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.