Analyst Perspectives

The contact center is where much of customer experience management happens, but it is by no means the beginning or end of the CX story. Other departments, notably marketing and revenue, have a stake in what happens during the customer’s lifecycle. Whether it’s developing offers, orchestrating interactions or assessing behavior and intent, handling the complexity of the relationship requires a broad set of participants who are not always in alignment.

With the emergence of multiple selling channels and the rise of the subscription model, the need for a unified approach to revenue planning and execution should be a priority for every organization. As I have written about in my analyst perspective Revenue Management: The Opportunity for Innovation and Optimization, this need to unify the approach and focus on alignment across all revenue supporting teams in furtherance of an organization’s objectives and targets is of key importance to ensure...

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...

The emergence of the Chief Revenue Officer (CRO) has mirrored the adoption of the subscription model and the development of multiple selling and buying channels over and above the traditional direct sales model, referred to as Revenue Management. Supporting the traditional sales team and management was the sales operations team with responsibilities around incentive compensation, territory and quota planning, sales metrics and reporting and sales forecasting as well as sales engagement and...

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?

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.

The age of digital communication platforms is upon us. The need has never been greater for an open and secure platform that satisfies the demand for enterprise-grade business and technology requirements. Overcoming today’s technology challenges is possible with a next-generation cloud-based delivery model that is both extensible and reliable. Organizations considering a digital communication platform can take decisive steps to ensure digital communications for everyone — inside, outside,...

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.

The work environment today demands that your organization advances the efficiency to execute business processes for continuous operations to have a positive impact on business performance. The capability to be responsive to any range of minor to disruptive business events is required to support business continuity and level of organizational readiness to meet the needs of digital business. Ventana Research asserts that in 2025, one-quarter of organizations will remain digitally ineffective in...

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...