A case study of applying LRFM model in market segmentation of a children’s dental clinic

Wei, Jo-Ting; Lin, Shu‐Yi; Weng, Chih-Chien; Wu, Hsin-Hung

doi:10.1016/j.eswa.2011.11.066

Cited by 79 publications

(54 citation statements)

References 26 publications

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“…The RFM is a very popular model for customer value analysis and it has been used by many scholars in order to perform market segmentation (Cheng & Chen, 2009;Khajvand & Tarokh, 2011;McCarty & Hastak, 2007). Since RFM analyzes the behavior of the consumers, it can be considered as a behavior-based model (Wei et al, 2012;Yeh et al, 2008). Wei et al (2012) adopted self-organizing maps (SOM) methodology in order to extend the RFM model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…SOM is a neural network method which can be used for clustering problems, visualization, and market screening (Fish & Ruby, 2009;Hanafizadeh & Mirzazadeh, 2011;Hsu et al, 2009;Hung & Tsai, 2008;Kiang & Fisher, 2008;Wang, 2001;Wei et al, 2012). SOM also has the advantage of automatically detecting strong features in large data sets (Wei et al, 2012). Wei et al (2012) used an extended model which is called LRFM (length, recency, frequency, and monetary value) to perform customer segmentation in a children's dental clinic in Taiwan.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Customer segmentation and clustering can be performed according to disparate criteria, including but not limited to profitability (Zeithaml et al, 2001), customer behavior (Ghazanfari et al, 2010;Gough & Sozou, 2005;Neal, 1998), degree of customer loyalty (Ansari & Riasi, 2016a;Cheng & Chen, 2009;Khajvand & Tarokh, 2011;McCarty & Hastak, 2007;Wei et al, 2012), purchase frequency (Ansari & Riasi, 2016a;Khajvand & Tarokh, 2011;McCarty & Hastak, 2007), purchase volume (Ghazanfari et al, 2010;Zeithaml et al, 2001), demographics (Dehghanpour & Rezvani, 2015;Gough & Sozou, 2005) and etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since RFM analyzes the behavior of the consumers, it can be considered as a behavior-based model (Wei et al, 2012;Yeh et al, 2008). Wei et al (2012) adopted self-organizing maps (SOM) methodology in order to extend the RFM model. SOM is a neural network method which can be used for clustering problems, visualization, and market screening (Fish & Ruby, 2009;Hanafizadeh & Mirzazadeh, 2011;Hsu et al, 2009;Hung & Tsai, 2008;Kiang & Fisher, 2008;Wang, 2001;Wei et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wei et al (2012) adopted self-organizing maps (SOM) methodology in order to extend the RFM model. SOM is a neural network method which can be used for clustering problems, visualization, and market screening (Fish & Ruby, 2009;Hanafizadeh & Mirzazadeh, 2011;Hsu et al, 2009;Hung & Tsai, 2008;Kiang & Fisher, 2008;Wang, 2001;Wei et al, 2012). SOM also has the advantage of automatically detecting strong features in large data sets (Wei et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

Ansari

Riasi

2016

IJBM

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Purpose-The goal of this study is to identify the main clusters of bank customers in order to help commercial banks to better identify their customers and design more efficient marketing strategies.Design/methodology/approach -Data from 250 bank customers were analyzed by using two-step scalable clustering.Findings -Five different clusters of bank customers were identified, namely, favorite customers, creditworthy customers, non-creditworthy customers, passers, and friends. The findings indicated that disparate clusters of bank customers are extremely different based on their loan amount, default risk, account balance, degree of loyalty and profitability for the bank.Practical implications -The differences which were observed between these five clusters of bank customers accentuate the importance of customer clustering and market segmentation in the financial services industry. Customer clustering can help financial institutions to augment their competitiveness by shifting from traditional marketing strategies to target marketing and segmentation-based marketing approaches.Originality/value -The most important contribution of this study is the incorporation of a wide range of factors that can potentially affect customer clustering in the analysis, whereas, the majority of previous studies only focused on a limited number of variables in order to determine the customer clusters. Specifically, the customer clustering in this study was performed by using demographic variables, profitability, loan amount, default risk, account balance, loyalty, account type, account closure history, customer location, and account currency.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

Ansari

Riasi

2016

IJBM

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SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

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New business models underpinned by new standards of openness, flexibility, and agility are arising in the banking sector. Therefore, banks have to establish new strategies to keep and to extend their client base, especially with the explosive growth of customers' data and interaction touch-points. Hence, banks and fintechs are in fierce competition to transform customers' data that incorporate their transactions into pertinent and significant knowledge for decision making. In this article, we present a novel system aimed toward solving a long-standing and challenging issue: obtaining classifiers to automatically categorize bank transactions for a Smart Budget Manager. We fit and test our system using real data. The strength of our system lies in the novel combination of incremental machine learning algorithms with a natural language understanding for a fine-grained categorization of bank transactions. Our system serves as a base layer for other advanced banking applications such as segmentation, next best offers and credit scoring. Our system is deployed in a real banking application of a Tunisian bank and has shown bank and customer's satisfaction.

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Cascaded RFM-Based Fuzzy Clustering Model for Dynamic Customer Segmentation in the Retail Sector

Sobantu,

Isafiade

2024

Communications in Computer and Information Science

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A case study of applying LRFM model in market segmentation of a children’s dental clinic

Cited by 79 publications

References 26 publications

Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Cascaded RFM-Based Fuzzy Clustering Model for Dynamic Customer Segmentation in the Retail Sector

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