Using AI to Reduce Agent Effort and Increase Service Personalization in Your Contact Center
Agent efficiency and service personalization go hand-in-hand. After all, an organization can’t successfully connect with customers if it doesn’t assist those responsible for making the connections. Research suggests the current state of the agent experience is…well, dim.
According to Deloitte, only 9% of large contact centers effectively use desktop analytics and 61% struggle with integrating systems. According to Regalix, 67% of agents use 3-5 different applications during one customer interaction (something that can cost a typical contact center up to $1.57 million a year in lost productivity, according to Gartner). The numbers are stacked, but there is a silver lining: contact center AI.
New research from Vanson Bourne shows that the overwhelming majority (99%) of organizations are using some form of AI in the contact center with key objectives related to the agent experience. For example, enabling agents to more quickly access relevant customer data from multiple sources or more efficiently handling complex, multi-faceted calls. In fact, the No. 1 driver of contact center AI—more than improved FCR and revenue per contact—is greater agent productivity, cited by 53% of organizations.
Here are five ways AI can be used in the contact center to reduce agent effort and, subsequently, increase personalization, customer loyalty and revenue:
- Conversational Intelligence
It can be difficult to serve, let alone meaningfully engage, with customers when you don’t entirely know what they’re saying. A company could apply an AI technology like natural language processing to its IVR portal to create conversational speech applications that help sharpen agents’ understanding of what customers are saying for faster yet more heartfelt engagement.
For example, a customer could call into the contact center and say, “I have two questions about billing.” A conversational speech application would automatically detect the words “billing” and “two” (as opposed to “to” or “too,” which makes all the difference) and get the customer to the right agent who now also has a better understanding of the situation. The result: better comprehension among agents, less time repeating information for customers, reduced operational costs in the form of faster service, and an overall improved customer experience.
- Sentiment Analysis
Real-time transcription of conversations can help agents not only better understand words that are being spoken but emotions that are being felt to drive more meaningful engagement. This might sound hippy dippy, but it’s one of the quickest and most efficient ways to understand customers’ needs and expectations.
Think about it: 84% of customers say being treated like a person, not a number, is critical to winning their business. How else are agents to understand at a deep and truly personal level how customers are feeling? Companies can even tailor engagement based on sentiment analysis from past interactions (ex: “I see last time you called you were really happy with the service upgrade you made, how’s everything going now?”). Monitoring sentiment during interactions in real-time is just one more way to help agents work more efficiently and improve overall customer outcomes.
- Smart Routing
Smart routing is critical for delivering a superior experience, pairing customers with the best-suited contact for better personalization and reducing the amount of effort agents typically exert during an interaction.
After analyzing customer data over a certain amount of time, companies can build intelligent machine learning algorithms that automatically pair customers with agents based on next-level drivers of satisfaction like emotion, relatability or sentiment. Agent effort is reduced by minimizing or even eliminating unnecessary transfers (or at the very least making handoffs as seamless as possible) while delivering truly personalized, contextual engagement based on the individual consumer.
- AI-enhanced Desktop
Organizations can elevate the traditional desktop using machine learning to help agents better handle concerns, complaints and inquiries in real-time. Upon detecting certain phrases or words spoken, the same kind of learning algorithms described above can trigger pop-up applications or extended desktop capabilities that present additional, relevant information. This way, agents don’t have to waste time searching outside their main screen or through multiple systems of data to find an answer or make a change.
For example, if a customer says something to the effect of “I’m frustrated that my delivery is past due,” the words “frustrated” “delivery” and “past due” could trigger a special discount for the agent to offer to retain the customer. The result: less time for agents to deliver more targeted personalization (while enjoying more autonomy in offering unique or custom resolutions to customers) and higher customer satisfaction.
- Virtual Assistants
Just like those used at home, a virtual assistant can help agents reduce handle time, increase accuracy, maintain compliance and stay more engaged in their roles to deliver more personalized, meaningful experiences. For example, this kind of assistant can present real-time conversation transcriptions on the desktop to help agents maximize efficiency and engagement. Or, siphon off tedious or repetitive interactions so that when an agent does handle an engagement it’s of a more complex and interesting nature. The solution can also help with time-consuming after-call work to improve efficiency and lower operational costs.
By reducing agent effort and increasing personalization, contact center AI has been proven to improve average number of transfers per call, minimize time spent by supervisors assisting agents, meet a greater number of quality SLAs and, of course, create more meaningful human connections. To learn more about effectively adopting AI in the contact center, check out Vanson Bourne’s new research report, “AI: The De Facto for Contact Center Experience,” commissioned by Avaya.