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Imagine if your car only had a speedometer and fuel gauge. Sure, you could theoretically drive from point A to point B without going over the speed limit or running out of gas. But what if your tire pressure or oil level were low? What if your engine was overheating? Without the other instruments on your dashboard, you’d be blind to critical information about the health of your vehicle. The same goes for your contact center analytics dashboard.
Analytics dashboards contain a wealth of information. Contact center metrics and key performance indicators (KPIs) are consolidated and visualized here, often in easy-to-digest charts, graphs and values. The best contact center dashboards provide a snapshot of historic and real-time data from an aggregate of sources. However, as valuable as dashboards are for assessing contact center health and performance, the picture they paint is often incomplete. So, what critical information are they missing? Context.
Data alone isn’t enough
Contact center dashboards run on numbers. The data they analyze is represented in stats, percentages and projections. While these numbers are essential for performance monitoring and forecasting, they offer little insight into the driving forces behind the data. In other words, they lack the context to present the data in its fullest form. Take our automotive dashboard example. There’s a big difference between a car that was driven hard and poorly maintained and an identical vehicle that was pampered and routinely serviced—even if both odometers show the same mileage. The data—in this case, miles driven—isn’t enough. We need context—service records, vehicle history, etc.—to fully assess the health and condition of each vehicle. But where do we find that level of context in the contact center—and how do we analyze it?Analyzing for context
Every conversation a customer has with a business is dripping with contextual data. However, most contact center analytics solutions zero in on the raw conversational data—average handle time, call resolution rate, etc.—and look past more nuanced information sources, such as customer sentiment. This is largely because most solutions aren’t built to measure contextual data. Capturing, analyzing and integrating contextual data within a contact center dashboard requires a highly sophisticated analytics engine. That’s where conversational AI comes in. Combining natural language processing, machine learning and artificial intelligence, today’s leading conversational analytics solutions can accurately gauge those previously “intangible” factors, such as customer sentiment, intent and emotion. What’s more, they can integrate that contextual data with the greater aggregate to create a fuller picture of each customer conversation.Maximizing analytics intelligence
While AI has undeniably changed the call center analytics game, there is a caveat: to operate effectively, AI engines need data that is AI-ready. In other words, data that’s been accurately captured, transcribed and formatted for AI use. This has traditionally been a challenge for call recordings, which are often captured in low-quality, unstructured format and/or closely guarded by third-party software vendors. Without access to structured, AI-ready voice data, contact centers cannot realize the full contextual value of their call recordings. However, this too is changing. With the advent of open APIs, next-generation call recording software, like U-Capture, is enabling contact centers to capture AI-ready voice and screen data from every conversation. This data can be fed, in real time, to integrative analytics solutions, like U-Analyze, to maximize analytics intelligence—giving contact centers the fullest customer conversation picture possible.Better insights = better decision-making
Conversational analytics, AI-ready voice recording—what does it all mean at the dashboard level? The short answer: better, more actionable insights. By analyzing conversations for context, solutions like U-Analyze provide contact centers with more than just raw, numerical data—they show how that data tells a story. They identify the root causes behind conversational trends and performance metrics. As a result, organizations can make better decisions based on actionable intelligence instead of educated guesswork. That intelligence grows exponentially with the addition of AI-ready voice recording. By pairing conversational analytics with a solution like U-Capture, organizations can unlock a goldmine of contextual conversational data. Together, these solutions provide more than just a snapshot of contact center performance. Much more. They create a highly detailed, diagnostic image of what drives customer behavior. And they give call center leaders the tools they need to take meaningful action.Add Missing Context to Your Analytics Dashboard
Learn more about how U-Capture can turn voice data into actionable insights.