Tighter integration with the CRM and IVR could enhance the overall integration capabilities, and there is room for improvement in the data ecosystem, including integration with multiple data platforms such as Snowflake.To make the experience smoother with OpenText Contact Center Analytics, the entire stack needs to support observability metrics. The addition of better observability metrics can yield better results, such as measuring churn reduction rates.
While OpenText Contact Center Analytics is strong in conversation intelligence and enterprise-scale analytics, I can suggest a few improvements. The key improvement is more real-time insights; most analytics are batch-oriented. Adding stronger real-time or near-real-time alerts for sentiment drops or spike patterns would help supervisors intervene faster during live operations. The second improvement involves deeper GenAI recommendations. Currently, it tells what is happening and why, but it could go further by suggesting next-best-actions based on trends and anomalies, which could be done using ML models. The third improvement is simpler customization for non-technical users, as creating custom categories and rules for dashboards requires some technical effort. A more low-code, no-code experience would help business users iterate faster without engineering support.
OpenText Contact Center Analytics could be improved in the integration with other SAP applications, particularly the integration mechanism with C4C systems, which is an area where we want OpenText Contact Center Analytics to provide an automated solution. We have not tried the mobile way of interacting with OpenText Contact Center Analytics, so I think having an easy and intuitive interface on mobile is something we want to see.
Find out what your peers are saying about Joulica Amazon Connect Analytics vs. OpenText Contact Center Analytics and other solutions. Updated: January 2026.
Find out in this report how the two Customer Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Tighter integration with the CRM and IVR could enhance the overall integration capabilities, and there is room for improvement in the data ecosystem, including integration with multiple data platforms such as Snowflake.To make the experience smoother with OpenText Contact Center Analytics, the entire stack needs to support observability metrics. The addition of better observability metrics can yield better results, such as measuring churn reduction rates.
OpenText Contact Center Analytics can be improved by being more flexible and scalable on other public clouds with new features.
While OpenText Contact Center Analytics is strong in conversation intelligence and enterprise-scale analytics, I can suggest a few improvements. The key improvement is more real-time insights; most analytics are batch-oriented. Adding stronger real-time or near-real-time alerts for sentiment drops or spike patterns would help supervisors intervene faster during live operations. The second improvement involves deeper GenAI recommendations. Currently, it tells what is happening and why, but it could go further by suggesting next-best-actions based on trends and anomalies, which could be done using ML models. The third improvement is simpler customization for non-technical users, as creating custom categories and rules for dashboards requires some technical effort. A more low-code, no-code experience would help business users iterate faster without engineering support.
OpenText Contact Center Analytics could be improved in the integration with other SAP applications, particularly the integration mechanism with C4C systems, which is an area where we want OpenText Contact Center Analytics to provide an automated solution. We have not tried the mobile way of interacting with OpenText Contact Center Analytics, so I think having an easy and intuitive interface on mobile is something we want to see.