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 serious problem for many contact centers. Without the right data management tools, it's tough to improve performance or meet your customers' ever-evolving needs.
QA Monitoring Tools That Streamline Call Center Data
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 bad data costs companies an average of $9.7 million per year.
Unfortunately, most call centers don't take full advantage of the technology and QA monitoring tools available. 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 they need to understand what's happening in their call center.
The ROI of QA Monitoring Tools
When you don't use the right tools to monitor, capture, and analyze your call center data, it's challenging to evolve from a basic customer service center to a strategic differentiator. You're also at risk of capturing too much data, too 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:
- Reduce average handle time by up to 40%,
- Increase self-service containment by 5% to 20%,
- Cut employee costs by up to $5 million,
- And boost conversion rates on service-to-sales calls by almost 50%.
Types of QA Monitoring Tools
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 popular approaches to analyzing call center data, including:
- Speech Analytics monitors calls in real-time, using machine learning and AI to analyze tone, sentiment, emotion, and satisfaction.
- Text Analytics breaks down all structured and unstructured text into key insights about trends, pain points, sentiment, satisfaction, and keywords.
- Cross-Channel Analytics optimizes data flows between all channels to alert agents to potential problems no matter the channel.
- Predictive Analytics predicts customer behaviors and identifies trends proactively to improve agent effectiveness.
- Performance Analytics provides real-time and historical data about all agent performance to capture insight on inefficiencies, training, and more.
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. When used correctly, it will help your entire team—particularly your QA managers—simplify data analysis and maximize insights.
Text Analytics Insights for QA Managers
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. From there, it analyzes and extracts crucial insights in a readable and actionable format.
With text analytics, QA managers can:
- Develop a clear vision and strategy for necessary improvements in the contact center.
- Build in-house talent capabilities based on knowledge gaps, training needs, and strategic goals.
- Improve performance and productivity metrics—such as reduced handle time—by analyzing specific customer interactions based on length, keyword, sentiment, emotion, and trends.
- Enhance customer satisfaction rates by identifying potential areas for customer experience improvements and optimizing processes.
- Proactively identify and mitigate issues before they seriously impact customer service.
- Identify gaps and systemic issues by monitoring and analyzing all agent interactions and comparing low-performing and top-performing agents.
- Increase workforce resilience and improve routing by proactively modeling service level needs based on trends, backlogged issues, and customer difficulty.
- Improve service-to-sales conversion by assessing real-time customer insight to predict when customers are likely to buy.
- Better understand customer pain points by analyzing every customer support ticket for keywords, phrases, sentiment, and emotion.
- Increase emotional intelligence (empathy) in the contact center by matching agent performance to customer satisfaction and attitude.
How to Use Text Analytics to Gain Insight as a QA Manager
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:
- Average Time in Queue
- Average Abandonment Rate
- First Contact Resolution (FCR)
- Agent Turnover Rate
- CSAT and NPS
- Contact Quality Manager Score
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 bad at resolving issues, understanding the customer, or determining the true 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 clarify around common pain points or trends in your customers' emotional state makes it challenging to assess agent performance or identify ways for them to improve. With text analytics, QA managers can dig into exactly what customers feel about the company's product, services, and experiences to significantly improve CSAT scores.
There is Immense Value in QA Monitoring Tools for the Contact Center
One of the most valuable business decisions a contact center manager can make is to implement 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.