Application of data mining techniques in customer relationship management: A literature review and classification

Ngai, Eric W.T.; Li, Xiu; Chau, Dennis

doi:10.1016/j.eswa.2008.02.021

Cited by 964 publications

(568 citation statements)

References 103 publications

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“…Alhaiou et al proposed some benefits of CRM which includes lower cost of customers' acquisition, improvement of customer services, customer retention and loyalty increase, higher customers' profitability, easier identification of profitable customers and companies' productivity increase [4].Ngai et al categorizes analytical CRM on four dimensions [5]. Other related works in CRM are explained as follows, Customer Identification includes mainly customer segmentation.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Business Framework in identifying Customer Driven Demand Discovery

2015

IJSR

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Customer Relationship Management (CRM) plays a vital role in identifying customer behaviour analysis which can help to a great extent of retaining the customers and providing an quality service to the customer.i Retail sector is an typical art of business, which requires an accurate identified approach in decisions making. CRM can be powered by data mining techniques in attaining to the right configuration or setting which help in finding right solutions On the other hand, the retailers are unable to predict the demand of products which may leads to inconsistencies in profit by the use of CRM. To overcome this disadvantage, an integrated version of CRM along with Supply Chain Management (SCM) is designed which affords the stock requirement from SCM based on the demand in CRM. Thus SCM with CRM enhances the retailers profit, meet the customer's demand and retain the current customers.

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Section: Related Workmentioning

confidence: 99%

An Analysis of Business Framework in identifying Customer Driven Demand Discovery

2015

IJSR

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“…42,48 In other words, Operational CRM embodies automation of sales, marketing and service. Operational CRM facilitates costto-serve reduction and transactional accuracy improvement.…”

Section: Crm Formsmentioning

confidence: 99%

“…39 Despite the fact that CRM has become widely recognized and a lot of authors have defi ned it from different points of view, 12,13,33,41 -46 there is no universally accepted defi nition of CRM. 42,45 For reference, some defi nitions of CRM that highlight its prominent role in intensifying customer value are as follows:…”

Section: Theoretical Background Crmmentioning

confidence: 99%

Proposing a customer knowledge management model for customer value augmentation: A home appliances case study

Sedighi

Mokfi²,

Golrizgashti³

2012

J Database Mark Cust Strategy Manag

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Mohamad Mohsen Sedighiis a researcher at the Young Researchers Club, South Tehran Branch, Islamic Azad University. Taha Mokfiis a data miner and data mining trainer conducting plenty of research projects in Iranian companies. Seyedehfatemeh Golrizgashtiis a lecturer at South Tehran Branch, Islamic Azad University.ABSTRACT Customer Relationship Management (CRM) plays a prominent role in enabling businesses to meet their customers ' needs, and therefore it acts as a catalyst in the process of creating and delivering value to them. As CRM concerns managing customer knowledge, it can be considered as a subset of Knowledge Management (KM). Therefore, in this study, the effort has been made to propose a Customer Knowledge Management (CKM) process model to compensate the existing lack of a study integrating CRM and KM with the aim of customer value augmentation. In this CKM model, all forms of CRM are employed to support all the phases of CKM. Finally, a home appliances case is studied to illustrate the proposed CKM model.

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“…Customer identification is the first stage of customer relationship management (CRM), which involves finding and targeting the customers who are most profitable to companies [29]. Feature selection is a useful tool in this process, and it will not only solve the "curse of dimensionality" problem in modeling, but also help companies to identify key feature subset that distinguishes high-value customers from low-value ones [24].…”

Section: Introductionmentioning

confidence: 99%

A new transferred feature selection algorithm for customer identification

Zhu¹,

Niu²,

Xiao³

et al. 2016

Neural Comput & Applic

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Class imbalance brings great challenges to feature selection in customer identification and most of current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural network (GMDH). The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application from cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially the predictive accuracy of minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish highvalue customers from low-value ones.

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Application of data mining techniques in customer relationship management: A literature review and classification

Cited by 964 publications

References 103 publications

An Analysis of Business Framework in identifying Customer Driven Demand Discovery

An Analysis of Business Framework in identifying Customer Driven Demand Discovery

Proposing a customer knowledge management model for customer value augmentation: A home appliances case study

A new transferred feature selection algorithm for customer identification

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