At a recent industry conference, the session on quality assurance began with the keynote speaker taking the mic, and boldly announcing: “when it comes to QA, monitoring one percent of one percent is BS, and we all know it.”
She was blunt. But she wasn’t wrong.
For most of the history of the contact center industry, the standard for QA has been for a team lead, coach, or supervisor to review and score three contacts per agent per week. One percent of one percent… at best. These efforts weren’t useless – they were, as noted, the standard. Coaches would get to know the agents on their teams. They could do spot checks for compliance. They could work on specific identified performance issues within the sample size.
But if that standard of sampling was the horse and buggy of our past, conversational analytics platforms are the Ferrari of QA today.
The contact center world has been talking about AI and machine learning, especially in the area of speech analytics, for a long time. The rate at which contact center technology has evolved accelerated during the pandemic years. Now, finally, access to AI-driven, machine learning-based conversation intelligence and quality analytics is a reasonable ask in your RFP process.
A small sample of customer care interactions has never really been enough to get an accurate big picture of what’s going on in your contact center. And as Forrester points out, the resulting dashboards and word clouds that point to top call drivers aren’t enough either.
The beauty of conversation intelligence solutions is the ability to analyze 100% of customer interactions, through phone, chat, and email. Conversation analytics software relies on AI, machine learning, and natural language processing to run deep analysis on every conversation. Imagine the transformation from static-ridden radio to ultra-HD TV—the difference is truly exceptional. Likewise, conversation analytics provides superior quality and visibility, and there’s no doubt it will transform the way contact centers approach customer experience strategy.
There are three specific areas where this technology will drive the contact center of the future. Read on.
When the QA process only samples a small collection of contacts, it’s difficult to identify trends that are happening until far too much time has already passed. Analyzing 100% of conversations in real time changes that. Suddenly, you can pinpoint—and address—trends before they become bigger problems.
Conversation analytics can measure a wide range of factors, including a customer’s emotions and stress as well as the agent’s empathy. It can identify specific words and phrases that provide deeper context for what a customer wants and expects from the conversation. It can also help diagnose what’s happening when call times are too long or when there’s too much silence on the line.
This analysis is real-time, so software and platforms have the option to provide immediate prompts and cues for agents to respond to the customer appropriately. This alone can prevent a contact from re-routing or escalating to a different channel or higher tier of agent support. Importantly for the customer, it can also increase the rate of first-call resolution and improve average handle time.
With customer habits and expectations evolving at a faster rate than ever before, the ability to keep up is critical. Conversation analytics platforms make that possible.
Happy employees make for happy customers. The best outsourcers seek to optimize the agent experience, and they can leverage conversation intelligence tools to achieve greater success toward this goal.
When 100% of interactions are being analyzed, coaching and training can be highly tailored to each agent’s specific needs based on strengths and opportunities. It’s far less likely that one or two contacts will skew the picture of an agent’s performance—which can be a particularly frustrating situation for the agent. Conversation intelligence can help identify gaps in agent knowledge as well as other areas of underperformance so that coaches and supervisors can provide more valuable support.
Additionally, as mentioned above, these sophisticated platforms can provide agents with real-time cues based on emotion analysis or other specific support needs. Average handle times and first-call resolution are factors that are just as important to the agent as they are to the customer—and an agent is more likely to feel empowered and confident when they are equipped to succeed.
Ultimately, conversation intelligence provides far greater visibility. It opens a window into who your customers really are—what they want, what they’re asking, and what they’re challenged by. Simultaneously, it’s a clearer view inside the operation—enabling a better understanding of your processes and allowing you to better evaluate the success of your training programs and escalation procedures. Automated conversation analytics also free up QA staff, redirecting their efforts to more valuable tasks than simply listening to calls.
Significantly, these platforms can also identify gaps and issues that can be addressed by self-service channels or outside of your contact center entirely. In many cases, trending customer issues provide insight that can help other departments—such as product engineering, sales and marketing, accounting and finance, or risk and compliance—improve their processes and best practices. In these instances, contact volume can be reduced (and, therefore, cost driven out of your business), with issues resolved before a customer even thinks to pick up the phone.
Ultimately, conversation analytics software empowers the best of both worlds; AI and machine learning to provide a deeper and broader perspective of your contact center operations and the human element to leverage that intelligence to better train and coach agents to deliver a more meaningful customer experience. It’s a win-win situation.