Contact centers are dependent on access to the right data, to ensure they can consistently deliver incredible experiences to customers. Now more than ever, agents need insights to help them adapt rapidly to changing customer expectations and preferences.
Conversational analytics tools have emerged as a critical resource for extracting valuable insights from every interaction. In fact, the market for conversational AI is currently growing at a CAGR of 21.5%, thanks to the incredible value this technology can offer.
However, many companies still don’t understand the full potential of these tools. Here’s your insight into some of the amazing use cases you can explore with the right analytical tools.
1. Automating Quality Assurance
Quality assurance is essential in any contact center, offering a comprehensive insight into the performance of your employees, and customer satisfaction. However, a comprehensive QA strategy can be time-consuming and complex to implement with many teams only monitoring 2-3% of the total interactions.
Conversational analytics tools can speed up and automate quality assurance strategies, using artificial intelligence to score every interaction with pre-defined criteria. That means you get a more consistent, accurate, and holistic view of QA metrics, and you save crucial time on analysis – which can be better spent on coaching your agents.
“This really is a game-changer for QA. While we wouldn’t recommend going ‘full AI’ – you still need your QA team’s expertise to dive into those must-monitor interactions – the ability to get the whole picture from 100% of your interactions is undeniably powerful. Your QA team’s time can be put to better use, focusing in on those conversations that the AI flags as potentially high-risk and validating the results of the GenAI.” - Matt Jones, Product Manager at evaluagent.
2. Measuring Sentiment
Today’s conversational analytics tools don’t just monitor the words said in a conversation. With advanced AI algorithms, they can examine the tone of each discussion, offering insights into both employee and customer sentiment.
With a clear view of customer sentiment, companies can determine which actions lead to happy or unhappy customers, and build training resources for their teams accordingly. What’s more, they can maintain a holistic view of employee engagement, and potentially reduce talent turnover rates.
3. Monitoring First Contact Resolution
With powerful AI algorithms, and the ability to process huge amounts of conversational data, the right analytical tools can help you to track critical metrics and drivers of cost such as FCR.
You can use these tools to gain rapid insights into how often agents resolve customer queries during the first conversation, and even allow employees to monitor their own first contact resolution rates. The data you gather could even help you develop training strategies that help employees address issues at the first time of asking and reduce costs..
4. Spotting Broken Contact Center Processes
Conversational analytics tools don’t just help you to discover where your contact center is excelling. They can also offer a behind-the-scenes look at the processes you need to correct or improve.
With automated QA tools, you can discover bottlenecks and issues within your processes, ensuring you can optimize business outcomes over time. You might even discover ways you can reduce contact center costs, by eliminating problematic processes and self-service failures that lead to complaints or customer churn.
Nowadays, software platforms can give you instant insights into specific problem areas – whether it’s refunds, complaints, returns, renewals – you name it. These reports have the potential to go beyond the contact center and create a positive outcome for the business as a whole.
