How to Support a Reduced Staff With AI for Call Centers
Consider for a moment all the reasons a call center's traffic may outpace capacity. Employee turnover, illness, or even unexpected service downtime inducing a call-in spike can leave a center struggling to maintain its normal productivity.
In the call center, both unplanned staffing gaps and call traffic upticks can severely disrupt or nearly halt operations regardless of industry — in large part because employees in these departments tend to work identical customer-facing roles.
The good news is that call centers dealing with an unplanned staffing gap or looking to safeguard against one have found an increasingly capable friend in artificial intelligence (AI). AI for call centers can help relieve operational pressure via call triaging, greater decision-making capacity, and a more flexible set of tools spanning from the customer's end of the phone to the agent's.
Keeping Customers Satisfied
In any consumer-facing industry, relatively minor disruptions to one arm of a company or a core product can put even a well-staffed call center in high water. A reduced team will likely end up in over their heads quickly in such situations. Circumstances depending, customers may find themselves:
· Unable to contact the people who provide key services.
· Dealing with lengthy delays, disconnects, poor service, and other standard trappings of high call volume for relatively minor issues.
· Left, overall, with a diminished sense of the company and its agents.
Then there's the matter of expectations. It's fair to say modern living has given customers the freedom to reinvest their loyalty over concerns that would've barely registered a decade ago. If you've ever gritted your teeth at a miserably inefficient phone queue following a power outage or similar problem, you know just how frustrating a lack of responsiveness can be. Your organization can avoid similar reputational damage with a little planning.
What Can AI for Call Centers Do?
AI's ability to reduce workloads and increase efficiency means companies can turn long waits and overworked employees into nonfactors. Customers who call anticipating a painful resolution and hang up having experienced shorter hold times or smarter self-service options, for instance, may come away with an improved opinion of the company. Here are a few ways AI can support those efforts.
Customer service representatives almost certainly feel less rushed and thus less panicked when call volumes and duration can be diminished during a staffing shortage. Customers and agents alike can benefit from time-saving tools that analyze the sentiment and content of the call and use the data to automatically populate the rep's screen.
For example, if a caller is connected to an agent to discuss a common software issue, AI can quickly retrieve relevant troubleshooting instructions for the agent to walk the customer through without having to search the system.
Answer Simple Questions
Consider the sheer number of calls on a single topic a center may take following a disruptive event. A sizable portion of your customer base may call in with the same simple question — when can they expect service to be restored?
Used effectively, AI can greatly improve the standard prerecorded message a company might otherwise place at the call start — greeting callers, answering initial questions, and forwarding to live agents only if it encounters a query it can't handle on its own. Simply diverting a percentage of customers before they're funneled to live help can have drastic effects on wait times, experience perception, and other key performance indicators, upping a call's chances of going well from the word "go."
Communicate Naturally and Helpfully
Natural language processing (NLP) is key to the growth of AI for call centers. This feature helps systems listen and respond in language natural enough to keep callers of a given need complexity satisfied. This equates to a constant prioritizing presence in times of extreme need, essentially granting an omniagent capable of handling high call volume through hang-up or handoff.
This isn't to say NLP's strengths end with answering simple questions. Take appointment scheduling in fields such as healthcare or finance, for example. This practice is both critically important to ongoing success during a crisis and highly taxing in terms of the potential impact on call centers.
Just as most callers can set a calendar entry via the voice assistant on their mobile phones, similar AI technology can help them find a mutually acceptable time, request follow-up contact information, and — again — forward callers with more complex needs to live help once automated options have been exhausted.
More Than a Stopgap
It's also worth noting that the same skills that make AI for call centers so useful during a spike in call center traffic have every bit as much utility during normal operations. The ability to route calls intelligently, increase self-service options, and reduce call volume and dead time for customers are the same things most every call center — from the public sector to the private — looks for in some fashion any time it implements a new communications technology.
In this way, AI is more than an emergency support for call centers. Rather, it can immediately impact regular operations and those strained by staffing or call volume. That's a far cry from the prerecorded, hard coded interactive voice responses (IVRs) of old. But like those IVRs, AI in the call center is likely to cross the threshold from competitive edge to operational necessity soon. You're better off implementing now than getting caught behind the curve.