{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T18:52:26Z","timestamp":1780944746617,"version":"3.54.1"},"reference-count":73,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T00:00:00Z","timestamp":1564444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2021,3,27]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers\u2019 transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors\u2019 knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-09-2018-0506","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T05:56:30Z","timestamp":1564466190000},"page":"221-242","source":"Crossref","is-referenced-by-count":22,"title":["A new methodology for customer behavior analysis using time series clustering"],"prefix":"10.1108","volume":"50","author":[{"given":"Hossein","family":"Abbasimehr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Shabani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2019,7,30]]},"reference":[{"issue":"4","key":"key2021043008575544000_ref001","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1080\/00207543.2012.707342","article-title":"A framework for identification of high-value customers by including social network based variables for churn prediction using neuro-fuzzy techniques","volume":"51","year":"2013","journal-title":"International Journal of Production Research"},{"key":"key2021043008575544000_ref002","first-page":"283","volume-title":"Data Mining Approach for Intelligent Customer Behavior Analysis for a Retail Store","year":"2016"},{"key":"key2021043008575544000_ref003","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.is.2015.04.007","article-title":"Time-series clustering \u2013 a decade review","volume":"53","year":"2015","journal-title":"Information Systems"},{"issue":"9","key":"key2021043008575544000_ref004","doi-asserted-by":"crossref","first-page":"1976","DOI":"10.1108\/MD-09-2014-0551","article-title":"Mining the dominant patterns of customer shifts between segments by using top-k and distinguishing sequential rules","volume":"53","year":"2015","journal-title":"Management Decision"},{"key":"key2021043008575544000_ref005","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1108\/K-07-2015-0180","article-title":"Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis","volume":"45","year":"2016","journal-title":"Kybernetes"},{"key":"key2021043008575544000_ref006","article-title":"SparseDTW: a novel approach to speed up dynamic time warping","volume-title":"Proceedings of the Eighth Australasian Data Mining Conference","year":"2009"},{"issue":"7","key":"key2021043008575544000_ref007","doi-asserted-by":"crossref","first-page":"106","DOI":"10.5539\/ijbm.v11n7p106","article-title":"Taxonomy of marketing strategies using bank customers\u2019 clustering","volume":"11","year":"2016","journal-title":"International Journal of Business and Management"},{"key":"key2021043008575544000_ref008","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1080\/01969722.2017.1412883","article-title":"Heavy moving averages and their application in econometric forecasting AU - Le\u00f3n-Castro, Ernesto","volume":"49","year":"2018","journal-title":"Cybernetics and Systems"},{"issue":"1","key":"key2021043008575544000_ref009","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.eswa.2007.09.006","article-title":"Mining changing customer segments in dynamic markets","volume":"36","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"key2021043008575544000_ref010","volume-title":"Time Series Analysis: forecasting and Control","year":"2015"},{"issue":"3","key":"key2021043008575544000_ref011","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0167-8116(02)00079-4","article-title":"The dynamics of value segments: modeling framework and empirical illustration","volume":"19","year":"2002","journal-title":"International Journal of Research in Marketing"},{"key":"key2021043008575544000_ref012","volume-title":"Introduction to Time Series and Forecasting","year":"2002"},{"issue":"4","key":"key2021043008575544000_ref013","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.eswa.2004.12.033","article-title":"Mining changes in customer behavior in retail marketing","volume":"28","year":"2005","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"key2021043008575544000_ref014","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.compbiomed.2011.11.010","article-title":"Identifying patients in target customer segments using a two-stage clustering-classification approach: a hospital-based assessment","volume":"42","year":"2012","journal-title":"Computers in Biology and Medicine"},{"key":"key2021043008575544000_ref015","first-page":"353","volume-title":"Clustering Method Using Item Preference Based on RFM for Recommendation System in U-Commerce","year":"2013"},{"issue":"1\/4","key":"key2021043008575544000_ref016","first-page":"395","article-title":"Customer needs as moving targets of product development: a