First, it was generative AI. Now, agentic AI is the term many tech providers are shouting from the rooftops, and there are already several nifty use cases.
Yet, on their own, use cases will only drive so much value.
Instead, some contact center leaders are realizing a much bigger opportunity, and it starts with data and context.
These leaders are asking: Who are our customers? Why do they reach out? Who are our agents? What intents do they handle well? Where do they need training?
Then, finally, how do we connect the dots?
Ultimately, that’s a challenge that deploying a call summary tool or AI agent won’t solve. However, an integrated contact center data layer will.
Building a Contact Center Data Layer
To build a data layer, don’t jump to solutions. Consider: What data do we already have? What do we know we need to know? And what else should we be looking for?
Then, build those insights into a data layer, which can inform the contact center’s routing strategies, quality assurance, journey orchestration, and beyond.
That intelligence should be real-time and actionable, not just post-facto analysis, which requires extra human capital to make sense of.
Unfortunately, most CCaaS platforms aren’t set up for this. Instead, they rely on customers to integrate diverse information into their data lake.
However, Content Guru is flipping the script.
The Content Guru Approach
Content Guru offers a full-stack CCaaS platform with an AI orchestration layer, alongside integrated workforce management, quality assurance, conversational intelligence, and various other tools.
All these generate data that flows into the vendor’s adjacent customer data platform (CDP): storm CKS.
As contact centers migrate to Content Guru, they can start with the data they already have and get it structured and integrated before creating a data loop.
The data loop takes new data, like analytics, and feeds it in automatically.