Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences

Liu, Duen‐Ren; Shih, Ya‐Yueh

doi:10.1016/j.jss.2004.08.031

Cited by 129 publications

(68 citation statements)

References 19 publications

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“…This identification enables the related company and organization to identify those customers that bring no profit to the system and avoid much concentration on their requests and tastes. In order to compare the performance of the proposed method, two methods presented by Liu and Shih (2005) were employed, namely WRFMCD and A-WRFMCD. In these methods that are applied on a hardware sale database, first the RFM values of each customer are normalized.…”

Section: The Proposed Methodsmentioning

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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Section: The Proposed Methodsmentioning

confidence: 99%

Measuring customer loyalty using an extended RFM and clustering technique

Zalaghi¹,

Varzi²

2014

10.5267/j.msl

View full text Add to dashboard Cite

show abstract

“…WRFM proposed by [11] where different weights are assigned to R, F, and M depends on characteristics of the industry. Authors in [12] and [13], use the WRFM and K-means for customer value segmentation and the customer lifetime value (CLV) of each cluster is calculated as the sum of normalized RFM multiplied by their weights. Other researchers try to develop RFM model and add some parameters to these three attributes.…”

Section: Customer Value Analysis In Brick and Mortar (Physical Market)mentioning

confidence: 99%

New Insight into Customer Value Analysis using Data Mining Techniques

Taher¹,

Salama²,

Elzanfaly³

2017

IJCA

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Recency, Frequency, Monetary model (RFM) has been widely used to analyze the customers' value in traditional market using three purchasing behavior attributes. This is considered one dimensional view of customers' value that is based on profit and purchasing criteria and ignores other useful attributes. Online customers have additional attributes that when captured and analyzed can give more details about customers' value other than provided by traditional RFM model. This gives companies better vision of their customers, and therefore serve them effectively, resulting in strong and long relationship with them. New Behavioral RFM1 Model (BRFM) is proposed in this paper to provide online retailers with a new customers' insight that reflects their web behavior beside their profitability. Three web behavioral attributes, represented in Recency of Session (Rs), Frequency of Session (Fs), and Number of clicks (NoC) are added to the traditional RFM attributes for customer value segmentation in online market using K-means clustering algorithm. The effectiveness of BRFM model is compared against the traditional RFM using Dunn index and Davies-Bouldin measures. Results show that the BRFM model enhances the clustering accuracy and reveals new customers' clusters disregarded by the traditional RFM model.

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“…For example, Mihelis et al (2001) use regression model to measure customer satisfaction. Liu and Shih [13] develop a product recommender model to increase customer satisfaction, using weighted RFM (WRFM) and K-nearest neighbors with preference-based collaborative filtering.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining for Business Intelligence in Social Network: A survey

Bhanap¹,

Kawthekar²

2015

International Advanced Research Journal in Science, Engineering

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Abstract:In the era of digital world, due to the revolution in the field of Information and Communication Technology, Social Networks are providing an accessible platform for users to share and use information in almost all sectors of the society. As a result of this , pervasive usage of social network generates huge amount of social data. Mining this social data has its potential to extract actionable patterns, behave ors which are beneficial business organizations and customers. Different mining tools and techniques can be used to achieve better decision making in the field of Electronic Business Intelligence. This paper reviews basics of Data Mining, Social Network Analysis and its applications in Business Intelligence with data mining techniques and suggest how this survey and study of the data mining approaches can benefit the importance of social network analysis and mining for business intelligence.

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Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences

Cited by 129 publications

References 19 publications

Measuring customer loyalty using an extended RFM and clustering technique

Measuring customer loyalty using an extended RFM and clustering technique

New Insight into Customer Value Analysis using Data Mining Techniques

Data Mining for Business Intelligence in Social Network: A survey

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