review","volume":"48","year":"2010","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"1","key":"key2021043008575544000_ref017","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11634-006-0004-6","article-title":"Adaptive dissimilarity index for measuring time series proximity","volume":"1","year":"2007","journal-title":"Advances in Data Analysis and Classification"},{"issue":"1","key":"key2021043008575544000_ref018","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1016\/j.jbusres.2012.09.024","article-title":"Data accuracy's impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees","volume":"67","year":"2014","journal-title":"Journal of Business Research"},{"key":"key2021043008575544000_ref019","article-title":"The impact of customer relationship management (CRM) practices on customer satisfaction","volume-title":"Business Governance and Society","year":"2019"},{"key":"key2021043008575544000_ref020","first-page":"34","volume-title":"Clustering Indices","year":"2013"},{"issue":"2","key":"key2021043008575544000_ref021","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.14778\/1454159.1454226","article-title":"Querying and mining of time series data: experimental comparison of representations and distance measures","volume":"1","year":"2008","journal-title":"Proceedings of the VLDB Endowment"},{"key":"key2021043008575544000_ref022","first-page":"1","article-title":"Customer segmentation by using RFM model and clustering methods: a case study in retail industry","volume":"8","year":"2018","journal-title":"International Journal of Contemporary Economics and Administrative Sciences"},{"key":"key2021043008575544000_ref023","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.tmp.2016.03.001","article-title":"Using data mining techniques for profiling profitable hotel customers: an application of RFM analysis","volume":"18","year":"2016","journal-title":"Tourism Management Perspectives"},{"issue":"5","key":"key2021043008575544000_ref024","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1108\/K-07-2015-0172","article-title":"Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods","volume":"45","year":"2016","journal-title":"Kybernetes"},{"issue":"4","key":"key2021043008575544000_ref025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/147078530204400402","article-title":"Needs-based segmentation: principles and practice","volume":"44","year":"2002","journal-title":"International Journal of Market Research"},{"issue":"3","key":"key2021043008575544000_ref026","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.aei.2006.12.001","article-title":"Applying knowledge engineering techniques to customer analysis in the service industry","volume":"21","year":"2007","journal-title":"Advanced Engineering Informatics"},{"key":"key2021043008575544000_ref027","first-page":"372","article-title":"Keeping track of customer life cycle to build customer relationship","volume-title":"International Conference on Advanced Data Mining and Applications","year":"2006"},{"key":"key2021043008575544000_ref028","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/S0360-8352(02)00141-9","article-title":"Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case","volume":"43","year":"2002","journal-title":"Computers and Industrial Engineering"},{"key":"key2021043008575544000_ref029","volume-title":"Data Mining: Concepts and Techniques: Concepts and Techniques","year":"2011"},{"issue":"3","key":"key2021043008575544000_ref030","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1057\/jma.2015.10","article-title":"New approach to customer segmentation based on changes in customer value","volume":"3","year":"2015","journal-title":"Journal of Marketing Analytics"},{"key":"key2021043008575544000_ref031","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.knosys.2014.02.009","article-title":"Discovering valuable frequent patterns based on RFM analysis without customer identification information","volume":"61","year":"2014","journal-title":"Knowledge-Based Systems"},{"key":"key2021043008575544000_ref032","volume-title":"Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program","year":"2000"},{"key":"key2021043008575544000_ref033","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1108\/09696471111103731","article-title":"Role of knowledge management and analytical CRM in business: data mining based framework","volume":"18","year":"2011","journal-title":"The Learning Organization"},{"key":"key2021043008575544000_ref034","first-page":"273","article-title":"Distance measures for effective clustering of ARIMA time-series. data mining","volume-title":"ICDM 2001, Proceedings IEEE International Conference on, 2001","year":"2001"},{"key":"key2021043008575544000_ref035","volume-title":"Data Mining: concepts, Models, Methods, and Algorithms","year":"2011"},{"key":"key2021043008575544000_ref036","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.procs.2011.01.011","article-title":"Estimating customer future value of different customer segments based on adapted RFM model in retail banking