Customer segmentation by web content mining
This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track...
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| Vydáno v: | Journal of retailing and consumer services Ročník 61; s. 102588 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2021
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| ISSN: | 0969-6989, 1873-1384 |
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| Abstract | This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track and discern changes in customer purchasing behaviors during their whole shopping cycle. Firstly, a web content retrieving system was developed to fetch publicly available customer data on a retailer's website, including demographic information (gender, age, location, etc.) and product information (name, price, date, etc.) of each purchase in a period from 2008 to 2019. The RFMT values of a customer were then computed from the retrieved data and subsequently analyzed by the hierarchical clustering to derive seven homogeneous clusters with specific customer profiles. Subsequently, demographic features and product preferences were identified for each cluster with business insights that can help the retailer to improve customer relationships and to implement targeted recommendation strategies.
•Expanding the traditional RFM model to the RFMT model by introducing a new dimension, interpurchase time, to include information on shopping regularity in a long-term period.•Deriving seven characteristic customer clusters from a large dataset retrieved on a global retailer's website based on the RFMT model.•Profiling customer purchasing behaviors by the cluster analysis. |
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| AbstractList | This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track and discern changes in customer purchasing behaviors during their whole shopping cycle. Firstly, a web content retrieving system was developed to fetch publicly available customer data on a retailer's website, including demographic information (gender, age, location, etc.) and product information (name, price, date, etc.) of each purchase in a period from 2008 to 2019. The RFMT values of a customer were then computed from the retrieved data and subsequently analyzed by the hierarchical clustering to derive seven homogeneous clusters with specific customer profiles. Subsequently, demographic features and product preferences were identified for each cluster with business insights that can help the retailer to improve customer relationships and to implement targeted recommendation strategies.
•Expanding the traditional RFM model to the RFMT model by introducing a new dimension, interpurchase time, to include information on shopping regularity in a long-term period.•Deriving seven characteristic customer clusters from a large dataset retrieved on a global retailer's website based on the RFMT model.•Profiling customer purchasing behaviors by the cluster analysis. |
| ArticleNumber | 102588 |
| Author | Xu, Bugao Wei, Jinliang Zhou, Jinfeng |
| Author_xml | – sequence: 1 givenname: Jinfeng surname: Zhou fullname: Zhou, Jinfeng email: Jinfengzhou@my.unt.edu organization: Department of Merchandising and Digital Retailing, University of North Texas, Denton,410 Ave. C, Chilton Hall 342J, Denton, TX, 76201, United States – sequence: 2 givenname: Jinliang surname: Wei fullname: Wei, Jinliang email: jinliangwii@hotmail.com organization: Department of Computer Science and Engineering, University of North Texas, Denton, 1504 W. Mulberry St., Science Research Building 275, Denton, TX, 76201, United States – sequence: 3 givenname: Bugao orcidid: 0000-0001-9221-5110 surname: Xu fullname: Xu, Bugao email: Bugao.Xu@unt.edu, bugao.xu@unt.edu organization: Department of Merchandising and Digital Retailing, University of North Texas, Denton,410 Ave. C, Chilton Hall 342J, Denton, TX, 76201, United States |
