A Machine learning model for support tickets servicing: a case of Strathmore University ICTS client support services

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dc.contributor.authorMaina, Antony Koimbi
dc.date.accessioned2023-05-22T11:07:27Z
dc.date.available2023-05-22T11:07:27Z
dc.date.issued2022
dc.descriptionSubmitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSc. IT) at Strathmore University
dc.description.abstractCustomer service is a highly vital part of any business. How satisfied your customers are can make or break a company. One of the greatest contributors to customer satisfaction is the ability to respond to their issues efficiently and effectively. Many businesses therefore opt to establish a customer service department that handles customers’ services, this includes receiving phone calls and replying to emails. Customers are expected to call with issues such as, “How do I reset my password?” “How do I access the Student Information System?” “Are the student’s marks out yet?” and the like. Often, the issues reported by customers are similar and tend to get similar resolutions. These requests can be overwhelming at times, for example in cases where the users/customers are accessing an online resource and the system goes down, the number of inquiries can be in the order of thousands depending on the number of system users. This means a human agent may not be able to service all these requests on time. This research aims to develop an intelligent chatbot model for a support ticketing system using machine learning to deliver an exceptional customer experience. This research specifically proposes to develop a machine-learning model that can be used to service customer tickets in the context of a university or learning institution. The Rapid Application Development methodology was used to produce a working prototype of a chatbot to test the model to be developed. Machine learning and natural language processing were used to extract a user’s intent from a message and by leveraging pre-trained frequently asked question models from the DeepPavlov library, the model was trained on 80% of the data and 20% for testing. All 37 sessions tested on Dialogflow were successful, translating to a 100% success response rate. The prototype was tested by integrating the WhatsApp messaging platform to send messages to the chatbot. The chatbot was able to respond to the user in a fraction of a second. The average response time was less than one minute during testing.
dc.identifier.urihttp://hdl.handle.net/11071/13178
dc.language.isoen
dc.publisherStrathmore University
dc.titleA Machine learning model for support tickets servicing: a case of Strathmore University ICTS client support services
dc.typeThesis
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MSIT Theses and Dissertations (2022)