Most of the discussion and planning around artificial intelligence (AI) tools for contact centers and CX has focused on finding appropriate use cases and understanding how to deploy these tools. There are already plenty of success stories about enterprises reducing friction for agents, saving time and expanding the breadth of interactions that can be handled automatically.
There are contact centers, and there is customer experience. They are not the same thing, even though we have a tendency to conflate them or at least discuss them in tandem for operational and planning purposes. This is mostly fine, but it does obscure one basic difference that will matter more as enterprises succeed at integrating contact centers into enterprise CX strategies. That difference is in metrics and, by extension, how we define success.
One way to think about the change in service and support in the current dynamic technological environment is to see it as a transition from managing interactions as they happen to managing the overall customer journey. It’s a lot harder to optimize a journey than an interaction. More people are involved, with different systems, key performance indicators and data sources, to cite just a few differences.
Zoho’s recent analyst conference was an opportunity to explore how the India-founded software company is faring in its efforts to extend its enterprise business, and to place AI at the center of its product portfolio. It appears the company is having some success on both fronts.
The breadth of customer experience (CX) solutions created by software providers in recent years has expanded into new areas, usually related or adjacent to existing, more traditional toolkits. Most providers in the space began as more-or-less pure play contact center software providers, so the CX toolset starts with that set of technologies. Products are grounded in core functions for service delivery, like interaction handling, labor management and customer tracking. Some software providers...
Field service is an often-overlooked area of the service equation, in part because managing its operations involves tools and activities outside of normal contact center models. Field service has a communications component, which is partly handled by contact centers. However, the bulk of field service management activity has to do with organizing the work of technicians and dispatchers. It also requires constant attention to informational resources about products, assets, locations, real-time...
Software providers have identified “openness” as a key consideration for contact center buyers. Since there is no generally accepted rule about what makes a system open, it makes sense for us to examine what components contribute to that quality, and why it should be thought of as a good thing.
Verint is operating in quite a different marketplace for contact center and agent management technology than existed five years ago. We have seen tremendous innovation and expansion of the available technologies for running centers and optimizing the performance of the human labor pool, as well as an explosion of tools built to automate customer interactions.
With a flood of artificial intelligence-related tools now available for contact center buyers, it can be helpful to take a step back and review where buyers can expect a quick return on investment and maximum improvement in efficiency and productivity. For now, it appears that there are three broad buckets into which contact center-focused AI applications are clustered. They all relate to agent behavior and activity, including:
Sprinklr’s analyst day in September was an opportunity for the company to dive deeply into its progress in pivoting its product offerings to align with a broader perception of the market for contact centers and adjacent customer-related applications.
