A centralized repository to enhance customer service using data mining and machine learning
Date
2016-04
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Abstract
In the quest to reduce customer churn rate and retain existing customers, organizations
have resorted to investing fortunes in their customer care services, which proves to be a
relatively cheaper means of staying in business. In this regard, this project sought to explore a
less costly way of providing quality customer care services to an organization’s clients in order
to keep them satisfied. As it stands today, there is a growing number of digital customer care
owing to the fact more clients have increased their online interactions. Organizations with
customer satisfaction as priority have invested in these e-care services to better serve their
customers. The gap identified however is the lack of a centralized repository to store and track
all concerns raised by customers. To bridge this gap, a prototype of a trouble ticketing system
was developed to allow clients to issue trouble tickets whenever faced with a difficulty. This
system in addition integrates and monitors company systems using Nagios IT Infrastructure
Monitoring, conducts sentiment analysis with Datumbox Machine Learning Framework,
analyzes and generate reports on the efficiency of the organization in dealing with their
customers. Since the world today revolves around making sense out of previous occurrences to
forecast the future and prepare adequately towards it, this system finds patterns in the errors
received in order to make predictions on which category of services provided by the company
is likely experience more error logs using the Amazon Machine Learning Web Service.
Description
Applied project submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2016
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Applied project
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Keywords
customer service, ticket system, trouble ticketing software, application