Estimating customer future value of different customer segments based on adapted RFM model in retail banking context

Khajvand, Mahboubeh; Tarokh, Mohammad Jafar

doi:10.1016/j.procs.2011.01.011

Cited by 82 publications

(52 citation statements)

References 9 publications

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“…Huang & Gartner (2012) regarded Sales Promotion as a direct stimulus to extra value of a product or an incentive to final consumers, salespeople, or distributors. Khajvand & Tarokh (2011) mentioned that Sales Promotion was composed of various temporary incentives, mainly to induce consumers or dealers purchasing certain products in advance or purchasing more quantity. indicated that Sales Promotion was essentially a complimentary marketing effort, being practiced in limited time and tending to stimulate consumer purchase.…”

Section: Sales Promotionmentioning

confidence: 99%

A Study on the Effects of Sales Promotion on Consumer Involvement and Purchase Intention in Tourism Industry

Chang

2017

EURASIA J MATH SCI T

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Section: Sales Promotionmentioning

confidence: 99%

A Study on the Effects of Sales Promotion on Consumer Involvement and Purchase Intention in Tourism Industry

Chang

2017

EURASIA J MATH SCI T

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“…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%

“…To analyze the clusters and to measure the value of the customers in each cluster, they used RFM (recency, frequency, and monetary value) model. 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).…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Finally, the obtained results along with the strategies that are to be applied by the company in various clusters are studied. Khajvand and Tarokh (2011) presented a model where the customers' loyalty rate is calculated after studying their history in different periods and their behaviors in the future is estimated. This framework consists of 7 phases.…”

Section: The Extended Rfm Modelmentioning

confidence: 99%

Measuring customer loyalty using an extended RFM and clustering technique

Zalaghi¹,

Varzi²

2014

10.5267/j.msl

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Today, the ability to identify the profitable customers, creating a long-term loyalty in them and expanding the existing relationships are considered as the key and competitive factors for a customer-oriented organization. The prerequisite for having such competitive factors is the presence of a very powerful customer relationship management (CRM). The accurate evaluation of customers' profitability is considered as one of the fundamental reasons that lead to a successful customer relationship management. RFM is a method that scrutinizes three properties, namely recency, frequency and monetary for each customer and scores customers based on these properties. In this paper, a method is introduced that obtains the behavioral traits of customers using the extended RFM approach and having the information related to the customers of an organization; it then classifies the customers using the K-means algorithm and finally scores the customers in terms of their loyalty in each cluster. In the suggested approach, first the customers' records will be clustered and then the RFM model items will be specified through selecting the effective properties on the customers' loyalty rate using the multipurpose genetic algorithm. Next, they will be scored in each cluster based on the effect that they have on the loyalty rate. The influence rate each property has on loyalty is calculated using the Spearman's correlation coefficient.

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Estimating customer future value of different customer segments based on adapted RFM model in retail banking context

Cited by 82 publications

References 9 publications

A Study on the Effects of Sales Promotion on Consumer Involvement and Purchase Intention in Tourism Industry

A Study on the Effects of Sales Promotion on Consumer Involvement and Purchase Intention in Tourism Industry

Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

Measuring customer loyalty using an extended RFM and clustering technique

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