Your contact center collects an incredible amount of data every day. This data can tell you everything about your operations, from patterns and trends in customer experience to agent performance, satisfaction rates, and employee productivity, and give you a comprehensive picture of your call center's overall performance.
However, you need to invest in QA monitoring tools to extract insights from your data and use them to improve your call center operations. After all, data overload is a severe problem for many contact centers. Without the right data management tools, it's tough to improve performance or meet your customers' ever-evolving needs.
Call center data is high-volume and high-velocity, and it comes in a wide variety of metrics. There's a reason why only 37% of organizations feel like they effectively use advanced analytics to create value and uncover missed opportunities. Generating actionable insights is not the easiest or simplest thing to do, and insufficient data costs companies an average of $9.7 million per year.
Unfortunately, most call centers don't take full advantage of the available technology and QA monitoring tools. And when they don't have the right tools in place, these organizations miss out on potential customer experience improvements. Data blind spots also mean that managers lack the critical internal and customer-facing context to understand what's happening in their call center.
When you don't use the right tools to monitor, capture, and analyze your call center data, it's challenging to evolve from a primary customer service center to a strategic differentiator. You're also at risk of capturing too much data, little data, inaccurate data, or poor quality data.
But if you apply the right QA monitoring tools and advanced analytics, you'll reduce costs, increase revenue, and boost customer satisfaction scores. According to McKinsey, companies that apply advanced analytics to their call center:
So, the question is, which QA monitoring tools can help your call center streamline large amounts of data into digestible, actionable reports and insights? There are many general approaches to analyzing call center data, including:
But what if your contact center had to choose just one of these QA monitoring tools to implement right now?
Text analytics is one of the most competitive and comprehensive analytics tools in the technology market. Using correctly will help your entire team—mainly your QA managers—simplify data analysis and maximize insights.
Text analytics is an invaluable tool if you communicate with your customers through live chat, social media, email, or you have call transcripts. It uses natural language processing and AI to break down 100% of structured and unstructured text into keywords, phrases, emotions, themes, trends, pain points, and sentiment. It analyzes and extracts crucial insights in a readable and actionable format.
With text analytics, QA managers can:
But how exactly does text analytics provide QA managers with all of this insight for necessary contact center improvements? It's all about the data you measure and the key performance indicators (KPIs) you keep in mind.
There are several metrics and KPIs that QA managers can use in conjunction with text analytics to monitor performance in the contact center, such as:
These benchmarks help QA managers keep track of the contact center's efficiency and efficacy, but only if they're backed with text analytics insight that reveals "why." After all, you need more detailed information than a number or percentage point to improve customer experience and agent performance.
For example, say an agent has a much lower FCR rate than their coworkers. A QA manager might automatically assume that this agent is terrible at resolving issues, understanding the customer, or determining the actual pain point. But, with text analytics, you might find that this top-performing agent is regularly sent the most complex customer issues that other agents aren't trained to handle. In this case, the top performer needs more resources, and other agents need more training.
Similarly, it's challenging to identify the cause of a low CSAT score when you don't have insights into customer behavior. A lack of clarity around common pain points or trends in your customers' emotional state makes it challenging to assess agent performance or identify ways to improve. With text analytics, QA managers can dig into exactly what customers feel about the company's product, services, and experiences to improve CSAT scores significantly.
One of the most valuable business decisions a contact center manager can make is implementing QA monitoring tools. These analytics tools are essential in establishing a consistent and effective way to monitor your performance from the agent and customer perspective. This is invaluable for helping your QA team take significant steps toward better customer service.
With advanced analytics technology—such as text analytics—you can dig below the surface and get a real handle on your contact center's data. And this insight will help you uncover every issue, identify knowledge gaps, reveal learning needs, improve agent training, and enhance the customer experience. It's about gaining a complete view of your customers' wants and needs and how you're meeting them or not.
Learn more about text analytics in the call center by reading our guide.
