Why Speech is Emerging as the Preferred Interface for Self-Service
Speech is emerging as the preferred platform for phone-based self-service applications. Although speech-enabled self-service solutions for customer care have been around for years, they’ve fallen short of mass adoption because, until now, they’ve been too costly and complex to deploy for all but the largest call centers. However, recent advances in conversational AI are removing those barriers to adoption. That, coupled with changing consumer behavior, is causing speech to become the preferred interface for self-service applications.
Evolving Customer Behavior
Over the past several years, mainstream consumers have grown accustomed to voice assistants. Whether interacting with their smartwatches, mobile phones or smart home devices, people are now comfortable speaking to these devices, and receiving service from them. This is an important leap of faith, as it signals a willingness (and even a preference) for people to seek information and resolution by interacting with virtual assistants.
This willingness also extends to customer support. Customers now expect to interact with their favorite brands in a similar fashion. This is driving businesses to implement speech-enabled self-service solutions that perform as elegantly as the smart devices customers interact with on a daily basis.
As DMG Consulting CEO Donna Fluss wrote in a recent blog post, “A remarkable thing is happening in the realm of customer service: After years of rejecting self-service, customers are changing their tune. Consumers of all ages are showing a preference for self-service solutions over talking to agents…”
Technical Innovation Improves Quality and Reduces Cost
Core speech technologies have evolved rapidly. Speech-to-Text, Text-to-Speech and NLP are all now packaged as a set of cloud services from leading technology vendors like Google, IBM and Amazon. The neural networks that support those services are being trained by millions of consumer interactions with smart devices, continually improving the quality of these services. At the same time, costs are reduced as new tools make it possible to deploy these technologies at a fraction of the cost, and without the need for dedicated technical resources.
Google is also investing heavily in its Contact Center AI (CCAI) solution, which is being adopted rapidly by contact center software providers to help a growing number of businesses use AI to automate common interactions, and assist live agents as they interact with users.
NLP Improves Customer Experience
The evolution of Natural Language Processing (NLP) makes possible a significantly improved customer experience. NLP makes it easier for customers to get support through an automated system because the complexity of the interaction can be dramatically simplified. Today’s NLP solutions, such as Google’s Dialogflow, are especially well-suited to freeform dialogue. They can translate speech into text for virtually anything a caller says. They can also interpret the user’s intent and fulfill the user’s request in an automated fashion. Other cloud services, like IBM’s Tone Analyzer, can interpret the caller’s level of frustration and escalate calls for immediate resolution by live agents trained to diffuse volatile situations.
The most sophisticated intelligent virtual agent (IVA) platforms now make it possible to extend closed grammar functionality to support foreign languages. They make it easy to use NLP agents developed with Dialogflow with a streaming interface, thereby reducing latency and making interactions more natural. In the latest release of our solution, Inference Studio, we now enable companies to use Dialogflow agents they’ve built themselves, or select from a library of pre-built agents using the Open Form node within the Studio platform.
Closing the Development Gap
Despite all the breakthroughs described above, legacy methods for developing and deploying conversational AI apps traditionally relied heavily on development resources to license, install, manage and tune on-premise software and equipment—a process that could easily exceed $1 Million and take many months to get up and running. This meant that only large institutions like banks, airlines, and other enterprise organizations had the infrastructure and resources to develop these solutions. But new tools are leveling the playing field, making it much easier to deploy virtual agents (no development team required) at a lower price point ($10,000 instead of $1 Million), and in less time (taking days instead of months).
The latest IVA platforms enable telecom carriers to build, package and deploy natural language applications. Carriers then resell these IVA capabilities to their business customers who, in turn, implement the virtual agents to provide service and support to their customers. This disrupts the old model where only large enterprises with deep pockets could offer IVA capabilities. Now businesses of all sizes have the ability to build and deploy IVAs that meet a wide variety of business needs by incorporating a broad set of virtual skills, including speech recognition, NLP, voice biometrics, TTS, transcription and API integration. A simple drag-and-drop interface makes it possible for even non-technical staff to customize and build additional functionality into their intelligent virtual agents’ skillsets.
In Closing
It’s clear to see how speech is quickly becoming the preferred self-service platform for businesses looking to connect with customers in a helpful, authentic manner. Improved speech recognition and NLP, coupled with reduced cost and complexity, are removing barriers that have long stood in the way of smaller organizations adopting powerful, self-service solutions to better connect with their customers.
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