Positive

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.

The best features I can identify about OpenText Contact Center Analytics include Speech Analytics, which automatically transforms voice calls into meaningful data by analyzing sentiment, emotion, themes, and trends, giving deep insight into why customers call, not just what they say. The second feature is Text and Social Analytics, which analyzes chat transcripts, CRM notes, survey text, and even social media to uncover trends and sentiment across all channels. The third is Multi-channel Interaction Intelligence, which brings data from calls, email, chats, surveys, and social channels, providing a unified view of customer interaction regardless of where they happen. The fourth feature is Behavioral Scoring, which uses AI to automatically score interactions, evaluating both agent behaviors and customer reactions, which is valuable for coaching and quality improvement. The fifth is Dashboards and Trend Detection, where intuitive dashboards help visualize trends, sentiment, anomalies, and performance KPIs, making it easy to track performance and act on the insights. Regarding AI and productivity enhancements, the GenAI and summarization tool creates conversation summaries, shortens review cycles, and supports agent workflows, boosting productivity and quality checks along with sentiment analysis. Additionally, for advanced capabilities, there are Omni-channel Analytics, Custom Alerts and Topic Tagging from extended documents, and Real-time and Predictive Insights. The multiple language support is also very valuable.

Out of these features, the single feature that has the biggest impact on my work is Speech and Text Analytics. This mattered most because it allowed me to move from assumptions to evidence. Instead of relying only on surface-level KPIs such as call volume or handle time, Speech and Text Analytics let me analyze actual customer conversations and agent responses, identifying the recurring pain points, confusion patterns, and transfer triggers. It correlates customer sentiment with operational metrics such as transfers and escalations, and the real impact on my work is that I could pinpoint why certain calls were getting transferred, not just that they were. It directly influenced the IVR routing changes, agent script improvements, and knowledge base subjects. It helped me close the loop: insight, action, and measurable improvement. The dashboard told us what is happening.

One final point I would add is how the features of OpenText Contact Center Analytics work together. What strengthened the impact for me was the combination of conversation analytics with the dashboards and trend analysis. Speech and Text Analytics helped me uncover root causes while dashboards helped prioritize issues by scale and impact and track the improvements over time. This integration made it easier to justify changes to stakeholders because insights were data-backed, repeatable, and measurable, turning analytics from a reporting function into a continuous improvement system.

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.

I have been using OpenText Contact Center Analytics for approximately six months during my time as a software developer intern.

OpenText Contact Center Analytics is stable. It already has a cluster and maintains high availability; its stability is good and is resilient. Although a few changes could enhance stability, overall it is very good and people can feel confident using it.

Customer support is very good; as part of the engineering team, I am not personally aware of all details, but I hear positive feedback from support and product management.

When I joined this organization, OpenText Contact Center Analytics was already in use. I am not aware of any other solutions they might have used before. They might have gone through some options before choosing OpenText, but I am not aware of that. OpenText Contact Center Analytics was already in use when I joined, and it is good.

OpenText Contact Center Analytics is deployed in a secured, enterprise-grade hybrid model, aligned with data privacy and operational needs. We use a hybrid deployment where customer interaction data is ingested from an on-premises and cloud contact center system into OpenText Contact Center Analytics platform, while access and reporting are enabled through a secure web interface.

As part of this setup, we are currently using AWS.

As part of our engineering team, I can say that time has been saved and cost savings have also been seen from our upper level.

Pricing, setup cost, and licensing are taken care of by our product management and upper level; as part of the engineering team, I am not aware of those details.

People should definitely consider using OpenText Contact Center Analytics as it is very good. It brings together messages, conversations, meetings, and social media experiences in one place, allowing for easy summarization and improvement in customer needs while displaying KPIs, with all indicators, including sentiment scores, increasing. It is a unified experience. My experience in 2023 with OpenText Contact Center Analytics was good, and although a few changes could enhance stability, overall it is very good and people can feel confident using it. Even with these gaps, OpenText Contact Center Analytics already delivers strong value. Addressing these areas would move it from a power analytics platform to a proactive decision-driving system. I would rate this product 8.5 out of 10.

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.

OpenText Contact Center Analytics helps with good speech analytics, provides good reports, and addresses several KPIs, making it a good solution for speech analytics and text and social analytics information.

The best features that OpenText Contact Center Analytics offers include helping with speech analytics, transforming voice calls into insights, analyzing the sentiment and emotion of customers, and delivering good reports from the KPIs. It also minimizes customer churn and analyzes the interaction between our customer agents and customers and the trend deduction in terms of customer behavior trends.

The feature that has made the biggest impact for our clients is sentiment analysis because it helps to understand actual customer feedback based upon the reply given by the customer without requiring additional employees to understand the text and sentiment. OpenText provides sentiment analysis out of the box, which is good for our customers.

I appreciate the unique usage of generative AI for doing the sentiment analysis in OpenText Contact Center Analytics, as it is fresh out of the market with no major players, and OpenText is pioneering these kinds of analysis which are very much required for our customers.

Positive

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.

I have been using OpenText Contact Center Analytics for the last couple of years.

In my experience, OpenText Contact Center Analytics is stable, with no downtime or reliability issues.

OpenText Contact Center Analytics is really scalable and helps us to expand whenever needed, meeting our scalability requirements as our client base grows.

Customer support from OpenText has been very good, and our experience with the support team has also been very good, with no complaints.

Positive

We have not used a different solution previously, making this our first implementation for one of our customers.

The purchasing decisions and actions concerning OpenText Contact Center Analytics were handled directly by our customer, and we were not influenced, as we only supported the implementation and change management part of it.

OpenText Contact Center Analytics has helped in reducing customer churn for one of our clients, which contributes to top-line growth for our clients.

We were not actively involved in pricing or experiencing the purchase part, and we did not receive any complaints from our customer regarding the purchasing side, so I think it is good.

We evaluated the capabilities of C4C before choosing OpenText Contact Center Analytics, but it was not capable enough, so we chose OpenText Contact Center Analytics.

A recent situation where OpenText Contact Center Analytics helped a client involved a client case where a customer had to give their feedback over voice, and OpenText Contact Center Analytics was able to interpret the data, all the voice information, and convert it into text. It was able to analyze and categorize the customer as positive or negative which enabled the client to reduce customer churn.

OpenText Contact Center Analytics has positively impacted my organization as it has been used by one of the customers to analyze customer churn based on feedback provided by the customer in their support center. It is able to identify which customer is going to churn in the next month or which customer is going to continue for a long time, helping our sales team identify appropriate actions to prevent customer churn.

We are measuring the reduction in customer churn for our customer, which is currently in process, and we are expecting at least a twenty percent reduction in customer churn.

My advice for others looking into OpenText Contact Center Analytics is that if someone wants to try a generative AI way of interpreting customer interactions, then OpenText Contact Center Analytics is a very good tool, and we recommend it.

In addition to being a customer, we also have a business relationship as a partner, selling OpenText Contact Center Analytics to our customers.

Overall, my experience with OpenText Contact Center Analytics is good, and I appreciate the opportunity to do this survey. I would rate this product a nine out of ten. Thank you.