context","volume":"3","year":"2011","journal-title":"Procedia Computer Science"},{"issue":"3","key":"key2021043008575544000_ref037","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/08911762.2013.878428","article-title":"A new application of RFM clustering for guild segmentation to mine the pattern of using banks\u2019 e-Payment services","volume":"27","year":"2014","journal-title":"Journal of Global Marketing"},{"issue":"5","key":"key2021043008575544000_ref038","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.omega.2010.11.002","article-title":"Optimal pricing and production decisions in the presence of symmetrical and asymmetrical substitution","volume":"39","year":"2011","journal-title":"Omega"},{"issue":"1","key":"key2021043008575544000_ref039","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.eswa.2005.09.004","article-title":"Customer segmentation and strategy development based on customer lifetime value: a case study","volume":"31","year":"2006","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"key2021043008575544000_ref040","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1111\/j.1468-0394.2005.00310.x","article-title":"Detecting the change of customer behavior based on decision tree analysis","volume":"22","year":"2005","journal-title":"Expert Systems"},{"key":"key2021043008575544000_ref041","volume-title":"Collaborative Customer Relationship Management: taking CRM to the Next Level","year":"2004"},{"key":"key2021043008575544000_ref042","volume-title":"Customer Relationship Management: Concept, Strategy, and Tools","year":"2018"},{"issue":"1","key":"key2021043008575544000_ref043","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijresmar.2011.06.003","article-title":"Dynamics in the international market segmentation of new product growth","volume":"29","year":"2012","journal-title":"International Journal of Research in Marketing"},{"issue":"2\/3","key":"key2021043008575544000_ref044","first-page":"55","article-title":"Customer lifetime value as the basis of customer segmentation: Issues and challenges","volume":"5","year":"2006","journal-title":"Journal of Relationship Marketing"},{"key":"key2021043008575544000_ref045","article-title":"Examining business value of customer relationship management systems: IT usage and two-stage model perspectives","year":"2018","journal-title":"Information and Management"},{"key":"key2021043008575544000_ref046","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1108\/K-10-2016-0302","article-title":"Top 10 data mining techniques in business applications: a brief survey","volume":"46","year":"2017","journal-title":"Kybernetes"},{"issue":"3","key":"key2021043008575544000_ref047","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.jretai.2013.04.001","article-title":"Capturing the evolution of customer\u2013firm relationships: how customers become more (or less) valuable over time","volume":"89","year":"2013","journal-title":"Journal of Retailing"},{"key":"key2021043008575544000_ref048","first-page":"262","volume-title":"Combining Time Series and Clustering to Extract Gamer Profile Evolution","year":"2014"},{"key":"key2021043008575544000_ref049","first-page":"149","article-title":"Using the OWA operator in the Minkowski distance","volume":"3","year":"2008","journal-title":"International Journal of Computer Science"},{"key":"key2021043008575544000_ref050","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.asoc.2016.11.024","article-title":"Distance measures, weighted averages, OWA operators and bonferroni means","volume":"50","year":"2017","journal-title":"Applied Soft Computing"},{"key":"key2021043008575544000_ref051","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1142\/S0218488513500268","article-title":"Generalized moving averages, distance measures and OWA operators","volume":"21","year":"2013","journal-title":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"},{"issue":"1","key":"key2021043008575544000_ref052","first-page":"1","article-title":"Tsclust: an r package for time series clustering","volume":"62","year":"2014","journal-title":"Journal of Statistical Software"},{"key":"key2021043008575544000_ref053","first-page":"86","article-title":"Algorithms for hierarchical clustering: an overview","volume":"2","year":"2012","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"3","key":"key2021043008575544000_ref054","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s00357-014-9161-z","article-title":"Ward\u2019s hierarchical agglomerative clustering method: which algorithms implement ward\u2019s criterion?","volume":"31","year":"2014","journal-title":"Journal of Classification"},{"issue":"2","key":"key2021043008575544000_ref055","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1287\/mksc.1070.0294","article-title":"A hidden Markov model of customer relationship dynamics","volume":"27","year":"2008","journal-title":"Marketing Science"},{"issue":"2","key":"key2021043008575544000_ref056","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.eswa.2008.02.021","article-title":"Application