By Vijai Shankar, VP Product & Growth, Uniphore
Whether it’s major news, a big event or a popular phenomenon, no one wants to be the last person to know what’s going on. Business technology is no different. And in customer service—particularly the contact center space—the topic dominating discussion is conversational AI. Today, it’s no longer a question of, “have you heard of conversational AI?” Instead, leading companies are now asking, “how can I use it to improve my business?” That’s important because those leading companies are setting the tone—and expectation—of tomorrow’s customer experience.
Just look at what Amazon has done for the online shopping experience. It not only created the preferred template for browsing, merchandising, etc.; it created the standard by which all other online retailers are judged. Customer service is no different. Today’s top customer service leaders are creating the template—and setting the standard—for the next generation of customer experience. And customers are taking notice.
In recent years, we’ve seen customer attitudes toward AI-driven CX shift from begrudging acceptance to wholesale preference. The global pandemic certainly helped accelerate this trend, but so have improvements in the technology. Today’s machine learning and natural language processing and understanding (NLP and NLU) capabilities are light years ahead of where they were less than a decade ago. In fact, in a recent discussion I had with fintech futurist and author Brett King, he shared how in 2017 we reached the milestone where NLP-based AI algorithms matched the performance of humans in understanding language. That’s huge—not just from a technology standpoint, but also in terms of what it means for user adoption. People are more likely to interact with someone—or something—who understands them and responds naturally, even empathetically.
That’s just what we’re seeing with conversational AI. People are no longer screaming or typing “representative!” the moment they connect with a virtual agent either via voice or chat; they’re engaging thoughtfully and thoroughly, and we’re seeing higher satisfaction and resolution rates as a result. What’s more, we’re now able to capture and analyze spoken and written customer input more accurately, so when a customer does need additional agent support, they don’t need to repeat the same information they told the bot. And that’s just scratching the surface of what conversational AI can do.
So, what are the compelling reasons driving companies to adopt conversational AI? Let’s break it down:
The Business Benefits of Conversational AI
While conversational AI can help businesses in many ways, the technology makes the biggest impact in three key areas:
Improving the Customer Experience
In addition to creating smoother, more natural interactions through better language processing, conversational AI “speaks” to customers’ primary desire—convenience—and eliminates friction in their interactions. If we consider the dominant customer service metrics—average call handle time (AHT), average hold time and others—we see that success is nearly always measured in terms of time and effort. The same holds true for customer satisfaction (CSAT) surveys: those that score higher often cite ease and speed to resolution, while those that fall short typically blame long, difficult processes. Conversational AI simplifies and accelerates complex, time-consuming processes by shifting the burden away from the customer and onto the technology. That translates to a better customer experience and, consequently, higher customer satisfaction.
Improving the Employee Experience
Yes, employee experience matters. And, in many ways, it matters now more than ever. Customer service has historically faced high attrition. Now, with more remote employment options available, agents are no longer anchored to a physical location. As a result, companies that offer a better employee experience have a considerable competitive advantage over their peers. Like customers, employees see value in terms of time and effort saved. Only, for employees, the burden is on the back end. Taking notes, scouring knowledge systems and completing after-call work—these manual tasks sap agents’ time, energy and patience. Conversational AI—together with robotic process automation (RPA)—can make quick work of these mentally draining (and often demoralizing) tasks as well as provide real-time agent guidance on customer intent and emotion. By making agents’ lives easier, companies can better retain quality employees.
Lowering Business Costs
Yes, conversational AI can help lower costs—and even add business value. Let’s start with operational efficiency. Remember the chatbots we mentioned earlier? With today’s conversational AI technology, they’re able to field more and more customer queries, reducing the number of calls that require human assistance. And those calls that do require live support no longer require the prolonged holds and excessive after-call work of yesterday thanks to RPA. Instead of wasting minutes (and even hours) executing low-value, manual tasks, agents can focus on high-value, customer-facing engagements. That brings us to another cost savings: agent onboarding and retention. By simplifying agents’ jobs, conversational AI can cut training and onboarding time (sometimes by weeks) and reduce the cost of constantly battling high turnover. Lastly, by mining calls for valuable customer data, conversational AI can identify potential upselling and new marketing opportunities.
Top Conversational AI Use Cases
Like its benefits, conversational AI has countless use cases. Many are business or industry specific (i.e., financial compliance auditing, collections outreach automation); however, the three most common use cases are, broadly:
We’ve already alluded to how conversational AI improves self-service adoption and usage by responding naturally to human input. As a result, many companies deploy it primarily as a means of deflecting low-value calls away from their human call center agents. While this is certainly one benefit of AI-enabled self-service, it misses the bigger value opportunity: driving customer engagement. With conversational AI, today’s chatbots can do so much more than merely check account balances and answer FAQs. They can visually guide customers to successfully resolve complex issues, automate appropriate follow-up actions and even identify potential upselling and cross-selling opportunities.
In-Call Agent Assistance
When most people think of conversational AI, chatbots are the first thing that come to mind. However, this thinking, again, only scratches the surface of what the technology is capable of. The same tech that can accurately capture and analyze customer input during self-service can actually assist human agents during live interactions. Today’s most advanced agent assistance software goes far beyond simply recording spoken interactions for quality assurance and coaching purposes. Using conversational AI, it can analyze speech for sentiment, intent and even emotion; scan knowledge management systems for relevant documents; auto-populate back-end forms; and even provide next-best action prompts—all in real-time.
After-Call Process Automation
Many, if not most, interactions involve some form of promise made by the agent to the customer. How well (or poorly) that promise is fulfilled directly impacts customer satisfaction and even loyalty. Thanks again to conversational AI and RPA, agents can now better capture what promises are made during calls and automate appropriate and timely follow-up or follow-through actions. This not only saves agents from having to remember and execute multiple promise management tasks, it also keeps customers engaged in the process, strengthening their perception of your company as a result.
Conversational AI in the Real World: A Case Study
In my role, I’ve had the pleasure of working with CX business leaders across all industries. As a result, I’ve seen conversational AI transform healthcare patient and payer experiences, strengthen banking regulatory compliance and improve online travel booking rates. Recently, I had the opportunity to witness how one of the world’s largest telecommunications companies optimized its agent operations with AI-enabled agent assistance. Here’s how:
Despite having an ample and skilled staff, the company struggled with long average handle times, which often required additional agents to handle swollen call queues. After carefully reviewing its contact center processes, the company identified after-call work—specifically its call summarization process—as an opportunity for improvement. It found that its agents spent 50 seconds on average to make post-call remarks and perform research tasks. And to make matters worse, the summaries were often incomplete or inaccurate.
The telecom leader realized that much of its after-call work—including call summarization—could be automated using conversational data gleaned during the call. By auto-populating post-call forms using AI, the company was able to free its agents to spend more time serving its customers—not on completing manual tasks.
After deploying conversational AI, the company was able to reduce after-call work by an average of 15 seconds per agent and reduce AHT by as much as 60 seconds. This faster speed of service not only met customer satisfaction goals; it also allowed the company to better prioritize its staffing needs rather than simply overstaffing whenever call traffic surges.
Additionally, with technology handling the notetaking and data entry tasks instead of error-prone humans, the company was able to achieve 80 percent accuracy on 90 percent of calls. Together, with the reduction in ACW (After Call Work) and AHT, the company was able to save a significant amount of time, effort and cost per call—all thanks to conversational AI.
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