Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

Chen, Daqing; Sain, Sai Laing; Guo, Kun

doi:10.1057/dbm.2012.17

Cited by 209 publications

(104 citation statements)

References 1 publication

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“…Apart CovType and SEP85L, the present approach is also experimented on two other real datasets that are not used in [23]. These datasets are STCO-MR2010 AL MO [27] and OnlineRetail [28] [29]. The method for mining submerging and emerging cuboids is experimented on all these four datasets.…”

Section: Resultsmentioning

confidence: 99%

Searching Data Cube for Submerging and Emerging Cuboids

Phan-Luong

2017

2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA)

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Section: Resultsmentioning

confidence: 99%

Searching Data Cube for Submerging and Emerging Cuboids

Phan-Luong

2017

2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA)

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“…Using RFM analysis, companies can better understand their customers' profitability. As a result, they can adopt appropriate marketing strategies to deal with different customers (Chen et al, 2012). Tsai and Chiu (2004) employed a RFM model to examine the profitability of market segments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tsai and Chiu (2004) employed a RFM model to examine the profitability of market segments. Chen et al (2012) used K-means and decision tree techniques to segment the customers of an online retailer based on RFM model. Shim et al (2012) first determined important customers based on RFM analysis.…”

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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“…This finding is in line with our finding as summarised in Table 1 Clustering is used in market segmentation because the number of possible segments in the industry is usually unknown. Chen, Sain, & Guo (2012) use K-Means clustering algorithm to segment customers of an online retail business. They pre-process the data set using the RFM S119 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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Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

Cited by 209 publications

References 1 publication

Searching Data Cube for Submerging and Emerging Cuboids

Searching Data Cube for Submerging and Emerging Cuboids

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

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

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