Microsoft Azure Machine Learning Studio Review

Reduces work for our front-line agents, but the terminology for questions could be stronger


What is our primary use case?

Our primary use for this solution is for customer service. Specifically, chat responses based on pre-defined questions and answers.

How has it helped my organization?

We have reduced the theme size front-line agents by ten percent using the AI elements on chat and email response.

What is most valuable?

The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses. This reduces our resources and costs.

The user interface that we have is relatively simple.

What needs improvement?

Some of the terminologies, or the way that the questions are asked, could be stronger. When people use local colloquialisms, it would be better if it understood rather than forwarding it to an agent.

If the frontline efficiencies were improved then we could pass this on to our clients.

Integration with social media would be a valuable enhancement.

For how long have I used the solution?

I have been using the Microsoft Azure Machine Learning Studio for about eighteen months.

What do I think about the stability of the solution?

The stability is good and we haven't had any issues.

What do I think about the scalability of the solution?

Scalability for us was fine.

We have about seven hundred users including customer service agents, sales agents, and cell phone account managers. It took us about twelve months to scale to this point, from an initial user base of seventy people, and we do not plan to increase usage further.

How are customer service and technical support?

We've got an internal IT department and we raised inquiries through them. They speak with whoever they need to in order to resolve the ticket.

Which solution did I use previously and why did I switch?

The previous solution that we were using was based on the Aspect platform. It was fifteen years old, which is why we reviewed it. We weren't able to offer any kind of AI or omnichannel experience using that platform, as its pure telephony. Anything else that we did was piecemeal. We switched because the platform couldn't offer the support that we needed for our clients.

How was the initial setup?

The initial setup is straightforward.

Our deployment took about six weeks, but that was also integrating the new telephony platform as well. For the AI elements, it was probably around five days.

Once the initial knowledge base was set it it took time to build and get it to where we needed it to be. Until that happens you can't really implement the AI element. This is what took about six weeks, so that it covered all of the inquiries that we wanted.

We started with an on-premises deployment and have moved to the cloud.

What about the implementation team?

We performed most of the implementation on-site by ourselves, but we had some help from a consultant to give us guidance.

What other advice do I have?

My advice to anybody who is implementing this solution is to be prepared to take a slow approach to get the best results.

The biggest lesson that I have learned from using this solution is that the strategic outsourcing contact will need to have a strategy for the next three to five years because the efficiencies that we will be gaining from AI will reduce the requirements on physical staff doing traditional roles. However, the support element will increase. It means that the roles will change and evolve over the next three to five years within the UK contact center based on the deployment of AI.

I think that we probably didn't start from the point that would have benefited us most in terms of the AI. Had we put more research into the front end then there would have been a lot less work during the implementation.

I would rate this solution a six out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
**Disclosure: I am a real user, and this review is based on my own experience and opinions.
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