Intelligent profitable customers segmentation system based on business intelligence tools

Lee, Jang Hee; Park, Sang C.

doi:10.1016/j.eswa.2005.01.013

Cited by 126 publications

(60 citation statements)

References 5 publications

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“…; i = 1, 2,..., n (18) ; i = 1, 2,..., n (19) Step 5: Compute similarities to the ideal solution: (20) …”

Section: The Fuzzy Topsis Methodsmentioning

confidence: 99%

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A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems

Ghazanfari¹,

Rouhani²,

Jafari³

2014

Polish Maritime Research

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“…; i = 1, 2,..., n (18) ; i = 1, 2,..., n (19) Step 5: Compute similarities to the ideal solution: (20) …”

Section: The Fuzzy Topsis Methodsmentioning

confidence: 99%

“…Subsequently, the similarities to an ideal solution are solved by means of Eq. (20). Finally, the values of each alternative (PCS) for the final ranking are illustrated in Tab.…”

Section: Evaluating Alternatives and Determining The Final Rankmentioning

confidence: 99%

A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems

Ghazanfari¹,

Rouhani²,

Jafari³

2014

Polish Maritime Research

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show abstract

“…socio-demographic analysis and RFM analysis. Kim et al (2005) and Lee & Park (2005) report good results on customer segmentation modelling using socio-demographic characteristics of customer households (e.g. average size of households, average age of S118 Australasian Journal of Information Systems Singh & Rumantir 2015, vol.…”

Section: Related Workmentioning

confidence: 99%

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

Singh

Rumantir

2015

AJIS

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Almost all the papers reported in the literatures on market segmentation modeling using retail transaction data deal with finding groupings of customers. This paper proposes the application of clustering and classification techniques for finding groupings of retailers who use the Electronic Funds Transfer at Point of Sale (EFTPOS) facilities of a major bank in Australia in their businesses. The RFM (Recency, Frequency, Monetary value) analysis on each retailer is used to reduce the large data set of customer purchases through the EFTPOS network into attributes that may explain the business activities of the retailers. Preliminary results published in Singh, show that groupings of retailers with distinct combinations of RFM values can be established. Encouraged by the promising business insights gained from the clustering experiments using the RFM values, in this paper, we report our findings in extracting business rules pertinent to each cluster. For this purpose, we incorporate attributes of the EFTPOS transaction data in addition to the derived RFM attributes to build a decision tree to facilitate the extraction of the business rules.

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“…For instance, loyalty status is a behavioral variable for market segmentation (Kotler & Armstrong, 2014). Several researchers have used these types of variables in data mining techniques for market segmentation (Kuo et al, 2002;Bloom, 2005;Lee & Park, 2005;Kuo et al, 2006;Huang et al, 2007). Lu and Wu (2009) proposed a customer segmentation method based on customers' transaction patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel model for product bundling and direct marketing in e-commerce based on market segmentation

Beheshtian-Ardakani¹,

Fathian²,

Gholamian³

2018

10.5267/j.dsl

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Nowadays, companies offer product bundles with special discounts in order to sell more products. However, it is important to note that customers show different levels of loyalties to companies, and each segment of the market has unique features, which influences the customers' buying patterns. The primary purpose of this paper is to propose a novel model for product bundling in e-commerce websites by using market segmentation variables and customer loyalty analysis. RFM model is employed to calculate customer loyalty. Subsequently, the customers are grouped based on their loyalty levels. Each group is then divided into different segments based on market segmentation variables. The product bundles are determined for each market segment via clustering algorithms. Apriori algorithm is also used to determine the association rules for each product bundle. Classification models are applied in order to determine which product bundle should be recommended to each customer. The results demonstrate that the silhouette coefficient, support, confidence, and accuracy values are higher when both customer loyalty level and market segmentation variables are used in product bundling. Accordingly, the proposed model increases the chance of success in direct marketing and recommending product bundles to customers.

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Intelligent profitable customers segmentation system based on business intelligence tools

Cited by 126 publications

References 5 publications

A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems

A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

A novel model for product bundling and direct marketing in e-commerce based on market segmentation

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