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Traditional conversational IVRs can be useful as conduits to agents, but they are not capable of resolving issues outside of simple FAQs, and even then, they only work when the customer speaks in short sentences using the right keywords.
These traditional conversational IVRs are often seen by customers as a hurdle they must leap before speaking to a person who can actually help them.
But things are changing with the introduction of customer-led conversational assistants.
This next generation of conversational assistants can understand people, however they speak, and give appropriate responses that move customers toward a resolution.
Customer-led conversational assistants can resolve simple and not-so-simple issues without passing them on to an agent. The ability to accurately extract intent from even long and complex utterances reduces the risk of misunderstandings or the assistant needing to respond with a generic, “I’m sorry, I didn’t get that.”
A customer-led conversational assistant should feel so natural that customers forget that they’re not speaking with a person. This enables customers to engage fully with the assistant, significantly increasing the likelihood of an accurate resolution.
When a conversational assistant decides it can’t resolve an issue, it will transfer to an agent. When that happens, it will have already transcribed the conversation and structured it into a useful format that can be sent directly to the agent’s desktop.
The agent gets a pop-up on their screen that tells them what has happened in the conversation so far, and they can pick up where the conversational assistant left off. In other words, the first thing an agent would say could be, “I understand you were trying to make a payment. Let me help you with that.”
This dramatically reduces the call duration, improves the customer experience, and frees up resources to increase first touch resolution.
The data used by managers to spot trends and optimize contact center performance typically come from two sources:
Conversational assistants transcribe and structure the data in real-time. Managers can view these insights on a live dashboard, enabling them to spot trends and be proactive in helping their agents resolve queries. They can also use the data to broaden the response of the conversational assistant to increase Zero Touch Resolution.
The more issues resolved by the conversational assistant, the more time agents have to work on complex, challenging calls. This is much more stimulating than repeating the same answers all the time, which means agents are more likely to stay in the role longer. This creates a virtuous cycle of more experienced agents resolving more issues on first contact.
Customer-led conversational assistants measurably improve first touch resolution and the efficiency of contact centres. PolyAI conversation assistants typically resolve 30-50% of calls without passing them on to an agent.
If you want to learn more about deploying a natural AI conversational assistant, get in touch with PolyAI today.