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Proceedings ArticleDOI

Making sense of customer tickets in cellular networks

TLDR
It is shown that most calls are due to customer-side factors and can be well captured by the model, and it is demonstrated that location-specific deviations from the model provide a good indicator of potential network-side issues.
Abstract
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.

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Understanding spatial relationships in resource usage in cellular data networks

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Experience: towards automated customer issue resolution in cellular networks

TL;DR: The field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues.
References
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Proceedings ArticleDOI

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Correlating instrumentation data to system states: a building block for automated diagnosis and control

TL;DR: Experimental results from a testbed show that TAN models involving small subsets of metrics capture patterns of performance behavior in a way that is accurate and yields insights into the causes of observed performance effects.
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Towards highly reliable enterprise network services via inference of multi-level dependencies

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Failure diagnosis using decision trees

TL;DR: A decision tree learning approach to diagnosing failures in large Internet sites is presented, and it is found that, among hundreds of potential causes, the algorithm successfully identifies 13 out of 14 true causes of failure, along with 2 false positives.