Artificial intelligence has come a long way from its early days of robotic, scripted messaging and rudimentary logic. There are so many AI customer service use cases that make interactions faster and more personal.
Today, AI’s greatest power in the practical world lies not in serving as a standalone solution, but in its ability to multiply the effectiveness of existing technologies (and to do the same for humans themselves).
Adding AI to existing systems can increase efficiency, reduce expenditures and boost output, both in terms of quality and quantity. When implemented correctly, it can have a dramatic effect on how your agents serve your customers and their resulting levels of satisfaction.
If you’re new to AI, it can be hard to see past the theoretical to the practical implications for smart technology. We’re big proponents of taking the mystery out of AI, so we’ve broken down five real-world use cases for AI in the contact center to inspire your customer service strategy and help you stay ahead of the machine learning curve.
Use case #1: Automated self-service with chatbots
Chatbots interpret a customer’s problem based on their inputs and leverage AI to resolve it, preferably without the need for human intervention. When a solution can’t be reached with the resources provided by the chatbot, the inquiry can be routed to a live agent for further resolution.
Now familiar to most customers, chatbots were one of the first AI tools to be widely implemented in a public-facing capacity. They’ve evolved considerably since their first iterations and are now a reliable tool to help customer service teams tackle their most frequent inquiries, especially ones that are tedious for human agents to manage. Additionally, chatbots enable companies to offer a 24/7 service channel when having round-the-clock human staffers available isn’t feasible.
According to industry estimates, chatbots are expected to save companies more than $8 billion by 2022, while lowering processing times and enabling more first-contact resolutions.
Use case #2: Speech analysis
Chatbots are a wonderful AI-powered tool, but they’re not without their shortcomings. One of them is that they lack the nuance of a human conversation, which can influence the outcome of a customer-to-brand interaction.
By layering sentiment analysis on top of chatbots, machines can ascertain not only the nature of a customer’s request but that customer’s emotional state. This can be used to inform the messages the chatbot uses to respond, leading to more effective virtual agent conversations.
This same concept can be applied to spoken words with the help of another AI tool, natural language processing. NLP analyzes a caller’s word choice, tone of voice, pauses, and other speech characteristics to understand their frame of mind and help agents deliver the best response. A caller who’s in a good mood, for example, may be much more amenable to an upsell for a new product, whereas an angry customer is looking for nothing more than a swift resolution.
The rise of IoT devices presents an exciting new opportunity for companies to deploy speech recognition technology, with hundreds of millions of such devices currently in use. Technology leaders like Amazon are already combining connected devices with language processing applications, biometrics sensors, and more to offer enhanced customer experiences and more personalized service. We anticipate this technology will become more accessible for a range of companies and industries shortly.
Use case #3: Intelligent call and channel routing
The modern call center is omnichannel, meaning support is available via several different platforms. An omnichannel structure helps you meet the customer where it’s most convenient for them, powering positive experiences.
One of the challenges of an omnichannel approach, though, is that it’s rare for volume to be distributed evenly across channels or agents. Artificial intelligence can help alleviate the imbalance.
Interactive voice response (IVR) systems gather information from callers and provide automated solutions. You may have used such a system to check your account balance or pay a bill. If a customer can’t self-resolve the issue via IVR, AI can route the call to the agent who’s best suited to respond to it.
AI can also be used to provide alternatives during peak call periods to distribute the load more evenly across channels, like offering a call back at a later time or advising customers that there’s no wait on the company’s live chat. Over email, AI scanners can “read” messages and sort them for agent follow-up, prioritizing them based on urgency, account status, and so on.
Use case #4: Proactive outreach
Customer service shouldn’t be purely reactive. You can take a proactive approach and scale it easily by using AI.
One example is sending emails or follow up texts based on a customer’s actions. Maybe a customer attempted to resolve their issue using your webchat feature but gave the experience a poor rating on your CSAT form. You might automatically follow up with an email from an agent providing additional options. Or, if a customer hasn’t interacted with you in a while, send them an email or SMS with your latest promotions.
Such personalization is highly effective in increasing open and read rates for your brand communications. Emails with personalized subject lines, for example, are 26% more likely to be opened, while 71% of consumers say a personalized experience influences their decision to open and read marketing emails.
Use case #5: Improving the agent experience
AI’s customer service capabilities aren’t limited to serving the customer directly. Pair AI with your CRM to power ultra-fast customer interactions that make agents’ lives easier. Call up relevant customer details at the time of the call and automatically place them in front of the agent so there’s no need for them to go clicking around looking for information or to make the customer repeat.
AI also facilitates personalization, helping guide agents through interactions based on a customer’s ticketing history. Their onsite actions like clicks, views, and purchases can be used to make specific predictions about the customer’s needs before they even have to ask for help, leading to highly effective service. These same characteristics can be used to determine the best upselling and cross-selling opportunities for a caller’s profile, which drives additional revenue.
AI-powered customer service isn’t some theoretical concept from the future–it’s here, and it’s a viable way for companies to deliver more effective solutions while lowering costs and delighting customers.