Şebeke kaynaklı müşteri şikayetleri için akıllı teşhis ve çözüm sistemleri

Name: Şebeke kaynaklı müşteri şikayetleri için akıllı teşhis ve çözüm sistemleri
Author: Kasapoğulları, T., Öneş, O., Albey, Erinç

İsim	Şebeke kaynaklı müşteri şikayetleri için akıllı teşhis ve çözüm sistemleri
Yazar	Kasapoğulları, T., Öneş, O., Albey, Erinç
Basım Tarihi:	2016
Basım Yeri	- IEEE
Konu	Müşteri şikayeti teşhisi, Şebeke anahtar performans göstergeleri (KPI), Deneyim niteliği (QoE), Sinyal sistemleri, Veri analitiği, Karar destek sistemleri, Karar ağaçları
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-5090-1679-2
Kayıt Numarası	d5df99ab-bf1e-44b4-9af8-ac5552efc92b
Lokasyon	Industrial Engineering
Tarih	2016
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	In the telecommunication sector, there exists a vast variety of protocol data (Such as ADR, IUCS, GB, IUPS, GN) produced by signaling systems. The data contains various information regarding network quality and performance. In the case of an anomaly, network surveillance experts need to overview the network key performance indicators (KPIs) to ensure sustainable high quality network services and to increase customer satisfaction. However, during the overview process, the customer based network problems can be easily overlooked. To overcome these problems by early diagnosis, customer-oriented customer quality index (CQI) sets needs to be produced. In this article a new customer oriented smart diagnosis and solution model approach is presented in the light of the customer experience information about of the customers who have faced network problems. While scoring the customer oriented data and determining the smart diagnosis, a decision tree method is applied. With the help of the provided decision support system (DSS), final CQI scores can be generated; smart diagnosis and recommendations to the call center employees can be provided; and scored customer network experience data can be transferred to the network solution teams automatically.
DOI	10.1109/SIU.2016.7496099

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Şebeke kaynaklı müşteri şikayetleri için akıllı teşhis ve çözüm sistemleri

Yazar Kasapoğulları, T., Öneş, O., Albey, Erinç

Basım Tarihi 2016

Basım Yeri - IEEE

Konu Müşteri şikayeti teşhisi, Şebeke anahtar performans göstergeleri (KPI), Deneyim niteliği (QoE), Sinyal sistemleri, Veri analitiği, Karar destek sistemleri, Karar ağaçları

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-5090-1679-2

Kayıt Numarası d5df99ab-bf1e-44b4-9af8-ac5552efc92b

Lokasyon Industrial Engineering

Tarih 2016

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin In the telecommunication sector, there exists a vast variety of protocol data (Such as ADR, IUCS, GB, IUPS, GN) produced by signaling systems. The data contains various information regarding network quality and performance. In the case of an anomaly, network surveillance experts need to overview the network key performance indicators (KPIs) to ensure sustainable high quality network services and to increase customer satisfaction. However, during the overview process, the customer based network problems can be easily overlooked. To overcome these problems by early diagnosis, customer-oriented customer quality index (CQI) sets needs to be produced. In this article a new customer oriented smart diagnosis and solution model approach is presented in the light of the customer experience information about of the customers who have faced network problems. While scoring the customer oriented data and determining the smart diagnosis, a decision tree method is applied. With the help of the provided decision support system (DSS), final CQI scores can be generated; smart diagnosis and recommendations to the call center employees can be provided; and scored customer network experience data can be transferred to the network solution teams automatically.

DOI 10.1109/SIU.2016.7496099