Clustering Analysis for Silent Telecom Customers Based on K-means++

Silent customers are part of customers that company is very easy to lose. It is necessary to analyze the features of such customers and make appropriate market decisions to improve the enterprise's revenue in telecom industry. This paper proposes a K-means++ method for customer segmentation based on silent customers. Firstly, key variables to the segmentation model was screened out and then the original data was preprocessed. Secondly, silent customers were clustered and the Calinski-Harabasz index was adopted to verify the best clustering effect when $k=6$. At last, radar chart analysis and suggestions were given, which would provide data supports to the improvement of operation and maintenance management and decision-making of the precision marketing.

[1]  V. Jayasree,et al.  A REVIEW ON DATA MINING IN BANKING SECTOR , 2013 .

[2]  Isti Surjandari,et al.  Data Mining Approach for Customer Segmentation in B2B Settings using Centroid-Based Clustering , 2019, 2019 16th International Conference on Service Systems and Service Management (ICSSSM).

[3]  Giovanni Seni,et al.  Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions , 2010, Ensemble Methods in Data Mining.

[4]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[5]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[6]  Wenbo Zhang,et al.  Improved K-Means cluster algorithm in telecommunications enterprises customer segmentation , 2010, 2010 IEEE International Conference on Information Theory and Information Security.

[7]  Sarika Chaudhary,et al.  A Survey: Clustering Algorithms in Data Mining , 2015 .

[8]  Zheng Huang,et al.  Association Analysis of Abnormal Behavior of Electronic Invoice Based on K-Means and Skip-Gram , 2019, 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC).

[9]  Cai Yi-chao.  Survey of Clustering Algorithms in Data Mining , 2007 .

[10]  Chen Ping,et al.  Customer Segmentation Algorithm Based on Data Mining for Electric Vehicles , 2019, 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

[11]  Bayu Adhi Tama.  Penetapan Strategi Penjualan Menggunakan Association Rules dalam Konteks CRM , 2010 .

[12]  Olatz Arbelaitz,et al.  An extensive comparative study of cluster validity indices , 2013, Pattern Recognit..

[13]  Carole Driver.  Handbook of Customer Satisfaction Measurement , 1999 .

[14]  Julia Ling,et al.  K-Means Clustering for Data Visualization and Flow Interpretation: Inclined Jet in Crossflow Example , 2013 .