Essays about: "Ticket Resolution"

Showing result 1 - 5 of 6 essays containing the words Ticket Resolution.

1. 1. Prediction Models for TV Case Resolution Times with Machine Learning

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Borja Javierre I Moyano; [2023]
   Keywords : Datasets; Machine Learning ML ; Prediction; Resolution Time RT ; Solve Time; TV Cases; Trouble Tickets TT ; Customer-Related Trouble Tickets Resolution Time; CRM system; BI system; Telecommunications; Dataset; Machine Learning ML ; Prediction; Resolution Time; Solve Time; TV Cases; Trouble Tickets TT ; Kundrelaterade problem Tickets Resolution tid; CRM-system; BI-system; Telekommunikationer.;

   Abstract : TV distribution and stream content delivery of video over the Internet, since is made up of complex networks including Content Delivery Networks (CDNs), cables and end-point user devices, that is very prone to issues appearing in different levels of the network ending up affecting the final customer’s TV services. When a problem affects the customer, and this prevents from having a proper TV delivery service in devices used for stream purposes, the issue is reported through a call, a TV case is opened and the company’s customer handling agents start supervising it to solve the problem as soon as possible. READ MORE

3. 2. Improving customer support efficiency through decision support powered by machine learning

   University essay from Linköpings universitet/Programvara och system

   Author : Simon Boman; [2023]
   Keywords : Machine Learning; AI; NLP; Natural Language Processing; GPT-3; GPT-4; Recommendation System; Decision Support; Semantic Textual Similarity; Text Similarity; Customer Support Tickets; Case Study; Customer Support Efficiency; Healthcare; Medical Technology;

   Abstract : More and more aspects of today’s healthcare are becoming integrated with medical technology and dependent on medical IT systems, which consequently puts stricter re-quirements on the companies delivering these solutions. As a result, companies delivering medical technology solutions need to spend a lot of resources maintaining high-quality, responsive customer support. READ MORE

4. 3. Trouble Tickets resolution time estimation : The Design of a Solution for a Real Case Scenario

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Riccardo Colella; [2021]
   Keywords : Trouble Ticket; Resolution Time Estimation; Ticket Resolution; Company processes; Trouble Ticket Management; Telecommunication; Data-Driven; Machine Learning; Problembiljett; uppskattning av lösningstid; lösning av biljetter; företagsprocesser; hantering av problembiljetter; telekommunikation; datadriven; Machine Learning;

   Abstract : Internet Service Providers are companies that deliver services managing a complex network of apparatus and cables. Given the complexity of the network, it often happens that alarms are generated. READ MORE

5. 4. Enhancing Business Support Systems through Data Science and Machine Learning : A study on possible applications within BSS

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Jacopo Castello; [2021]
   Keywords : Data Science; Time Series Analysis; Regression; Classification; Support Systems; Datavetenskap; Tidsserieanalys; Regressionsanalys; Klassifikation; Supportsystem;

   Abstract : The companies’ support phase, as all of business’ functional areas and components, went through a heavy and rapid digitalization which has unlocked the availability of an unprecedented amount of data. Unlike other relevant business areas and components, the support phase seems to have experienced fewer improvements attributable to Data Science and machine learning. READ MORE

6. 5. Modeling Trouble Ticket ResolutionTime Using Machine Learning

   University essay from Linköpings universitet/Statistik och maskininlärning

   Author : Asad Enver; [2021]
   Keywords : Machine Learning;

   Abstract : This thesis work, conducted at Telenor Sweden, aims to build a model that would try to accurately predict the resolution time of Priority 4 Trouble Tickets. (Priority 4 trouble tickets are those tickets that get generated more often-e in higher volumes per month). READ MORE