For years, contact centers have talked about creating a 360-degree view of the customer.
In 2025, it’s still a key industry talking point.
Ultimately, that suggests creating an all-encompassing customer view – on a single screen – is much easier said than done.
Yet, Gartner hasn’t given up on the possibility.
In a 2024 report entitled "How to Evolve QA Into a Strategic Quality Intelligence Program," the analyst coined the term "quality intelligence".
Fundamentally, quality intelligence brings together three key streams of contact center data:
- Traditional Quality Management (QM) Data: Conventional agent performance insights.
- Conversation Intelligence Data: Additional intelligence into contact center conversations, including insights into customer moods and intent.
- Voice of the Customer (VoC) Data: Feedback and input directly from customers.
In combining these three data steams - via solutions like the evaluagentCX platform - Gartner posits that contact centers can create a holistic view of a customer’s service experience.
With this more comprehensive view, the contact center may improve QM processes, enhance coaching workflows, and even engage in brand monitoring.
Yet, perhaps most crucially, if all this quality intelligence data filters into a CRM or CDP, it can contribute towards a view of the customer that spans customer-facing teams.
Not only will that help service agents troubleshoot, but it will connect sales, marketing, and commerce teams, removing data silos that scupper customer experiences.
4 Steps to Achieving Quality Intelligence
Contact centers are often a data black hole. As a result, all customer-facing teams miss out on opportunities to improve the customer experience.
By following these steps for achieving quality intelligence – as put forward by Ben Cave, Product Director at evaluagent – contact centers can begin to fill the void.
- Data Consolidation
Bring all relevant quality intelligence data together in a single framework. This includes not only conversational insights but also VoC from sources outside the core contact center platform.
Of course, this is easier said than done. However, many CCaaS providers – including AWS, Cisco, and Five9 – are layering data lakes over their platforms to support service teams in this endeavor.
- Uncovering Hidden Intelligence
Utilize AI to analyze the data in ways humans cannot, monitoring new predictive and, ideally, prescriptive metrics.
Predictive metrics forecast future outcomes, while prescriptive metrics suggest specific actions based on past events.
The goal is for AI to discover unexpected insights within the data – the "unknown unknowns" – such as emerging trends, surprising topics, and previously unnoticed CX deficiencies.