Next Generation Contact Centers Have the Ingredients for Happy Customers
“The contact center has its mojo back!” These words by Aspect Software’s Chris Koziol kicked off the ACE 2018 Conference last month and reflect how organizations and industries are thinking about new omnichannel technologies and approaches to keep pace with rapidly evolving consumer expectations. From a sommelier chatbot that can pair wine with foods to a chatbot that helps customers navigate automotive repair and service manuals, the use cases are as vast as they are fascinating. What’s more, additional AI use cases will continue to emerge over time as contact centers and organizations explore new ways to creatively foster dialogue and engagement with their customers.
Contact centers are certainly back in the limelight, but not in the traditional sense. As companies combine their front lines of human agents with automated search and retrieval mechanisms (like chatbots and conversational search), a powerful and innovative ecosystem emerges. This new ecosystem can drive a richer, deeper, and more memorable customer experience.
Overwhelmingly, recent trends and customer discussions point to an increase in the use of self-service, chatbots, and other channels to interact with both internal stakeholders and end customers. Self-help web portals, for example, are a tangible way to drive a more holistic customer experience. Many inbound requests that may have previously been handled through a traditional call center are now being routed to these self-service mechanisms.
This increase in the use of the self-service channel signals a seismic shift in the way customers want to find answers and interact with organizations they do business with. The change is driven in part by generational preferences. Millennials are less likely to engage through traditional inbound voice channels and more likely to use online approaches, particularly mobile devices. Companies are pursuing self-service mechanisms to lower operational costs and free up staff to tackle other tasks and initiatives, so in some ways the shift is beneficial to customers and companies alike.
Self-service channels are not perfect, however. If customers have spent too much time seeking a resolution they typically reach a trigger point at which they will abandon the process. Companies that understand the paths of the abandonment process can allow the chatbot to drive the customer to the information they seek, improving the overall quality of the interaction. In this scenario, including an “off ramp” to a human agent is essential because it provides a natural escalation point. As Lois Frankel once wrote, “Feedback is a gift.” Escalations within the customer journey are not necessarily a bad thing. Customer feedback (gathered through escalation or human interaction) can be a valuable way to explore actionable areas of improvement within the workflow.
While the arrival of new omnichannel technologies and approaches is exciting, it needs a strong foundation of relevant information. Knowledge engineering is the keystone to creating a scalable and extensible customer experience within the context of AI channels.
Knowledge engineering entails structuring and managing the underlying information assets to be quickly located (and in the correct context) to align customers with the information they seek.
Building a knowledge base becomes even more critical as an organization progresses beyond a small proof-of-concept project and moves from sandbox into production.
As Seth Earley noted during his keynote speech at the ACE Conference, “Managing vast amounts of knowledge requires a knowledge engineering approach.” Earley Information Science (EIS)’s primary charter is to organize information to drive measurable outcomes for its customers. To bring this principle to life, EIS showcased a chatbot solution at ACE. The chatbot was jointly developed with Aspect Software, and demonstrates the power of aligning knowledge engineering and taxonomy management best practices with chatbot functionality.
Through the alliance, Aspect and EIS engineered a scalable chatbot solution using Aspect CXP that centers around search and retrieval for sales enablement. The chatbot has many cross-industry applications that will generate ROI during an initial proof of concept phase while readying an organization for a larger scale AI program. The joint solution is a testament to EIS’s experience with knowledge engineering and Aspect’s commitment to solving customer problems upstream before they get to the contact center.
EIS would like to thank Aspect Software for its hospitality in hosting an enlightening and informative week. We plan to continue working together in partnership to develop solutions that combine our respective skills and expertise for the benefit of our customers.