My main use case for OpenText Contact Center Analytics is focused on Speech and Text analytics. I analyze transcribed calls and chat logs to identify customer sentiments and their feedback and ratings for the services we provide in our day-to-day operations. For example, when we use a ServiceNow tool to resolve customer tickets, I take that data, the logs, and the ratings, and then use OpenText Contact Center Analytics to analyze the data and provide us with a dashboard.
I used OpenText Contact Center Analytics for approximately six months during my time as a software developer intern. During that period, I worked on analyzing customer interaction data and contact center metrics, supporting reporting and dashboard insights for operational teams, integrating analytic outputs with backend services and automation workflows, and improving performance and reliability of analytics-related components. My usage was hands-on and production-oriented, focused on extracting actionable insights rather than just tool-level exposure. My main use case for OpenText Contact Center Analytics focused on extracting actionable insights to enhance operational efficiency. During my internship, I worked on improving contact center efficiency using OpenText Contact Center Analytics. One recurring issue was a high call transfer rate and long average handle time for certain support queues. I used the CCA tool to analyze call transcripts, agent disposition codes, and time-based trends. The analytics showed that a significant percentage of calls were being transferred because agents lacked quick access to troubleshooting steps for a specific product module. Based on that insight, I collaborated with the support and engineering teams to update the agent knowledge base and refine IVR routing rules. After that change, we observed a measurable reduction in call transfers and a noticeable improvement in average handling time, which directly improved customer satisfaction and agent productivity. OpenText Contact Center Analytics helped us move from reactive support to data-driven operational improvements. By analyzing conversation data and interaction trends, we identified repeat call drivers, high transfer queues, and sentiment drops much earlier. The concrete outcomes we saw included reduced call transfers and average handle time by addressing the exact topics causing agent confusion. It improved first contact resolution as the agent scripts and knowledgeable articles were updated based on real conversation insights, along with better agent coaching using analytics-backed evidence rather than subjective feedback. We experienced faster issue escalation to engineering and improved customer experience reflected in more stable sentiment trends over time. The biggest improvement was not just metrics; it was a confidence in decisions, with customer trust growing significantly.
My main use case for OpenText Contact Center Analytics is consulting products like OpenText Contact Center Analytics for our clients, and it is very helpful for our customers where they are able to get all the information from their customers and then identify what kind of useful information can be obtained from the conversations. It helps them to create an incident, create a case, or create a positive requirement in terms of other ERP systems, which is how we typically use it for day-to-day operations.
My main use case for OpenText Contact Center Analytics is focused on Speech and Text analytics. I analyze transcribed calls and chat logs to identify customer sentiments and their feedback and ratings for the services we provide in our day-to-day operations. For example, when we use a ServiceNow tool to resolve customer tickets, I take that data, the logs, and the ratings, and then use OpenText Contact Center Analytics to analyze the data and provide us with a dashboard.
I used OpenText Contact Center Analytics for approximately six months during my time as a software developer intern. During that period, I worked on analyzing customer interaction data and contact center metrics, supporting reporting and dashboard insights for operational teams, integrating analytic outputs with backend services and automation workflows, and improving performance and reliability of analytics-related components. My usage was hands-on and production-oriented, focused on extracting actionable insights rather than just tool-level exposure. My main use case for OpenText Contact Center Analytics focused on extracting actionable insights to enhance operational efficiency. During my internship, I worked on improving contact center efficiency using OpenText Contact Center Analytics. One recurring issue was a high call transfer rate and long average handle time for certain support queues. I used the CCA tool to analyze call transcripts, agent disposition codes, and time-based trends. The analytics showed that a significant percentage of calls were being transferred because agents lacked quick access to troubleshooting steps for a specific product module. Based on that insight, I collaborated with the support and engineering teams to update the agent knowledge base and refine IVR routing rules. After that change, we observed a measurable reduction in call transfers and a noticeable improvement in average handling time, which directly improved customer satisfaction and agent productivity. OpenText Contact Center Analytics helped us move from reactive support to data-driven operational improvements. By analyzing conversation data and interaction trends, we identified repeat call drivers, high transfer queues, and sentiment drops much earlier. The concrete outcomes we saw included reduced call transfers and average handle time by addressing the exact topics causing agent confusion. It improved first contact resolution as the agent scripts and knowledgeable articles were updated based on real conversation insights, along with better agent coaching using analytics-backed evidence rather than subjective feedback. We experienced faster issue escalation to engineering and improved customer experience reflected in more stable sentiment trends over time. The biggest improvement was not just metrics; it was a confidence in decisions, with customer trust growing significantly.
My main use case for OpenText Contact Center Analytics is consulting products like OpenText Contact Center Analytics for our clients, and it is very helpful for our customers where they are able to get all the information from their customers and then identify what kind of useful information can be obtained from the conversations. It helps them to create an incident, create a case, or create a positive requirement in terms of other ERP systems, which is how we typically use it for day-to-day operations.