Goals and Challenges of IVR Modernization

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Read the complete transcript of this clip:

Emerson Sklar: Rather than throw away their existing infrastructure, more and more enterprises are integrating these AI platforms into their legacy IVR systems to capture the benefits available from this new age of technology. And so what are those benefits? The goals of IVR modernization typically focus on three different metrics. The first, reducing operating expense; the second, increasing the volume of concurrent calls that a given con call center and fixed pool of human agents can handle; and finally, increasing customer satisfaction. All of those have very tangible monetary and reputational benefits, and all also are largely dictated by the objective quality that your newly implemented system possesses.

And what does quality look like for an IVR? Let's take one of these modernization efforts in practice. one of our favorite Bespoken customers, EPCOR. Formerly known as the Edmonton Power Corporation, EPCOR is the largest utility company in Alberta, Canada. They manage water, wastewater, natural gas, and electricity distribution for millions of customers across both Canada and the United States. They have a large contact center with a number of human agents and they actually have to staff it with temporary on-demand agents during seasons of typical increased load, like wildfire season. And even without additional staffing, they aren't able to successfully handle the huge volume of calls that may come in after a significant storm or after a power outage. Their existing legacy IVR was largely DTMF-based, which means that every phone call for someone to report an outage required actually talking to a human agent. To reduce costs, to increase throughput, and to increase customer satisfaction, they decided to embark on a journey of modernization with Twilio Autopilot actually providing that AI background.

But they had a number of challenges, and these challenges we see actually quite frequently, they're really quite common for even the largest of enterprises that are following the same path. They had a very tight timeframe. They had a need to get the system functional and deployed in under a month. They had a team that was very, very skilled with traditional IVR processes and very skilled in web and mobile development, but lacking in conversational AI expertise. Being a Canadian company, they needed to support both Canadian English and Canadian French, and for their U.S. customers needed to be able to support U.S. English as well. And their testing team was quite small. No one spoke Canadian French. Very few of the members even had Canadian English as their primary accent, and the initial set of functionality that they wanted to support--again, power outages--had 40,000 unique addresses in the Edmonton, Alberta, Canada market. As is often the case with with many of these engagements, they had tremendous executive visibility. It was a big investment that they were making, and the executives had an expectation that the end product, the end result, would not be as good as their existing one, but would be much better, that the quality would be much higher. Figuring out how to measure and how to think about that quality was a challenge for them, as it is a challenge for for many organizations.

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