Interactive voice response (IVR) allows a company’s computer system to engage with customers through voice and touch-tone telephone keypads. From its introduction in the 1970s, it has become increasingly common thanks to increased CPU power and advances in speech applications that have made the technology cheaper to deploy. Today, it is evolving into the Conversational IVR model that offers much more than a simple customer routing system.
The main purpose of the traditional IVR system is to act as a self-service option that enables customers to help themselves to quick solutions, while increasing the containment rate by avoiding transfers to live agents, thereby freeing them up to handle more strategic or complex enquiries.
However, despite the efficiencies IVR offers, these robotic menus are seldom well received by consumers. Research reveals that 61% of consumers think IVRs provide a poor customer experience because they force them to listen to irrelevant options (63%), stop them from getting through to a live person (54%), present long menus (46%), and force them to repeat themselves (45%). This negative experience causes severe frustration – 27% of consumers say they have abandoned calls to a company because they encountered an IVR.
But times are changing. A range of emerging technologies – specifically conversational AI and Computer Vision – are transforming legacy IVR systems into next-generation conversational platforms that cater to emerging consumer demands for more intuitive, immersive experiences.
The move toward conversational platforms
Conversational AI platforms – better known as chatbots – have become the go-to technology for automating and scaling simple customer episodes. According to Gartner, by 2025, 30% of major enterprises will be utilizing conversational platforms for both customer service interactions and to improve employee effectiveness. These new platforms are projected to deliver annual savings of $8 billion by 2022.
The adoption of conversational AI platforms will reduce the need for separate hierarchical IVR systems, which are menu-based and are rarely used as a front end for business applications outside of the contact center. A conversational AI platform can serve as the front end for all customer interactions: on the company website, via SMS, on social media platforms, or in other customer-facing applications. Using Machine Learning approaches to understand customer intent, these platforms have become increasingly conversational, allowing customers to have an open-ended dialogue or to make complex requests. While most conversational AI platforms began with a text-based focus, they are now adding speech – and increasingly vision – to their list of capabilities. These platforms are the channels of the future but in many cases, they will be built on existing IVR flows, using the customer journey insights gathered over many years.
Conversational IVR transition challenges
Although the advantages of shifting from legacy IVR to a conversational model are clear, the transition can be challenging. Company leadership must determine which elements of their self-service strategy – including digital, web, voice and visual channels – should be prioritized in order to achieve the greatest ROI.
Once strategy is determined, the multiple technology options available for converging IVR and conversational AI platforms must be weighed. Organizationally, converging these channels can be challenging when different departments – with different priorities – are involved. Those responsible for IVR may favor a voice-first approach to customer engagement and see text chat as a separate channel, whereas those responsible for text chat may envision these solutions as a small part of the bigger AI picture. Leadership must ensure that both the strategy and the organizational implications of the convergence are considered.
Focusing on the customer experience can ensure that the shift to conversational IVR is successful. CX is now at the heart of many companies’ growth strategies, viewed as a key brand differentiator, even more than price and product. Here are some tips to hit that sweet spot.
Know your customer: to provide a positive experience, companies must get to know their customers better in order to understand their needs and challenges. They must take a hard look at the details involved in every interaction a customer has with the business along the entire journey.
Evaluate customer journeys: companies should scrutinize their current processes and determine which customer journeys or use cases would benefit most from the shift to conversational IVR. Common scenarios generally account for 60% of customer issues, especially for telcos, insurers, consumer electronics suppliers and utility providers.
Consider each customer’s perspective: not all customers have the same familiarity or level of comfort with technology and will therefore not respond to new self-service options in the same manner. Make sure to incorporate a “voice of the customer” program that incorporates customers’ feedback about their experiences and expectations into the planning process.
Anticipate new use cases: consider any new customer journeys that must be designed to align with upcoming company initiatives or products. For example, an insurance company may have multiple known customer journeys for setting premiums, onboarding customers to new policies or making claims. However, an insurer that is entering the Smart Home market with new products such as smart security systems will need to create an entirely new conversational flow.
Measure effectively: as with any business initiative, the shift to conversational AI must be measured to determine its level of success. The goal of the move is to help the customer find the answers they are seeking in a fast and convenient way, without expending significant effort. A key KPI to measure this initiative is the Customer Effort Score (CES), a CX metric that uses a simple question to measure how much effort a customer must exert during an interaction with a company. Low effort is now seen as the key driver of customer loyalty. By tracking CES, businesses can make the necessary improvements to further enhance the customer experience.
Adding vision to the mix
Visual communication is further revolutionizing conversational AI platforms. Customers can now use text, voice and images to help a virtual agent understand their problems. Powered by Computer Vision AI, automated visual assistants can interact in real-time video mode, enabling the customer to show their issue and receive interactive AR guidance on their smartphone screen. The visual virtual assistant can also provide feedback and correct the customer as required. For example, when unboxing a new router, a virtual technician recognizes the cables and inputs and guides the customer using AR on which cable to put where. It can see the customer’s physical space, advise on the optimal locations to place the router, recognize the LEDs, and help the customer complete the installation as quickly and effortlessly as possible.
Using emerging technologies – specifically conversational AI and Computer Vision – to transform IVR from a frustrating customer experience into an effective self-service process will be a key goal of many customer-facing organizations in the coming years. To succeed in moving toward next-generation intent-based conversational platforms, companies should start planning how to deal with the inevitable leadership and organizational challenges and start strategizing how best to focus their efforts on the right use cases and scenarios.