Customer segmentation utilization for differentiated approach

Panuš, Jan; Jonášová, Hana; Kantorová, Kateřina; Doležalová, Martina; Horáčková, Kateřina

doi:10.1109/dt.2016.7557178

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…In addition, it is important to recognize that the methods for calculating customer lifetime value will continue to evolve, but the analytical framework proposed provides the marketing and managing professionals with a sustainable opportunity to fulfill the fundamental goal of a business: nurture profitable, long-term relationships with valuable segments of customers. These segments are base for aimed building of relationship with individual groups of customer within CRM, also argued by Panuš and colleagues (Panuš et al 2016). Combination using data mining techniques for synthesis of data gained from ABC and RFM analysis is suitable for utilization within CRM approach to customers.…”

Section: Discussionmentioning

confidence: 96%

“…It refers to use of data from a IT company offering advisory services. It is a paper of Panuš and colleagues (Panuš et al 2016), published in the proceedings of the IEEE International Conference on Information and Digital Technologies. The authors also sustained their research in that the customer value can provide valid knowledge for more targeted and personalized marketing strategies.…”

Section: Crm Analytical Methodsmentioning

confidence: 99%

“…So, based in a case study in the printing industry, this paper presents a proposal of improving the customer knowledge sustained in RFM (Recency, Frequency and Monetary value) and ABC (Pareto Rule) analytical methods. A combination of these data mining methods allows processing data from the customer behavior and can create an effective knowledge base for manager access to CRM system (Kumar and Reinartz 2019;Peppers and Rogers 2017;Panuš et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Learning about the customer for improving customer retention proposal of an analytical framework

Simões

Nogueira

2021

J Market Anal

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The market faces new challenges in retaining customers, since they have very high expectations, which translate into the demand for a swift response and intransigence to empty promises on the part of brands. These requirements result from the ability to disseminate and infuse information, which in turn makes customers more informed, more participative, and more uncompromising. This change in behavior implies redesigning the strategic management of the brands, in terms of the relationship with the customer. In view of this challenge, the relevance of developing an adequate differentiation model for customer retention prevails. Based on this premise, this paper presents a proposal based on RFM and ABC analytical methods applied to customer relationship management and contextualized in a particular case of the printing industry. The proposed model defines a set of metrics aimed at customer segmentation, which improves the customers knowledge. The outcomes will allow to define more assertive marketing strategies for customer loyalty and to increase the volume of a brand's revenue.

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Section: Discussionmentioning

confidence: 96%

Section: Crm Analytical Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning about the customer for improving customer retention proposal of an analytical framework

Simões

Nogueira

2021

J Market Anal

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“…The RFM Model The RFM model is a useful segmentation method for market segmentation through effective analysis [18]. RFM analysis of the three parameters is important for the Recency, F for Frequency, and M for Monetary [9]. In theory RFM analysis is assumed (P).…”

Section: Methodsmentioning

confidence: 99%

“…In a sense segmentation is the process of taking advantage of opportunities by dividing the market into several segments [8]. Customer segmentation and various customer results are useful for an explanation of a company's JOIN (Jurnal Online Informatika) p-ISSN: 2528-1682 e-ISSN: 2527-9165 Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means (Bayu Rizki 1 , Nava Gia Ginasta 2 , Muh Akbar Tamrin 3 , Ali Rahman 4 ) 131 marketing strategy [9]. Moreover, with the cementation of capable subscribers, the company will be able to provide customer groups to maximise profits [10].…”

Section: Introductionmentioning

confidence: 99%

Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means

Rizki

Ginasta

Tamrin

et al. 2020

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It is no doubt that the development of the business world has been progressive. Point of sale is one of the many system used as a means of payment in various existing businesses, especially in heterogeneous markets. The activity of transactions between Point of Sale Systems and Customers occur in the business world. Keep in mind also that one of the main factors of business success, is from customers. There is the need of an attractive strategy and certainly it will be to increase the income and assets of a business. To know that, this research will explore the behavior of customer which is based marketing, through RFM Method (Recency, Frequency and Monetary). The case of this study is in Goldfinger Store. It will do segmentation and also use data mining technique to do clustering by using K-Means with result of loyal and potential customer. The results of segmentation using RFM (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.

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Dynamic Classification of Bank Clients by the Predictability of Their Transactional Behavior

Bezbochina

Stavinova

Kovantsev

et al. 2022

Lecture Notes in Computer Science

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Customer segmentation utilization for differentiated approach

Cited by 6 publications

References 6 publications

Learning about the customer for improving customer retention proposal of an analytical framework

Learning about the customer for improving customer retention proposal of an analytical framework

Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means

Dynamic Classification of Bank Clients by the Predictability of Their Transactional Behavior

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