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| Cites_doi | 10.1186/s12859-017-1965-5 10.1016/j.eswa.2008.07.018 10.1111/jbl.12120 10.1108/10610420210435443 10.17535/crorr.2018.0020 10.1016/j.jclepro.2018.09.189 10.1109/TPAMI.2002.1114856 10.1016/j.future.2017.02.031 10.3758/PBR.15.6.1209 10.1016/j.eswa.2011.11.066 10.1016/j.eswa.2008.08.059 10.1016/S0022-4359(02)00068-4 10.1177/002224376600300309 10.1108/03090560810840925 10.1016/j.eswa.2011.05.034 10.30646/sinus.v18i1.448 10.1504/IJEBR.2017.085549 |
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| Keywords | Customer segmentation Interpurchase time Hierarchical clustering Web content mining |
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| References | Morrison (bib29) 1966; 3 Meyer‐Waarden (bib27) 2008; 42 Wei, Lin, Wu (bib41) 2010; 4 Alvandi, Fazli, Abdoli (bib2) 2012; 3 Miglautsch (bib28) 2000; 8 Yeh, Yang, Ting (bib44) 2009; 36 Velotio Technologies (bib38) 2019 Maryani, Riana, Astuti, Ishaq, Pratama (bib24) 2018 Seif (bib34) 2018 Lin, C. F., 2002. Segmenting customer brand preference: demographic or psychographic. J. Prod. Brand. Manag. 11(4), 249-268. https://doi.org/10.1108/10610420210435443. Vakratsas, Bass (bib37) 2002; 78 Ouellette (bib30) 2020 Scitovski, Sušac, Has (bib33) 2018; 9 Sharma, López, Tsunoda (bib35) 2017; 18 Guo (bib11) 2009; 36 Huseynov, Yıldırım (bib17) 2017; 14 Wei, Lin, Weng, Wu (bib42) 2012; 39 Garbade (bib10) 2018 Chang, Tsai (bib6) 2011; 38 Shihab, Afroge, Mishu (bib36) 2019 Elrod, Stanley, Cudney, Fisher (bib8) 2015; 7 Widyawati, Saptomo, Utami (bib43) 2020; 18 Kim (bib19) 2013 Maryani, Riana (bib23) 2017 Liu, Li, Xiong, Gao, Wu (bib21) 2010 Yoseph, Heikkila (bib45) 2018 Guo, Gao, Liu, Wu (bib1) 2018; 78 Hughes (bib14) 1994 Bushery (bib5) 2020 Christy, Umamakeswari, Priyatharsini, Neyaa (bib7) 2018 Hung, Lien, Ngoc (bib16) 2019 Hui (bib15) 2019 Hood, Clarke, Clarke (bib13) 2016; 26 Kadir, Achyar (bib18) 2019 Haider, Zafar, Khalid, Majid, Abdullah, Sarwar (bib12) 2017 mahmoud Taher, Elzanfaly, Salama (bib22) 2016; 4 Wei (bib40) 2020 Praveen, Rama (bib32) 2018; 9 Massimino (bib25) 2016; 37 An, Kwak, Jung, Salminen, Jansen (bib3) 2018; 8 Anitha, Patil (bib4) 2019 Patlolla (bib31) 2018 Young (bib46) 2020 Maulik, Bandyopadhyay (bib26) 2002; 24 Farrell, Ludwig (bib9) 2008; 15 Wang, Somogyi (bib39) 2019; 206 Elrod (10.1016/j.jretconser.2021.102588_bib8) 2015; 7 Farrell (10.1016/j.jretconser.2021.102588_bib9) 2008; 15 Scitovski (10.1016/j.jretconser.2021.102588_bib33) 2018; 9 Young (10.1016/j.jretconser.2021.102588_bib46) 2020 Maryani (10.1016/j.jretconser.2021.102588_bib23) 2017 Guo (10.1016/j.jretconser.2021.102588_bib11) 2009; 36 Maulik (10.1016/j.jretconser.2021.102588_bib26) 2002; 24 Anitha (10.1016/j.jretconser.2021.102588_bib4) 2019 Kim (10.1016/j.jretconser.2021.102588_bib19) 2013 Yeh (10.1016/j.jretconser.2021.102588_bib44) 2009; 36 10.1016/j.jretconser.2021.102588_bib20 Kadir (10.1016/j.jretconser.2021.102588_bib18) 2019 Garbade (10.1016/j.jretconser.2021.102588_bib10) Hui (10.1016/j.jretconser.2021.102588_bib15) mahmoud Taher (10.1016/j.jretconser.2021.102588_bib22) 2016; 4 Patlolla (10.1016/j.jretconser.2021.102588_bib31) Sharma (10.1016/j.jretconser.2021.102588_bib35) 2017; 18 Widyawati (10.1016/j.jretconser.2021.102588_bib43) 2020; 18 An (10.1016/j.jretconser.2021.102588_bib3) 2018; 8 Huseynov (10.1016/j.jretconser.2021.102588_bib17) 2017; 14 Wang (10.1016/j.jretconser.2021.102588_bib39) 2019; 206 Liu (10.1016/j.jretconser.2021.102588_bib21) 2010 Maryani (10.1016/j.jretconser.2021.102588_bib24) 2018 Velotio Technologies (10.1016/j.jretconser.2021.102588_bib38) Seif (10.1016/j.jretconser.2021.102588_bib34) Guo (10.1016/j.jretconser.2021.102588_bib1) 2018; 78 Haider (10.1016/j.jretconser.2021.102588_bib12) 2017 Vakratsas (10.1016/j.jretconser.2021.102588_bib37) 2002; 78 Wei (10.1016/j.jretconser.2021.102588_bib40) Wei (10.1016/j.jretconser.2021.102588_bib41) 2010; 4 Hung (10.1016/j.jretconser.2021.102588_bib16) 2019 Meyer‐Waarden (10.1016/j.jretconser.2021.102588_bib27) 2008; 42 Morrison (10.1016/j.jretconser.2021.102588_bib29) 1966; 3 Alvandi (10.1016/j.jretconser.2021.102588_bib2) 2012; 3 Bushery (10.1016/j.jretconser.2021.102588_bib5) Miglautsch (10.1016/j.jretconser.2021.102588_bib28) 2000; 8 Massimino (10.1016/j.jretconser.2021.102588_bib25) 2016; 37 Hood (10.1016/j.jretconser.2021.102588_bib13) 2016; 26 Christy (10.1016/j.jretconser.2021.102588_bib7) 2018 Wei (10.1016/j.jretconser.2021.102588_bib42) 2012; 39 Chang (10.1016/j.jretconser.2021.102588_bib6) 2011; 38 Shihab (10.1016/j.jretconser.2021.102588_bib36) 2019 Ouellette (10.1016/j.jretconser.2021.102588_bib30) 2020 Yoseph (10.1016/j.jretconser.2021.102588_bib45) 2018 Praveen (10.1016/j.jretconser.2021.102588_bib32) 2018; 9 Hughes (10.1016/j.jretconser.2021.102588_bib14) 1994 |
