An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Abstract

<span>Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.</span>

Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman)

“…Clustering (berbasis centroid) adalah algoritma clustering yang menggunakan titik centroid dalam melakukan pengelompokan objek [7]. Kmeans merupakan algoritma sederhana dengan waktu proses yang cepat dan menghasilkan cluster yang optima [8]. K-Means adalah algoritma iteratif; itu terus mengulangi langkah-langkah di atas sampai tidak ada perubahan lokasi centroid [9], Tujuan dari metode clustering data ini adalah untuk meminimalkan fungsi tujuan yang ditetapkan dalam proses clustering [10].…”