Call centers and voice AI
Marco van Hurne
Partnering with the most innovative AI and RPA platforms to optimize back office processes, automate manual tasks, improve customer service, save money, and grow profits.
If you’ve ever picked up the phone to call customer service (and we know you have), chances are you’ve encountered an interactive voice response - IVR- system. That is the most basic and quite frankly, outdated, form of call center automation. It’s just as likely that you’ve had a frustrating experience with one, too.
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The high cost of voice
Here’s how smart automation can help brands lower costs, increase self-service resolution, and improve CX.
Despite migration to digital channels in recent years, voice is still the most popular support channel. But with rising call volumes, many companies are looking to technology to help them relieve burden.
Let’s look at the difference between traditional IVR, a AI call agent, and call center automation so you can make the right investment for your business.
256Billion number of calls served globally each year; 5-8%: average abandonment rate for contact centers. 6 minutes approximate waiting time for customers
What is call center automation?
Call center automation uses technology to carry out repetitive, labor-intensive processes and tasks. It lowers the requirement for human intervention by a lot.
IVR was the first approach to call center automation
The landline was the original customer service channel. In fact, not so long ago it was the only way consumers could contact companies outside of a brick-and-mortar store. IVRs were developed to help companies increase call center efficiency by collecting information from callers and routing them to the right agent or department.
IVR is an automated system that uses a phone-tree framework to gather information about why a customer is calling and potentially let them take action, such as paying a bill, or be routed to an agent. You know how these calls go: “For A, say ‘A’ or press 1. For B, say ‘B’ or press 2…” and so on. To get where they need to go, callers have to work their way through the menu by providing pre-set responses.
An IVR system is usually paired with other call center software like automatic call distribution, which routes incoming calls to the best department or agent, depending on a caller’s need. Routing to the right agent the first time is critical for brands, because customers really don’t like getting bounced around.
"68% of customers are annoyed when they are transferred between departments"
The downside of IVR
The biggest issue with old-school IVRs is that they’re not designed to truly resolve customer issues; they’re better suited to gather information about intent and route calls to the right place. And while that matters, it’s not enough for today’s independent customers who expect to be able to self-serve for most issues. Rather than helping them get what they need, callers often see the IVR as standing in their way.
Legacy IVRs don’t use NLU technology to discern what callers are saying. They use voice recognition, but it’s limited on its own and the caller’s response has to match the menu tree-based system, which can follow a long, maze-like path, adding to frustration.
If a caller’s need isn’t addressed in the menu or the system doesn’t understand them, they can get stuck waiting on hold or be bounced around between departments. Callers often resort to dialing zero to try and bypass the system and reach an agent. This phenomenon, known as “zeroing out,” shows that customers see the IVR as being difficult to use and time consuming, without actually helping them reach resolution.
>1/3 longer wait times to speak to an agent than before the pandemic. 30-60: average number of seconds a customer will wait on hold before abandoment
In addition to being complicated for customers to navigate, traditional IVR systems can be complex on the back end. Many are clunky and difficult to set up, typically requiring a heavy lift on the part of IT teams. Poor integration capabilities often prevent customers from doing anything beyond providing information. And when they do finally reach an agent, the customer often has to start over and explain why they’re calling. With today’s higher customer expectations, and shorter patience, this leads to frustration that can impact a brand’s reputation, and the bottom line.
AI call agents are the next step in call center automation
Unlike legacy systems, AI is driven by ML and NLU. By combining NLU with automatic speech recognition, these AI call agents are able to recognize callers and understand what they’re saying, giving them the freedom to use everyday language. Making conversations feel more natural and human-like immediately improves the customer experience. Better understanding also leads to higher resolution and containment rates, boosting CSAT and efficiency.
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Unlike legacy systems, this next generation of AI call agents can be easier to integrate with core business systems such as a CRM or order management system, as well as other customer support tools, making it possible to personalize the experience and automate actions over the phone. This is a giant leap forward, as today’s customers expect to be able to get things done by themselves — not just get through the menu.
