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?
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.
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.
Tools for contact center agent management have changed considerably in the past few years. The suite of software applications has grown from those that perform core functions in scheduling and quality control to include more advanced solutions for agent guidance, integrated desktops, and workflow and automation design. One area of intense investment by vendors has been analytics, specifically for assessing customer satisfaction and hearing the “voice of the customer.”
Account-based marketing (ABM) serves as a model for how a complex manual activity can be improved and automated. That does not mean it always goes well. ABM is a practice that many B2B marketers swear by, but it can be very difficult to get a technology-enhanced ABM program up and running.
The primary effect of the pandemic on agent/workforce management beginning in 2020 was a rush to re-site and re-equip contact centers and agents. This was achieved with a surprising speed and smoothness industrywide. But once achieved on a “temporary” or “emergency” basis, it became clear that this shift was going to be semi-permanent. Even if the majority of the agent population moves back into standard centers, there is now a consensus that some portion of the workforce will work from home. As a result, the industry’s vendors have focused their messaging and short-term roadmaps on enhancing workforce engagement, or what we call agent management. These include promoting tools for measurement, management, and supervisory tools like mobile interfaces into workforce systems.
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
Dialpad provides contact center and business phone services, a market that is in transition due to a convergence of technologies and business conditions.