Keith Dawson's Analyst Perspectives

Ventana Research recently announced its Market Agenda in the expertise area of Customer Experience. CX has emerged as a way for organizations to demonstrate value and stand out in the marketplace. The technology underlying modern CX is transitioning from tools that are based on communication to those centered on data analysis and process automation. This allows organizations to build great experiences and reap the benefits in customer loyalty and value. It also forces companies to reckon with the complexity and disruption that technologies like artificial intelligence and automation bring to an organization.

Topics: Customer Experience, Voice of the Customer, CEM, Self-service, Analytics, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service

Any organization that relies heavily on a large labor force looks to automation to reduce costs, and contact centers are no exception. They handle interactions at such large scale that almost any effort to automate some part of the process can deliver measurable efficiencies. Two factors have ratcheted up attention on automating customer experience workflows: the dramatic expansion of digital interaction channels, and the development of artificial intelligence and machine learning tools to facilitate workflow deployment.

Topics: Customer Experience, Analytics, Data Integration, Contact Center, Data, AI and Machine Learning, agent management, data operations, Customer Experience Management, Field Service, customer service and support, digital business, Experience Management

When migrating their communications stacks to the cloud, many organizations come face to face with a quandary: do they emphasize the business phone system and gravitate toward a unified communications vendor? Or should they focus on the specific applications needed for running their contact centers and seek out a CCaaS vendor?

Topics: Customer Experience, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service

When NICE acquired inContact in 2016, it began a transformation that saw it broaden its product offering and positioned itself to play a larger role in the contact center and customer experience industries. It was a prescient move, creating a firm that could supply end-to-end contact center functionality in the cloud. And it anticipated today’s market dynamic, in which NICE and its competitors are racing to define (and capitalize on) the post-contact center future.

Topics: Customer Experience, Voice of the Customer, Business Continuity, Analytics, Contact Center, Data, Digital transformation, AI and Machine Learning, agent management, Customer Experience Management, Field Service, customer service and support, digital business, Experience Management

In part one of this Analyst Perspective on the use of artificial intelligence within contact center applications, we focused on the evolution — and resulting benefits — of tools embedded with AI, including ease-of-use for non-data-scientists.

Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management, Field Service, customer service and support

When artificial intelligence emerged from the labs and vendors started offering it as a component of their software, many contact-center buyers shied away from it. From their point of view, AI and machine learning tools were new, expensive, relatively untested and had an uncertain use case. This stance was understandable, as contact center professionals are traditionally expected to be risk-averse when deploying technology into their operations. Contact centers are, by design, supposed to be hardened, mission-critical sites of high reliability. There has historically been a bias towards avoiding new technology, deploying only when it has been thoroughly vetted across the industry.

Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service, customer service and support

Customer support operations increasingly rely on automation and complex workflow processes to reduce costs and improve experiences. Automation also allows organizations to make their service processes richer, incorporating information and staff from back offices, for example, or embedding conversational tools into contact center processes.

Topics: Customer Experience, embedded analytics, Analytics, Contact Center, natural language processing, AI and Machine Learning, agent management, Customer Experience Management, Field Service, Process Mining, Streaming Analytics, customer service and support

Customer Service & Support (CSS) is a software segment that provides tools for tracking and resolving customer problems, primarily through contact centers. The segment has been mature for decades but today is reinvigorated by a new emphasis on workflows and automation. Vendors, like ServiceNow, have been innovative in developing new technologies for managing self-service and field service, and providing agents with contextually relevant information during interactions. The new technologies brought to bear on this include artificial intelligence (AI) for knowledge search and delivery; agent assist and guidance tools; and SMS-centric customer messaging.

Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, Product Information Management, Digital Commerce, AI and Machine Learning, Subscription Management, agent management

Customer service and support (CSS) is a term with two meanings. Most generally, it refers to the functions of a contact center in handling post-sales customer inquiries that require some effort or action on the part of the business. More specifically, it refers to the elements of the software stack that facilitate those operations, primarily case tracking and trouble ticketing.

Topics: Customer Experience, Analytics, Contact Center, AI and Machine Learning, agent management

Voice of the Customer (VoC) is a catch-all term that refers to the collection of customer feedback in various formats. Sometimes this feedback is in the form of quick surveys or reactions to questions like, "Did I resolve your issue today?" or "Would you recommend our service to a friend?" Alternatively, it can be derived from less specific but more numerous data signals that span multiple interactions or across a customer base. Most businesses make an effort to capture some customer feedback.

Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management