| References_xml | – volume: 4 start-page: 5 year: 2016 end-page: 10 ident: bib22 article-title: Investigation in customer value segmentation quality under different preprocessing types of RFM attributes publication-title: iJES – year: 2018 ident: bib7 article-title: RFM ranking–An effective approach to customer segmentation publication-title: J. King Saud Univ., Comp. & Info. Sci. – volume: 9 start-page: 255 year: 2018 end-page: 268 ident: bib33 article-title: Searching for an optimal partition of incomplete data with application in modeling energy efficiency of public buildings publication-title: Croat. Oper. Res. Rev. – year: 2019 ident: bib18 article-title: Customer Segmentation on Online Retail Using RFM Analysis: Big Data Case of Bukku. Id – volume: 39 start-page: 5529 year: 2012 end-page: 5533 ident: bib42 article-title: A case study of applying LRFM model in market segmentation of a children's dental clinic publication-title: Expert Syst. Appl. – volume: 15 start-page: 1209 year: 2008 end-page: 1217 ident: bib9 article-title: Bayesian and maximum likelihood estimation of hierarchical response time models publication-title: Psychon. Bull. Rev. – volume: 78 start-page: 451 year: 2018 end-page: 461 ident: bib1 article-title: Recommend products with consideration of multi-category inter-purchase time and price publication-title: Future Generat. Comput. Syst. – start-page: 1 year: 2017 end-page: 6 ident: bib23 article-title: Clustering and profiling of customers using RFM for customer relationship management recommendations publication-title: 2017 5th International Conference on Cyber and I – volume: 3 start-page: 289 year: 1966 end-page: 291 ident: bib29 article-title: Interpurchase time and brand loyalty publication-title: J. Mar. Res. – volume: 4 start-page: 4199 year: 2010 end-page: 4206 ident: bib41 article-title: A review of the application of RFM model publication-title: Afr.J.Bus.Manage. – volume: 36 start-page: 5866 year: 2009 end-page: 5871 ident: bib44 article-title: Knowledge discovery on RFM model using Bernoulli sequence publication-title: Expert Syst. Appl. – year: 2020 ident: bib46 article-title: U.S. ecommerce sales grow 14.9% in 2019 – volume: 8 start-page: 1 year: 2018 end-page: 19 ident: bib3 article-title: Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data publication-title: Soc. Netw. Anal. Min. – start-page: 1 year: 2013 end-page: 18 ident: bib19 article-title: Understanding online consumer's inter-purchase time publication-title: INFORMS CIST – volume: 3 start-page: 2294 year: 2012 end-page: 2302 ident: bib2 article-title: K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services publication-title: Int. Res. Judgement – year: 2019 ident: bib38 article-title: Web scraping: introduction, best practices & caveats – year: 2018 ident: bib10 article-title: Understanding K-means clustering in machine learning – volume: 18 start-page: 546 year: 2017 ident: bib35 article-title: Divisive hierarchical maximum likelihood clustering publication-title: BMC Bioinf. – year: 2018 ident: bib34 article-title: The 5 clustering algorithms data scientists need to know. Medium – volume: 78 start-page: 119 year: 2002 end-page: 129 ident: bib37 article-title: The relationship between purchase regularity and propensity to accelerate publication-title: J. Retailing – start-page: 1 year: 2018 end-page: 6 ident: bib24 article-title: Customer segmentation based on RFM model and clustering techniques with K-means algorithm publication-title: 2018 Third International Conference on Informatics and Computing – year: 2019 ident: bib15 article-title: Machine learning-Expectation-Maximization algorithm (E.M.) – volume: 14 start-page: 12 year: 2017 end-page: 28 ident: bib17 article-title: Behavioural segmentation