strong>Received: </strong>29 June 2023 <strong>| Revised: </strong>21 August 2023 <strong>| Accepted:</strong> 6 September 2023</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The author declares that she has no conflicts of interest to this work.</p> <p>聽</p> <p><strong>Data Availability Statement</strong></p> <p>The data that support the findings of this study are openly available in [Google Drive] at <a href="https://drive.google.com/file/d/1qme8WeYkmXWfWkLa87jG37owWPCsNY65/view?usp=drive_web">https://drive.google.com/file/d/1qme8WeYkmXWfWkLa87jG37owWPCsNY65/view?usp=drive_web</a></p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89269", "element":"#trendmd-suggestions"}'></script> </div> <div class="entry_details"> <div class="item cover_image"> <div class="sub_item"> <a href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/68"> <img src="https://ojs.bonviewpress.com/public/journals/7/cover_issue_68_en_US.png" alt=""> </a> </div> </div> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1293/622"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>2023-09-08</span> </div> </section> </div> <div class="item issue"> <section class="sub_item"> <h2 class="label"> Issue </h2> <div class="value"> <a class="title" href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/68"> Vol. 2 No. 1 (2024) </a> </div> </section> <section class="sub_item"> <h2 class="label"> Section </h2> <div class="value"> Research Articles </div> </section> </div> <div class="item copyright"> <h2 class="label"> License </h2> <p>Copyright (c) 2023 Author</p> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> </div> <div class="item citation"> <section class="sub_item citation_display"> <h2 class="label"> How to Cite </h2> <div class="value"> <div id="citationOutput" role="region" aria-live="polite"> <div class="csl-bib-body"> <div class="csl-entry">Lewaaelhamd, I. 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Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis | Journal of Data Science and Intelligent Systems