Share your experience using IBM Watson Customer Experience Analytics

OpenText Contact Center Analytics is primarily used for analytics on call recordings, integrated with our in-house IVR system.

The integration with the IVR system focuses on menu options in the IVR for routing, which is our primary use case.

OpenText Contact Center Analytics is used for identifying misconfigured routes in the IVR. When routes are misconfigured in the IVR, this tool is utilized by multiple teams, including the IVR product team and the CRM team.

The best features of OpenText Contact Center Analytics include the entire gamut of speech-to-text analysis, which performs comprehensive speech and text analysis with sentiment detection.

The sentiment analysis feature helps in customer decision-making in our day-to-day operations.

OpenText Contact Center Analytics supports omni-channel integration, resulting in better Net Promoter Score across different channels, which allows us to support customers more effectively.

The biggest advantage of OpenText Contact Center Analytics is its positive impact on customer churn and revenue, where revenues have increased drastically due to the adoption of the tool.

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.

I have been using OpenText Contact Center Analytics for the last one year.

OpenText Contact Center Analytics is stable.

OpenText Contact Center Analytics has good scalability, as we can ingest IVR recorders and scale up without having to custom shard. Currently, we are using Arabic and English while adhering to PCI compliance.

We have subscribed to the professional services group and are utilizing advanced customer support with OpenText Contact Center Analytics. Advanced customer support is minimizing downtime and maximizing ROI with dedicated experts.

Positive

We previously used a different solution called NICE CXone, which offered similar capabilities and analytics bundled. We switched because of its quote-based licensing.

The initial setup cost of OpenText Contact Center Analytics depends on the deployment model, the scope of implementation, and the kind of services we are integrating, such as whether we are utilizing support or professional services. These factors determine the initial setup cost.

For organizations looking into using OpenText Contact Center Analytics, my key advice is to define success metrics upfront, such as KPIs related to churn rate reduction, and prioritize data integration to ensure clean IVR recorder and CRM feeds from day one, as analytics quality depends on complete interaction context.

I have seen a return on investment with OpenText Contact Center Analytics, which includes cost savings from a 15% to 20% drop in churn rates as one of the biggest ROI outcomes. Apart from churn reduction, IVR optimization has resulted in a 20% self-service lift within 9 to 12 months.

Before choosing OpenText Contact Center Analytics, we evaluated CallMiner for deep search analytics and emotion detection. We found that CallMiner was less GenAI focused and had a higher setup cost compared to OpenText Contact Center Analytics.

The main aim with OpenText Contact Center Analytics should be to reduce churn rates, as that is the quickest win. Once the churn use cases are stable for the large banking environment, OpenText Contact Center Analytics can scale well and be a strategic part of the entire enterprise architecture ecosystem. I would rate this solution a 7 out of 10.

On-premises

Other

My main use case for Amazon Connect Analytics is for our help desk service, where we're trying to analyze the interactions that our customers have in order to provide better services.

One specific example of how I use Amazon Connect Analytics for our help desk service is the sentiment analysis, which helps us identify if the reason a client requests help is because, for example, a client who always calls is upset. Something is really bad with the service he's receiving or the equipment he's using. Based on that, we can create specific plans to help them, which has helped us considerably and has also helped us determine which services the clients are using more. Those are two basic examples.

The best features Amazon Connect Analytics offers include the information that comes in real-time and the dashboards, which are very useful, easy to use, and very easy to show to the final client and end user.

When my team uses the dashboards from Amazon Connect Analytics, we have to see the information from different perspectives, so it's not one specific dashboard or one specific metric that stands out for us.

The information we get from Amazon Connect Analytics helps us not only with the current service but also in creating new services and for the renovation of the service when it comes, making it very helpful.

Regarding how Amazon Connect Analytics can be improved, the maturity level of our clients is fine with Amazon Connect's base, and I think as we navigate the gigantic world, we'll see some business use cases that are going to be much more specific.

I have been using Amazon Connect Analytics for approximately a year.

Amazon Connect Analytics is stable, and so far, I have experienced no problems.

The scalability of Amazon Connect Analytics has been pretty easy and friendly so far.

Customer support for Amazon Connect Analytics is great.

I did not previously use a different solution.

My experience with pricing, setup cost, and licensing was pretty good as it was easy and very clear.

Customization with Amazon Connect Analytics has been pretty easy, as our implementation team picked up the bar and the line very fast, so it's pretty good.

We have seen a return on investment with Amazon Connect Analytics as we have reduced the number of managers that we need, making us much more cost-efficient.

A specific outcome we've seen from Amazon Connect Analytics is an improvement in customer satisfaction that we measure every six months, which has improved significantly.

My experience with pricing, setup cost, and licensing was pretty good as it was easy and very clear.

Before choosing Amazon Connect Analytics, we evaluated other options such as Larigo and some other in-house solutions from various providers, but nothing relevant came out of it.

My advice for others looking into using Amazon Connect Analytics is to evaluate how easy it is to grow in this service, because it's not only about the features but also how easy you can move between them, and I think the service is very strong in that regard.

I have no additional thoughts about Amazon Connect Analytics; I just think it's very interesting that you can keep growing on use cases, which will make it even easier. I would rate this product a 9 overall.

Public Cloud

Amazon Web Services (AWS)