analysis of online consumer audience in Turkey by using real e-commerce transaction data publication-title: Int. J. Econ. Bus. Res. – start-page: 33 year: 2019 end-page: 37 ident: bib16 article-title: Customer segmentation using hierarchical agglomerative clustering publication-title: Proceedings of the 2nd International Conference on Information Science and Systems – start-page: 108 year: 2018 end-page: 116 ident: bib45 article-title: Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method publication-title: 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) – volume: 36 start-page: 6301 year: 2009 end-page: 6308 ident: bib11 article-title: A multi-category inter-purchase time model based on hierarchical Bayesian theory publication-title: Expert Syst. Appl. – year: 2020 ident: bib5 article-title: Behavioral segmentation examples and insights – volume: 37 start-page: 34 year: 2016 end-page: 42 ident: bib25 article-title: Accessing online data: web‐crawling and information‐scraping techniques to automate the assembly of research data publication-title: J. Bus. Logist. – volume: 18 start-page: 75 year: 2020 end-page: 87 ident: bib43 article-title: Penerapan agglomerative hierarchical clustering untuk segmentasi pelanggan publication-title: J.ILM.Sinus. – reference: Lin, C. F., 2002. Segmenting customer brand preference: demographic or psychographic. J. Prod. Brand. Manag. 11(4), 249-268. https://doi.org/10.1108/10610420210435443. – year: 2018 ident: bib31 article-title: Understanding the concepts of Hierarchical clustering technique – year: 2019 ident: bib4 article-title: RFM model for customer purchase behavior using K-Means algorithm publication-title: J. King Saud Univ., Comp. & Info. Sci. – volume: 42 start-page: 87 year: 2008 end-page: 114 ident: bib27 article-title: The influence of loyalty programme membership on customer purchase behaviour publication-title: Eur.J.Mark. – volume: 9 start-page: 324 year: 2018 end-page: 327 ident: bib32 article-title: Improving efficiency and effectiveness of hierarchical clustering publication-title: Int. J.Adv. – volume: 38 start-page: 14499 year: 2011 end-page: 14513 ident: bib6 article-title: Group RFM analysis as a novel framework to discover better customer consumption behavior publication-title: Expert Syst. Appl. – start-page: 911 year: 2010 end-page: 916 ident: bib21 article-title: Understanding of internal clustering validation measures publication-title: 2010 IEEE International Conference on Data Mining – volume: 7 start-page: 10756 year: 2015 end-page: 10769 ident: bib8 article-title: Empirical study utilizing Q.F.D. to develop an international marketing strategy publication-title: Sustain. Times – start-page: 1 year: 2019 end-page: 4 ident: bib36 article-title: RFM based market segmentation approach using advanced K-means and agglomerative clustering: a comparative study publication-title: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) – volume: 24 start-page: 1650 year: 2002 end-page: 1654 ident: bib26 article-title: Performance evaluation of some clustering algorithms and validity indices publication-title: IEEE Trans. Pattern Anal. – volume: 206 start-page: 966 year: 2019 end-page: 975 ident: bib39 article-title: Consumer adoption of sustainable shellfish in China: effects of psychological factors and segmentation publication-title: J. Clean. Prod. – year: 1994 ident: bib14 article-title: Strategic Database Marketing – year: 2020 ident: bib40 article-title: How to measure clustering performances when there are no ground truth – volume: 26 start-page: 113 year: 2016 end-page: 136 ident: bib13 article-title: Segmenting the growing U.K. convenience store market for retail location planning publication-title: Int. Rev. Retail Distrib. Consum. Res. – volume: 8 start-page: 67 year: 2000 end-page: 72 ident: bib28 article-title: Thoughts on RFM scoring publication-title: J. Database Mark. Cust. Strategy Manag. – year: 2020 ident: bib30 article-title: Online shopping statistics you need to know in 2021 – year: 2017 ident: bib12 article-title: Marketing Management. Head, B, 22 – volume: 18 start-page: 546 issue: 16 year: 2017 ident: 