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Self-service is often the front door that customers go through when they want to engage with your business. But how those customers self-serve is changing rapidly, as artificial intelligence (AI) gets them to their answers faster and machine learning continually improves those answers. And while voice may no longer be the primary interaction channel, it’s still essential to good customer experience.
AI-driven conversational interactive voice response (IVR) has opened opportunities for businesses that want to offer better self-service — and do so in a way that’s trending with customers. Conversational IVR is voice-driven, hands-free customer self-service that uses Natural Language Understanding (NLU) to understand content and the context of spoken requests. It removes the burden on customers to navigate through slow, confusing and hierarchical menus. And it lets customers self‑serve and resolve issues within the IVR.
Solving the Problems of Traditional IVR
A traditional voice interaction, on average, costs $15 per interaction, while a virtual agent can cost less than a dollar. Traditional IVR systems were designed for self-service support that cut down on the number of interactions between customers and support agents to reduce costs.
But these legacy systems don’t have machine learning capabilities and they can’t adapt to human error. If a customer makes a mistake, it can be slow and painful to backtrack and redo a request. Traditional IVRs are cumbersome for businesses to keep updated and customers don’t like them — for good reason. They often have lengthy instructions, too many options (or not enough), complex routing, and confusing or poor navigation. This leads to unhappy callers who eventually take it out on an agent — if they even make it that far.
IVR abandonment rates are typically included with average handle time metrics, and they can drag down customer satisfaction scores. Even worse, they can wipe out advancements from other more successful efforts to improve your customer experience. Conversational IVR offers many advantages over its traditional cousin.
Why Conversational IVR Matters
With conversational IVR that’s powered by AI and machine learning, customers lead the conversation by interacting naturally with you in their own language and using more complete phrases. They also choose the path they prefer versus the static menu path offered by traditional IVR. Because the IVR uses machine learning, it not only recommends the best matching options it also continually improves skills and knowledge for future interactions and offers a more human-like interaction.
In fact, conversational IVR is capable of capturing customers’ exact phrases and provide insight into specific issues that customers are looking for via self-service. This is especially helpful because tasks vary by industry and business. If customers call a bank to check their balance, they only need to say “Check balance,” instead of going through a cumbersome menu to get the same result. But if you’re selling auto parts, common questions, as well as how they’re asked, will differ.
This flexibility is part of the technology’s design for machine learning. For instance, if a caller says something that a conversational IVR doesn’t understand, a live agent can seamlessly intervene and take over. The next time the IVR faces a similar situation, it won’t need that intervention. As the IVR builds its store of data and knowledge, it can handle more tasks on its own — far beyond the capabilities of rules-based engines.
Money Talks and Happy Customers Don’t Walk
Conversational IVR saves on the cost of using live agents for all interactions, plus it gives customers what they want in terms of faster and more personalized responses.
Conversational IVR can cut live agent calls in half and similar improvements can be realized in the accuracy of call routing and customer satisfaction. As AI advances, customers will be even less willing to navigate long menus and sit through lengthy wait times for problem resolution. It’s easier to hang up and engage with a competitor who offers more efficient engagement.
IBM has reported that companies worldwide spend more than $1.3 trillion to serve 265 billion customer calls each year. Saving 10 or 20 seconds for agents and callers on each interaction translates into huge savings. And, along with reducing the time it takes to serve each customer, conversational IVRs are also more consistent than humans.
Thanks to these advances in AI, machine learning, NLU and speech recognition, businesses are getting better at quickly and more correctly understanding a caller’s needs and intent. But effective communication is more than words. In a self-service IVR environment without facial and body cues, conversational IVR must learn how to sound less like a computer and more like a human.
Developing a new voice takes time, although technology is developing rapidly. Speechmorphing, for example, can produce a new, high-quality voice based on only 30 minutes of recorded speech. It addresses the growing need of companies that want personalized, branded voices for their self-service IVRs and digital agents to offer more realistic customer experiences.
Conversational IVR for Easier Self-Service
With the widespread adoption of personal assistants such as Siri, Google Now and Alexa, consumers are becoming pretty comfortable engaging with them. Give customers the IVR experience they expect with natural, intuitive self-service conversational IVR — and you’ll set expectations for their entire customer journey. Customers are happier with a fast, accurate resolution to questions. And, in the long run, those satisfied customers are more likely to purchase more from you.
Implementing a conversational IVR involves more than developing a menu or training a bot to perform simple tasks. It’s an ongoing opportunity to continually learn and improve interactions with your customers as they self-serve.
Register now for our upcoming customer webinar, 5 things to consider when building chatbots, and learn best practices for building bots into your overall customer experience strategy.
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