How Conversation Intelligence Is Revolutionizing the Call Center Experience
The humble call center is no stranger to innovation. From first-generation call routing systems that triage customer requests and connect them with call handlers, to interactive voice response (IVR) systems that allow customers to self-serve over the phone, contact centers have been at the forefront of communication technology.
This innovation, particularly in the early 2000s, has typically been geared toward increasing operational efficiency and reducing overhead. For a while, the idea that call centers could be fully operational without human agents not only seemed plausible, but actually desirable. But then things changed. The smartphone revolution created an always-on landscape where customer experience was king. Companies would compete to carve out the most effective and engaging customer journey, pairing back their use of automation to preserve the human element they once proudly eschewed. That's where we are today. People still want to self-serve on the internet, and the smoother that experience the better, but when it comes to picking up the phone and having a conversation, people generally want to speak to people.
That doesn't mean artificial intelligence has no role to play in call centers; we just weren't looking at it from the right perspective. For a time, the industry was so caught up in whether AI could replace the human element that we've only recently begun exploring how AI could support and enhance it. That's where conversation intelligence (CI) enters the fray.
Reading Between the Lines
When people hear the term conversation intelligence, their mind often snaps to automated chatbots or virtual assistants designed to help customers self-serve. But perfect artificial intelligence isn't really the goal when it comes to human-to-human communication. We don't want to replace conversation; we want to improve it. That's what CI allows us to do. There's a seemingly limitless amount of useful information locked in every conversation, from speaker pace and ratio to caller sentiment and the number of topics covered.
Even the silence between the words can reveal a lot about the speakers involved: their level of engagement and their motivation. By leveraging AI, we can create easy-to-digest analytical summaries from conversations or groups of conversations that can tell us where those conversations are going well and where they could be improved in the pursuit of better service and more positive outcomes.
We're all used to hearing the disclaimer "This call may be recorded for training purposes" at the beginning of a call with a customer service agent. The problem is that a significant portion of that recording's value remains untapped. In most cases it's temporarily archived in-case of a customer complaint, while some recordings might be saved to one day see the light of day in a staff training seminar.
Instead of recording or transcribing a call and then filing it away, what if contextual insights could be gathered in real time to help steer interactions and offer more rounded, continuous coaching? Sentiment analysis could reveal details such as agent empathy and customer satisfaction levels. Key phrase monitoring could determine the kinds of questions being asked and how quickly customer queries were resolved. Among sales teams, top performers and model behaviors could not only be identified, but studied and analyzed to understand precisely what made them so effective. By tracking sentiment and caller intent in real time (or asynchronously), data on customer dynamics can be pooled over time to improve customer satisfaction scores and conversation rates. According to a recent report, taking this Big Data approach to customer service can improve productivity by nearly 60 percent and lead to 36 percent better decision-making.
What's more, 55 percent of call centers spend up to 12 weeks training and onboarding new agents. Using some of the capabilities outlined above would enable new agents to hit the ground running and learn on the job with real-time course corrections as needed.
CI has a great deal of potential when it comes to training call center staff, but it can also empower them and make it easier for them to meet customer needs. Custom keyword tracking could be set up to automatically equip handlers with real-time, step-by-step assistance in resolving customer queries. Rather than putting customers on hold while they find the right information or escalate to another member of the staff, handlers will have everything they need while the conversation is happening in real time. This technology could also create automated workflows and escalation paths so that if a hand-off from one agent to another is required, it can be done with minimal inconvenience to the customer.
Calls can also be transcribed with an incredible amount of insight and context already baked in so key action points and call summaries can be automatically reported. Instead of manually entering updates onto customer files for the next agents to pick up, details of previous conversations will be logged automatically, with key points and phrases highlighted. In this way, CI's benefits extend beyond case-by-case interactions, allowing call center managers to uncover macro trends and apply data-driven improvement strategies.
Perhaps most impressive of all is that CI technology is already here and can be easily integrated via APIs into most technology stacks. By leveraging CI capabilities, contact centers will not only preserve the human aspect of their service models but elevate the human element beyond the reach of competitors.