Increasing IVR Self-Service Rates With AI and NLP

Increasing IVR Self-Service Rates With AI and NLP

4 min read
Self service

As little as a decade ago, consumers were relatively happy to navigate cumbersome touch-tone systems then wait on hold for the next available call agent. But the modern consumer is far less patient and increasingly expects not just immediate resolution but also to solve their issues themselves.

For example, keynote speaker Steven Van Belleghem cites that 70% of consumers expect companies to include a self-service application on their website and 40% prefer self-service to communicating with a human. While CMO Council research finds that 75% of consumers believe providing fast response times is the most critical customer experience attribute.

The rise of digital platforms, such as mobile and web, makes self-service easier, which only increases customer expectations for quick and straightforward brand interactions. But, despite this demand for self-service, many organizations’ current Interactive Voice Response (IVR) systems fail to deliver customer satisfaction or reduce their call center costs.

This is likely due to self-service customer support presenting its own set of implementation challenges, including:

  • The technology has to be updated regularly.
  • It can lack the personal touch that many consumers demand.
  • It can provide a service that customers find frustrating and irritating.

How AI and NLP Can Improve IVR Customer Self-Service

Evolving technologies and methodologies like artificial intelligence (AI) and natural language processing (NLP) are improving the service you can offer customers. They’re making IVR self-service interactions quicker and helping you to deliver the interactions that customers demand.

Machine learning helps transform an IVR’s understanding of language, enabling you to learn customer trends without excessive programming. In comparison, AI can track changes in speech modulation and decode changes in a caller’s voice, allowing you to analyze and predict customers’ accents, behavior patterns and emotions.

AI and NLP can boost your customers’ IVR self-service adoption rates with the following benefits:

Greater Personalization

Customers increasingly expect interactions that are highly customized and personalized to their specific interests and requirements. Delivering on this means offering tailored recommendations without navigating through long-winded menus or re-entering their personal information.

IVR systems help you to personalize customer service by using biometric authentication to recognize a caller through their voice. This enables you to welcome customers by name and modify the menu options available based on their previous call interactions and online payment history.

Quick Call Resolution

Customers increasingly want to resolve their issues as quickly as possible, regardless of the medium they use to do so. IVR technology now allows you to help customers immediately resolve simple problems, such as checking on the status of an order, service outage or package delivery, locating a professional service like a plumber or technician and troubleshooting technical issues. For example, phone numbers can be matched with existing customer data, enabling you to identify callers proactively, reduce completion times and the need for a call agent and create personalized experiences that anticipate customers’ needs and issues.

Increased Sales

An effective, innovative IVR enables you to provide personalized and proactive messages and recommendations to users. This also allows you to track your customers’ journeys across various channels, which helps you offer an improved experience and remove the potential of lost revenue.

Performance Optimization

IVR data analytics can help you understand the system’s performance and spot trends in your customer interactions. This allows you to identify potential issues in the customer lifecycle, understand why people contact your support team and gain a deeper understanding of users’ behaviors and preferences. The insight gained from this needs to be applied to help customers easily find the answers they need, increase call containment and boost user satisfaction.

True Understanding of Customer Interactions

NLP enables an IVR solution to fully understand callers by detecting emotion and tracking dialects and keywords. Using speech recognition software allows your customers to speak naturally and ensures you can accurately capture their intent. This is key to decreasing customer effort, steering customers to the right place every time and, if required, ensuring conversations are always routed to the most appropriate agent. As a result, you can increase first contact resolution and lower your average handling times.

Intelligent Digital Conversations with U-Self Serve

The success of a virtual assistant comes down to its ability to continuously learn from conversations and the resulting actions or outcomes. Our intelligent conversational digital assistant U-Self Serve uses AI and NLP to empower sales and service interactions across digital channels. It uses conversational AI and real-time data to provide premium self-service and human-like conversational experiences.

U-Self Serve enables you to automate conversations and deliver cost savings while improving user experience. The solution enhances the features and functionality of intelligent virtual assistants, making them more cost-effective to manage, easier to set up and enable smarter responses. It offers AI and automation capabilities that help you personalize content and understand intent and sentiment across web, mobile and social channels. This enables you to easily automate the tasks that employees don’t like doing, resolve customer issues in real-time and seamlessly hand the interaction back to a call agent at any time.

U-Self Serve offers secure connections to backend applications, such as your customer relationship manager (CRM) and tickets, to help you handle a wide range of customer requests. The solution tracks real-time performance metrics, simulates new intent behavior and uses machine learning to continuously improve natural language models and adapt to historical conversations.

Get in touch with a Uniphore expert today to learn more.

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