Customer Churn Analysis Model in Manufacturing Industry

Abstract:

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To address the problem of customer churn in CRM in manufacturing industry, this paper proposes a prediction model based on Support Vector Machine (SVM). Considering the large-scale and imbalanced churn data, principal component analysis (PCA) is adopted to reduce dimensions and eliminate redundant information, which makes the sample space for SVM more compact and reasonable. An improved SVM is used to predict customer churn. Firstly, PCA is adopted to process 17 dimensional feature vectors of customer churn data, and then the application in manufacturing industry verifies that this model based on both PCA and SVM performs better than the model based on SVM only and other traditional models.

Info:

Periodical:

Advanced Materials Research (Volumes 69-70)

Edited by:

Julong Yuan, Shiming Ji, Donghui Wen and Ming Chen

Pages:

675-679

DOI:

10.4028/www.scientific.net/AMR.69-70.675

Citation:

D.S. Liu and C. H. Ju, "Customer Churn Analysis Model in Manufacturing Industry", Advanced Materials Research, Vols. 69-70, pp. 675-679, 2009

Online since:

May 2009

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Price:

$35.00

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