10.1016/j.jretconser.2021.102588_bib35 article-title: Divisive hierarchical maximum likelihood clustering publication-title: BMC Bioinf. doi: 10.1186/s12859-017-1965-5 – volume: 26 start-page: 113 issue: 2 year: 2016 ident: 10.1016/j.jretconser.2021.102588_bib13 article-title: Segmenting the growing U.K. convenience store market for retail location planning publication-title: Int. Rev. Retail Distrib. Consum. Res. – start-page: 1 year: 2019 ident: 10.1016/j.jretconser.2021.102588_bib36 article-title: RFM based market segmentation approach using advanced K-means and agglomerative clustering: a comparative study – volume: 36 start-page: 5866 issue: 3 year: 2009 ident: 10.1016/j.jretconser.2021.102588_bib44 article-title: Knowledge discovery on RFM model using Bernoulli sequence publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.07.018 – volume: 37 start-page: 34 issue: 1 year: 2016 ident: 10.1016/j.jretconser.2021.102588_bib25 article-title: Accessing online data: web‐crawling and information‐scraping techniques to automate the assembly of research data publication-title: J. Bus. Logist. doi: 10.1111/jbl.12120 – ident: 10.1016/j.jretconser.2021.102588_bib20 doi: 10.1108/10610420210435443 – year: 2019 ident: 10.1016/j.jretconser.2021.102588_bib18 – volume: 9 start-page: 255 issue: 2 year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib33 article-title: Searching for an optimal partition of incomplete data with application in modeling energy efficiency of public buildings publication-title: Croat. Oper. Res. Rev. doi: 10.17535/crorr.2018.0020 – volume: 206 start-page: 966 year: 2019 ident: 10.1016/j.jretconser.2021.102588_bib39 article-title: Consumer adoption of sustainable shellfish in China: effects of psychological factors and segmentation publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2018.09.189 – start-page: 1 year: 2017 ident: 10.1016/j.jretconser.2021.102588_bib23 article-title: Clustering and profiling of customers using RFM for customer relationship management recommendations – volume: 9 start-page: 324 issue: 2 year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib32 article-title: Improving efficiency and effectiveness of hierarchical clustering publication-title: Int. J.Adv. – start-page: 1 year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib24 article-title: Customer segmentation based on RFM model and clustering techniques with K-means algorithm – volume: 4 start-page: 4199 issue: 19 year: 2010 ident: 10.1016/j.jretconser.2021.102588_bib41 article-title: A review of the application of RFM model publication-title: Afr.J.Bus.Manage. – volume: 24 start-page: 1650 issue: 12 year: 2002 ident: 10.1016/j.jretconser.2021.102588_bib26 article-title: Performance evaluation of some clustering algorithms and validity indices publication-title: IEEE Trans. Pattern Anal. doi: 10.1109/TPAMI.2002.1114856 – year: 2020 ident: 10.1016/j.jretconser.2021.102588_bib30 – volume: 78 start-page: 451 year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib1 article-title: Recommend products with consideration of multi-category inter-purchase time and price publication-title: Future Generat. Comput. Syst. doi: 10.1016/j.future.2017.02.031 – start-page: 33 year: 2019 ident: 10.1016/j.jretconser.2021.102588_bib16 article-title: Customer segmentation using hierarchical agglomerative clustering – volume: 15 start-page: 1209 issue: 6 year: 2008 ident: 10.1016/j.jretconser.2021.102588_bib9 article-title: Bayesian and maximum likelihood estimation of hierarchical response time models publication-title: Psychon. Bull. Rev. doi: 10.3758/PBR.15.6.1209 – ident: 10.1016/j.jretconser.2021.102588_bib31 – year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib7 article-title: RFM ranking–An effective approach to customer segmentation publication-title: J. King Saud Univ., Comp. & Info. Sci. – year: 2019 ident: 10.1016/j.jretconser.2021.102588_bib4 article-title: RFM model for customer purchase behavior using K-Means algorithm publication-title: J. King Saud Univ., Comp. & Info. Sci. – volume: 39 start-page: 5529 issue: 5 year: 2012 ident: 10.1016/j.jretconser.2021.102588_bib42 article-title: A case study of