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Rosset, Saharon; Neumann, Einat; Eick, Uri; Vatnik, Nurit

doi:10.1023/a:1024036305874

Cited by 83 publications

(4 citation statements)

References 13 publications

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“…Customer attraction attempts to attract the target customer segments by motivating customers to place orders through various channels (Cheung, Kwok, Law, & Tsui, 2003;Liao & Chen, 2004). Customer retention refers to the activity of preventing the existing customers from switching to competitors by enhancing the level of customer satisfaction through one-to-one marketing, loyalty program, complaints management, and etc (Chen, Chiu, (Rosset, Neumann, Eick, & Vatnik, 2003).…”

Section: Discussionmentioning

confidence: 99%

A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector

Ravasan

Mansouri

2015

International Journal of Information Systems in the Service Sector

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Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy.

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Section: Discussionmentioning

confidence: 99%

A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector

Ravasan

Mansouri

2015

International Journal of Information Systems in the Service Sector

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“…There are three factors we have to determine in order to calculate the LTV of a customer or customer group (Rosset et al, 2003). The first is the customer's value over time (v[t] for t ≥ 0, where t is time and t=0 is the present).…”

Section: Customer Lifetime Value Calculationmentioning

confidence: 99%

A Suggested Model for Customer Relationship Management with Customer Experience-Oriented Data Optimization in the Mobile Phone Industry

Karaboğa¹

2023

CMUJASR

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This study develops a marketing information system algorithm for determining whether and to what degree customers of mobile phone businesses prefer their own brands after experiencing problems with their current product, or in customer service or repair services during or at the end of an average use period for their product. With a process map and system algorithm, a data analytics model required for creating a satisfied and loyal customer group is described based on analysis results obtained by tracking/analyzing customer experiences in different processes. The article‘s findings can help to optimize product, sales, and service development activities focused on business profitability by highlighting data-driven customer experience tracking systems for business. Keywords Customer experience, CRM, Current customer, System algorithm, Conceptual modeling.

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“…Ao repetir negócios com uma empresa, de forma sistemática, e permanecendo retidos ou leais ao fornecedor, os clientes representam uma continuidade de receitas (Vavra, 1994 Kumar, 2000Rosset et al, 2003;Gupta et al, 2004), Customer Lifetime Value (Berger e Nasr, 1998;Gurau e Ranchhod, 2002;Jain e Singh, 2002;Venkatesan e Kumar, 2003;Kumar et al, 2004;Venkatesan e Kumar, 2004;Fader et al, 2005;Ryals, 2005;Gupta e Lehmann, 2006) ou Customer Equity (Blattberg e Deighton, 1996;Blattberg et al, 2001;Rust et al, 2001;Bell et al, 2002;Rust et al, 2004), o que reflete o valor individual do cliente ao longo do tempo.…”

Section: Valor Do Cliente: Lifetime Value Customer Lifetime Value Ouunclassified

Análise do valor do cliente (lifetime value): um estudo ambientado em uma operadora de planos de saúde

Milan¹,

Prestes²,

Munhoz³

2010

base

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Este artigo apresenta um estudo realizado em uma operadora de planos de saúde com atuação regional (Serra Gaúcha), focada no segmento empresarial, e tem por objetivo analisar a aplicação do método de cálculo do valor do cliente (Lifetime Value) proposta por Rust et al. (2001). O estudo fundamenta-se nos conceitos da importância da retenção de clientes como fator gerador de lucro para a empresa e na proposta da estruturação de uma prática relacional eficiente entre a empresa e seus clientes. Para tanto, foram selecionados os vinte maiores clientes da empresa, de acordo com o faturamento, e coletados os seus respectivos dados financeiros. Foram utilizados os dados financeiros fornecidos pela operadora relativos ao período entre janeiro de 2006 e setembro de 2007. A análise demonstrou que os clientes que geram maior receita para a empresa não são, necessariamente, os de maior Lifetime Value (LTV). Mostrou também que o acompanhamento sistemático do LTV pode ser benéfico para a operadora. Por meio da análise de correlação entre o resultado do cliente para a empresa e o seu LTV, observou-se que há uma correlação positiva forte, com alto nível de significância.

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Untitled

Cited by 83 publications

References 13 publications

A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector

A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector

A Suggested Model for Customer Relationship Management with Customer Experience-Oriented Data Optimization in the Mobile Phone Industry

Análise do valor do cliente (lifetime value): um estudo ambientado em uma operadora de planos de saúde

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