For decades, the customer engagement center (CEC) and contact center (CC) have been integrating at arm’s length, with limited sharing of customer interaction channel functionality and data. The result has been a fragmented customer experience in which the customer has to guess which channel will yield the best answer fastest — but often trying multiple channels in hopes of getting a more favorable outcome.
CSS leaders should approach the broader set of technologies as an integrated ecosystem of functionality
“The fragmented nature of these systems has made it difficult to leverage consistent analytics and knowledge tools for gathering, analyzing and sharing critical information and recommendations to customers and employees,” says Drew Kraus, VP Analyst, Gartner. “Customer service and support (CSS) leaders should approach the broader set of technologies as an integrated ecosystem of functionality, rather than a set of compartmentalized and separate decisions and systems, to deliver a more holistic customer experience.”
The Gartner Hype Cycle for Customer Service and Support Technologies, 2019, describes the most critical technologies for supporting customers as they seek answers, advice or resolutions to problems, either through a variety of interaction channels or enabling customer-facing employees to deliver the resolutions and advice. The CSS Technology Hype Cycle combines the formerly separate but related Hype Cycle for CRM Customer Service and Customer Engagement and Hype Cycle for Contact Center Infrastructure. Four technologies from this year’s Hype Cycle are of high importance to CSS leaders.
Customer journey analytics
Customer journey analytics track and analyze the way customers and prospects use a combination of available channels to interact with an organization over time. Customers now frequently use multiple channels and switch among them to select/purchase services and receive care, making it important to link data from multiple channels and understand customer identities (when many are anonymous) and behaviors.
Journey analytics cover all channels — including human interactions, fully automated, customer assistance, physical locations and limited two-way interaction — with which customers and prospects engage. This provides more valuable insight than tracking by channel alone.
Virtual customer assistants
Virtual customer assistants (VCAs) act on behalf of an organization to engage, deliver information or take action on behalf of a customer. VCAs differ from chatbots in that they require more infrastructure, have memory and form a relationship with customers.
Only VCAs that create a compelling user experience and deliver business value will survive
The current generation of VCA deployments and other types of conversational agents are often implemented incorrectly, failing to capture customer intent or handle unexpected input elegantly. Only VCAs that create a compelling user experience and deliver business value will survive. As leaders look to multichannel engagement, VCAs should determine both the current and future states of this technology.
Chatbots are the most common use of artificial intelligence (AI) in organizations. This technology is composed of a domain-specific conversational interface that uses an app, messaging platform, social network or chat solution for its conversations. Varying in sophistication, text- or voice-based chatbots have been leveraged in social media, service desk, human resources, commerce and self-service.
Although already in use, chatbots still have enormous potential to impact the number of service agents employed by an enterprise, and how customer service itself is conducted. As chatbots begin to “learn” user wants, the use cases for the technology will expand to aid onboarding, training, productivity and efficiency inside the workplace.
Conversational user interfaces
Like Amazon Alexa and Google Home, which have already brought AI to customers at home, conversational user interface (CUI) offers a back-and-forth interaction in which the user and machine interactions primarily occur in the user’s spoken or natural language. Building on chatbots and virtual assistants, CUIs will create conversations that take user input and determine user intentions.
CUIs shift the responsibility between the user and the interface. In traditional user interfaces, the user is an operator of the technology and is largely responsible for the effects of using the technology. In a CUI, the CUI is responsible for taking the user input and determining the intention of the user. Conceptually, the CUI has taken over some of the responsibility that was once reserved for the user.
Knowledge management for customer service includes the creation, discovery and delivery of various forms of targeted content for support agents, customers, chatbots, peer-to-peer support communities and partners.
New uses of machine learning (ML), combined with communication via chatbots and devices, have created new opportunities and challenges for knowledge delivery. Organizations that have adopted a self-service approach to knowledge management are often supported by dedicated knowledge workers who constantly update and fine-tune the knowledge engine to improve the accuracy of responses.
This involves maintaining and expanding the collection and categorization of knowledge — thereby reducing dependence on humans to identify and categorize knowledge — by enabling faster retrieval of appropriate data elements, at the proper time and through a web-based or mobile interface.