Another key advantage is that AI and ML have the ability to continually learn and optimize in ways that traditional IVR tools simply can’t. This not only improves recognition and resolution rates, it identifies improvements and other automation opportunities, compounding business value over time.
How to apply conversational AI across the call center
Call center automation can be used to enhance efficiency on the front end as well as behind the scenes, and can be deployed in a number of ways.
On customer-facing channels, including messaging and voice, AI call agents can automate self-service interactions. With a robust automation platform, integration with third-party tools allows for automation of additional processes, making it easier for customers to resolve inquiries themselves. Automation can also help to reduce long wait times by facilitating call backs so customers don’t have to wait on hold.
Automation is a valuable tool for agents, too, improving performance and resolution.
When integrated with core systems, conversational AI can help agents serve customers better by providing information about preferences, past purchases, and other relevant data.
Conversational AI can even collect customer information up front and pass it along to the agent when a conversation is escalated, reducing average handle time (AHT) and making the agent’s job easier. A smooth, contextual handoff also reduces customer effort by not making them repeat themselves.
With the right vendor, conversational AI can be deployed across channels, including chat, voice, SMS, and social media. This allows brands to increase Automated Resolution and decrease AHT. Plus, companies can create seamless experiences across channels.
The benefits of a conversational AI call agent
Happier customers
We all know that customers today expect a lot. They want to get what they need as fast as possible with the least amount of effort. Conversational AI makes it possible for more customers to resolve their issues quickly and easily, without waiting to speak with an agent.
Happier agents
Working in a call center isn’t always easy — or rewarding. High volumes and repetitive tasks lead to frequent burnout and high turnover — 42% in 2021 , according to one study. Another study found that 74% of contact center agents are at risk of burnout .?
Finding and hiring a new agent costs up to double the cost of an existing employee's salary.
Not only is this bad for morale, it’s bad for business. Frustrated agents are less likely to provide top-quality service. Plus, the cost of hiring and training new agents can be up to double the cost of retaining an existing agent. So making life easier for agents isn’t just the right thing to do, it’s also a smart business strategy.
Increasing self-service lightens the load on agents by decreasing their call volumes. And automating the most common tasks and inquiries relieves them of the repetitive work that quickly becomes mundane, freeing them up to focus on more valuable and rewarding work instead.
Lower costs
Call center automation helps brands manage rising call volumes as well as the spikes that occur around special events like Black Friday/Cyber Monday (BFCM), without needing to constantly increase headcount. This makes a big difference to the bottom line because agents are the most costly resource. At the same time, automation boosts agent efficiency by handling repetitive tickets, authenticating customers and passing context, and decreasing AHT.
According to Gartner , automation also lowers cost-per-interaction: “Gartner’s 2019 Customer Service and Support Leader poll identified that live channels such as phone, live chat, and email cost an average of $8.01 per contact, while self-service channels such as company-run websites and mobile apps cost about $0.10 per contact.”
This is why many companies no longer show a phone number on their website, trying to drive customers to other lower-cost channels instead. But with voice still being the most preferred channel for customer support , that’s a short-sighted approach. By adding automation to the support lineup, companies can cater to customer preferences without driving up costs.
The top 5 customer-preferred channels: Phone (44%) Chat (17%) Email (15%) Company website (12%) Search engine (4%)
Greater availability
In today’s always-on world, customers expect to be able to contact a company wherever and whenever they prefer. Relying solely on live agents limits the places and channels that brands can be in, as well as the time they’re available for customers.
With call center automation, brands can meet their customers where they are and offer 24/7 support across all the channels they’re already using. This is especially critical for businesses that need to handle issues outside of regular business hours, such as travel companies, as well as those that sell across time zones and around the world.
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4 个月Very informative Marco! I have read your article and can agree with it. Thank you very much for sharing!