Customer segmentation based on survival character

Chen, Yun; Zhang, Guozheng; Hu, Dengfeng; Fu, Chuan

doi:10.1007/s10845-007-0059-z

Cited by 24 publications

(7 citation statements)

References 11 publications

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“…The segmentation techniques found are the following: Clustering, Classification, Association, Regression and Visualization. Out of the studies using clustering, 73% use clustering for behavioral segmentation (Wu & Chou, 2011;Jansen, 2007;Ye, Yijun &Zhu, 2013;Cheng & Chen, 2009) 36 % for demographics (Chen et al, 2007, Tsiptsis & Chorianopoulos, 2009, 36% for attitudinal (Hong & Kim, 2012;Miguéis et al, 2012) and 18% for value-based (Chan, 2005). On the other hand, out of the studies using classification, 75% use classification for loyalty-based (Chan, 2008), 50% for value-based segmentation (Han, Lu &Leung, 2012), and 25% for behavioral (Kim et al, 2006).…”

Section: Tools and Algorithms Suitable For Customer Segmentationmentioning

confidence: 99%

“…A vital process for Online Marketing is Customer Segmentation, which constitutes the process of dividing customers into distinct and homogeneous groups. Customer segmentation is considered an effective method for managing different customers with different preferences, while developing diverse marketing strategies (Chen et al, 2007;Tsitptis & Chorianopoulos, 2009). Online customers can be segmented according to their characteristics that are tracked online with the use of specific techniques and algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Big Data Opportunities for Online Customer Segmentation

Fotaki

Spruit

Brinkkemper

et al. 2014

International Journal of Business Intelligence Research

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In today's competitive business environment, more and more organizations move or extent their business online. Thus, there is an increasing need for organizations to build concrete online marketing strategies in order to engage with their customers. One basic step towards achieving the objectives related to online marketing is the segmentation of online customers, based on the customer data gathered online. Since there is an onslaught of customer information collected from online sources, new techniques are required for managing and analyzing the huge amount of data, and this is where the concept of Big Data can play an essential role. This research sheds light on three fields: Online Marketing, Customer Segmentation, and Big Data Analytics. The three domains are integrated into the Online Customer Segmentation (OCS) framework, which attempts to show how online marketing objectives can be supported by techniques and tools applicable to extremely large datasets. For the creation of the OCS framework a set of main online marketing objectives is defined. Moreover, the differences among customer attributes gathered from offline and online channels are discussed and OCS categories are identified. Finally, the concept of Big Data is introduced and relevant techniques and tools suitable for analyzing customer segmentation categories and segmenting customers effectively are described. This work demonstrates the OCS framework by applying it on a hypothetical business scenario using an online customer data set.

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Section: Tools and Algorithms Suitable For Customer Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Big Data Opportunities for Online Customer Segmentation

Fotaki

Spruit

Brinkkemper

et al. 2014

International Journal of Business Intelligence Research

View full text Add to dashboard Cite

show abstract

“…The implementation process of customer segmentation is based on sample learning method. When deicide the marketing strategy of customer relationship management, managers often use some description of customer characters, such as "high-income customers", "low-income customers", "trendy customer", "conservative clients", "high risk customer", the main task of customer segmentation is to ensure the corresponding relations between t these concepts and the corresponding customers [14][15][16][17]. Customer data contains several discrete customer attributes and continuous customer attributes, each customer attribute as a dimension, every customer as a bit of space, the enterprise customer database of all customers can constitute a multidimensional space, called the attribute space of customers.…”

Section: Structure Modelmentioning

confidence: 99%

Optional Insurance Compensation Rate Selection and Evaluation in Financial Institutions

Zhikun¹,

Wang²,

Liu³

2014

IJUNESST

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“…Cluster analysis can be used in the customer support and relationship management industry (Berry and Linoff, 2004). Chen et al (2007) use cluster analysis to perform customer segmentation aimed at improving customer retention in the telecommunication industry. Choudhary et al (2009) provide a thorough review of clustering techniques used to solve manufacturing problems such as defect analysis, system rule generation, yield improvement and process optimization.…”

Section: Measuring Similarity Through Citation Network Analysismentioning

confidence: 99%