applying LRFM model in market segmentation of a children's dental clinic publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.11.066 – volume: 36 start-page: 6301 issue: 3 year: 2009 ident: 10.1016/j.jretconser.2021.102588_bib11 article-title: A multi-category inter-purchase time model based on hierarchical Bayesian theory publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.08.059 – volume: 78 start-page: 119 issue: 2 year: 2002 ident: 10.1016/j.jretconser.2021.102588_bib37 article-title: The relationship between purchase regularity and propensity to accelerate publication-title: J. Retailing doi: 10.1016/S0022-4359(02)00068-4 – volume: 3 start-page: 2294 issue: 11 year: 2012 ident: 10.1016/j.jretconser.2021.102588_bib2 article-title: K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services publication-title: Int. Res. Judgement – volume: 8 start-page: 67 issue: 1 year: 2000 ident: 10.1016/j.jretconser.2021.102588_bib28 article-title: Thoughts on RFM scoring publication-title: J. Database Mark. Cust. Strategy Manag. – ident: 10.1016/j.jretconser.2021.102588_bib40 – ident: 10.1016/j.jretconser.2021.102588_bib10 – year: 2017 ident: 10.1016/j.jretconser.2021.102588_bib12 – start-page: 911 year: 2010 ident: 10.1016/j.jretconser.2021.102588_bib21 article-title: Understanding of internal clustering validation measures – ident: 10.1016/j.jretconser.2021.102588_bib38 – volume: 7 start-page: 10756 issue: 8 year: 2015 ident: 10.1016/j.jretconser.2021.102588_bib8 article-title: Empirical study utilizing Q.F.D. to develop an international marketing strategy publication-title: Sustain. Times – volume: 3 start-page: 289 issue: 3 year: 1966 ident: 10.1016/j.jretconser.2021.102588_bib29 article-title: Interpurchase time and brand loyalty publication-title: J. Mar. Res. doi: 10.1177/002224376600300309 – start-page: 108 year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib45 article-title: Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method – volume: 8 start-page: 1 issue: 54 year: 2018 ident: 10.1016/j.jretconser.2021.102588_bib3 article-title: Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data publication-title: Soc. Netw. Anal. Min. – year: 2020 ident: 10.1016/j.jretconser.2021.102588_bib46 – ident: 10.1016/j.jretconser.2021.102588_bib15 – volume: 42 start-page: 87 issue: 1/2 year: 2008 ident: 10.1016/j.jretconser.2021.102588_bib27 article-title: The influence of loyalty programme membership on customer purchase behaviour publication-title: Eur.J.Mark. doi: 10.1108/03090560810840925 – ident: 10.1016/j.jretconser.2021.102588_bib34 – volume: 38 start-page: 14499 issue: 12 year: 2011 ident: 10.1016/j.jretconser.2021.102588_bib6 article-title: Group RFM analysis as a novel framework to discover better customer consumption behavior publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.05.034 – start-page: 1 year: 2013 ident: 10.1016/j.jretconser.2021.102588_bib19 article-title: Understanding online consumer's inter-purchase time publication-title: INFORMS CIST – volume: 18 start-page: 75 issue: 1 year: 2020 ident: 10.1016/j.jretconser.2021.102588_bib43 article-title: Penerapan agglomerative hierarchical clustering untuk segmentasi pelanggan publication-title: J.ILM.Sinus. doi: 10.30646/sinus.v18i1.448 – volume: 14 start-page: 12 issue: 1 year: 2017 ident: 10.1016/j.jretconser.2021.102588_bib17 article-title: Behavioural segmentation analysis of online consumer audience in Turkey by using real e-commerce transaction data publication-title: Int. J. Econ. Bus. Res. doi: 10.1504/IJEBR.2017.085549 – ident: 10.1016/j.jretconser.2021.102588_bib5 – year: 1994 ident: 10.1016/j.jretconser.2021.102588_bib14 – volume: 4 start-page: 5 issue: 4 year: 2016 ident: 10.1016/j.jretconser.2021.102588_bib22 article-title: Investigation in customer value segmentation quality under different preprocessing types of RFM attributes publication-title: iJES |
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| SubjectTerms | Customer segmentation Hierarchical clustering Interpurchase time Web content mining |
| Title | Customer segmentation by web content mining |
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