of data mining techniques in customer relationship management: a literature review and classification","volume":"36","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"key2021043008575544000_ref057","first-page":"25","article-title":"Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value","volume":"5","year":"2012","journal-title":"Journal of Optimization in Industrial Engineering"},{"key":"key2021043008575544000_ref058","first-page":"197","article-title":"Combining data mining and group decision making in retailer segmentation based on LRFMP variables","volume":"25","year":"2014","journal-title":"International Journal of Industrial Engineering and Production Research"},{"key":"key2021043008575544000_ref059","article-title":"Customer relationship management: emerging practice, process, and discipline","volume":"3","year":"2001","journal-title":"Journal of Economic and Social Research"},{"issue":"2","key":"key2021043008575544000_ref060","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1108\/TR-01-2017-0011","article-title":"Customer relationship management research in tourism and hospitality: a state-of-the-art","volume":"72","year":"2017","journal-title":"Tourism Review"},{"issue":"3","key":"key2021043008575544000_ref061","first-page":"10","article-title":"Addressing big data time series: mining trillions of time series subsequences under dynamic time warping","volume":"7","year":"2013","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"key2021043008575544000_ref062","first-page":"1","article-title":"Discovering playing patterns: Time series clustering of free-to-play game data","volume-title":"EEE Conference on Computational Intelligence and Games (CIG), 20-23 Sept. 2016","year":"2016"},{"issue":"1","key":"key2021043008575544000_ref063","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","year":"1978","journal-title":"IEEE Transactions on Acoustics, Speech, and Signal Processing"},{"key":"key2021043008575544000_ref064","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1108\/MIP-11-2016-0210","article-title":"LRFMP model for customer segmentation in the grocery retail industry: a case study","volume":"35","year":"2017","journal-title":"Marketing Intelligence and Planning"},{"key":"key2021043008575544000_ref065","volume-title":"Data Mining for Business Analytics: concepts, Techniques, and Applications in R","year":"2017"},{"issue":"3","key":"key2021043008575544000_ref066","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0957-4174(01)00037-9","article-title":"Mining the change of customer behavior in an internet shopping mall","volume":"21","year":"2001","journal-title":"Expert Systems with Applications"},{"key":"key2021043008575544000_ref067","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.knosys.2017.05.027","article-title":"Statistics-based CRM approach via time series segmenting RFM on large scale data","volume":"132","year":"2017","journal-title":"Knowledge-Based Systems"},{"key":"key2021043008575544000_ref068","volume-title":"Accelerating Customer Relationships: Using CRM and Relationship Technologies","year":"2001"},{"key":"key2021043008575544000_ref069","volume-title":"Introduction to Data Mining","year":"2006"},{"key":"key2021043008575544000_ref070","volume-title":"Data Mining Techniques in CRM: Inside Customer Segmentation","year":"2010"},{"issue":"4","key":"key2021043008575544000_ref071","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.eswa.2004.12.041","article-title":"Visualization method for customer targeting using customer map","volume":"28","year":"2005","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"key2021043008575544000_ref072","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TFUZZ.2008.917299","article-title":"Time series smoothing and OWA aggregation","volume":"16","year":"2008","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"7","key":"key2021043008575544000_ref073","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1016\/j.eswa.2014.12.022","article-title":"A decision-making framework for precision marketing","volume":"42","year":"2015","journal-title":"Expert Systems with Applications"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-09-2018-0506\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-09-2018-0506\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:50:07Z","timestamp":1753393807000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/k\/article\/50\/2\/221-242\/263086"}},"subtitle":["A case study on a bank\u2019s customers"],"short-title":[],"issued":{"date-parts":[[2019,7,30]]},"references-count":73,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,7,30]]},"published-print":{"date-parts":[[2021,3,27]]}},"alternative-id":["10.1108\/K-09-2018-0506"],"URL":"https:\/\/doi.org\/10.1108\/k-09-2018-0506","relation":{},"ISSN":["0368-492X","0368-492X"],"issn-type":[{"value":"0368-492X","type":"print"},{"value":"0368-492X","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,30]]}}}