TY - GEN
T1 - Making sense of customer tickets in cellular networks
AU - Jin, Yu
AU - Duffield, Nick
AU - Gerber, Alexandre
AU - Haffner, Patrick
AU - Hsu, Wen Ling
AU - Jacobson, Guy
AU - Sen, Subhabrata
AU - Venkataraman, Shobha
AU - Zhang, Zhi-Li
PY - 2011/8/2
Y1 - 2011/8/2
N2 - Effective management of large-scale cellular data networks is critical to meet customer demands and expectations. Customer calls for technical support provide direct indication as to the problems customers encounter. In this paper, we study the customer tickets - free-text recordings and classifications by customer support agents - collected at a large cellular network provider, with two inter-related goals: i) to characterize and understand the major factors which lead to customers to call and seek support; and ii) to utilize such customer tickets to help identify potential network problems. For this purpose, we develop a novel statistical approach to model customer call rates which account for customer-side factors (e.g., user tenure and handset types) and geo-locations. We show that most calls are due to customer-side factors and can be well captured by the model. Furthermore, we also demonstrate that location-specific deviations from the model provide a good indicator of potential network-side issues.
AB - Effective management of large-scale cellular data networks is critical to meet customer demands and expectations. Customer calls for technical support provide direct indication as to the problems customers encounter. In this paper, we study the customer tickets - free-text recordings and classifications by customer support agents - collected at a large cellular network provider, with two inter-related goals: i) to characterize and understand the major factors which lead to customers to call and seek support; and ii) to utilize such customer tickets to help identify potential network problems. For this purpose, we develop a novel statistical approach to model customer call rates which account for customer-side factors (e.g., user tenure and handset types) and geo-locations. We show that most calls are due to customer-side factors and can be well captured by the model. Furthermore, we also demonstrate that location-specific deviations from the model provide a good indicator of potential network-side issues.
UR - http://www.scopus.com/inward/record.url?scp=79960888987&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960888987&partnerID=8YFLogxK
U2 - 10.1109/INFCOM.2011.5934875
DO - 10.1109/INFCOM.2011.5934875
M3 - Conference contribution
AN - SCOPUS:79960888987
SN - 9781424499212
T3 - Proceedings - IEEE INFOCOM
SP - 101
EP - 105
BT - 2011 Proceedings IEEE INFOCOM
T2 - IEEE INFOCOM 2011
Y2 - 10 April 2011 through 15 April 2011
ER -