Improve your customer service metrics with AI
At this point, nobody needs to be convinced that good customer services one of the most important aspects of any brand. We’ve seen countless examples of empires being toppled by news of an unpleasant customer service experience. Sometimes, we even hear news of an exceptional experience a customer had – and the immense effect this can have on a brand’s image (and, more directly, sales).
As such, ensuring that your customer service team is firing on all cylinders is a top priority, whether you’re a small brand or an international juggernaut. Properly analysing your customer service metrics will help you understand how best to serve your customers.
Not only that – a deep understanding of what your analytical data is telling you will help you see what your strong and weak points are. Some improvements are made much more easily than others, however. Improving some metrics requires serious amounts of effort and resources – or it would, were it not for our secret weapon.
Customer service automation – the silver bullet
We’ve talked a lot about customer service automation – it’s one of our primary areas of expertise, after all. Some of the improvements touted by automation advocates (such as ourselves) may admittedly sound too good to be true. We hasten to assure you, however, that this is no snake oil that we’re promoting here.
Many tools used in modern automation – from automated routing systems to AI chatbots are designed with just one goal in mind: saving time. The simple fact is, the way “traditional” customer service is conducted is rife with compounding inefficiencies. Although they might seem trivial at first, they quickly add up, resulting in a sub par customer experience.
The principle at work here is simple: remove any obstacle preventing your team from doing real work that matters. The advantage of this approach is that its benefits are easily measurable. The following metrics all benefit from automation in different ways – and all are worth a closer look.
Average time to first response
The average time to first response is a metric expressing how long an average customer has to wait between initiating contact with the brand and receiving a response. You might think to yourself, “well, that’s easy enough to improve. Deploy an autoresponder!”
An automated response may feel like it’s accomplishing something – but it only puts an additional step in front of the actual issue. Autoresponders don’t do anything to shorten the actual wait time. What they do, however, is obscure the fact that the customer is waiting.
Instead of wasting time on band-aid solutions, let’s dig deeper and find the root of the problem. What prevents our agents from responding to inquiries in a timely manner?
As is often the case, the answer is a combination of several factors. The two main culprits are constant context switching and inefficient routing. Customer service automation offers solutions for both problems.
Better routing – through artificial intelligence
One of the biggest pain points that ruins customer satisfaction is the constant juggling between different support departments. If, on average, your customers’ calls are routed through more than one agent before being resolved, you have a problem.
You can alleviate it by using a smart routing algorithm – ensuring that every message reaches a relevant agent. That’s not all – thanks to machine learning, the algorithm can judge the workload of every agent on the team. This, in turn, allows it to arrange incoming tickets in such a way that customers never have to wait long to get in touch with an agent.
Such is the power of good AI!
Context switching, or how to tire out a brain
Modern customer service requires us to be available at all times, on every platform our customers use. Normally, this would require our agents to constantly switch between platforms in order to quickly respond to every message.
This constant act of hopping from one platform to another is called context switching – and it is the easiest way to tire out a human brain.
The solution is simple – avoid it! A customer service automation approach called omnichannel allows you to have all of your social media communication in one place – saving your agents a lot of unnecessary mental fatigue.
Ticket volume and resolution rate
The resolution rate expressed the percentage of cases that end with the users’ issue being solved within all incoming enquiries – your ticket volume. Obviously, you’d like this to be as close to 100% as possible. However, many factors may make achieving this goal rather difficult.
One of the biggest hurdles is simply enquiry volume – if your agents are swamped with too many requests, they obviously won’t be able to handle all of them. Not all tickets are created equal, which only compounds the problem. The truly difficult problems require extra love and care. How are you supposed to provide that if you have to handle three dozen instances of the same task a day?
As it turns out, these constantly repeating requests and tasks are a prime candidate for automation. By deploying a chatbot solution, the customer can solve their own issue! Why waste your agents’ time on doing the same thing over and over again every single day? Free them up to work on the harder issues!
By using automation to take care of the most commonly repeated requests, the workload of your staff will be severely reduced – allowing them to service every request they get, and, eventually, reach that coveted 100% resolution rate.
“Hang on a moment”, we hear you say. “How can automation improve something as abstract as customer satisfaction?” We’ll admit – improving customer satisfaction through automation and AI isn’t immediately obvious, but it is indeed possible.
In fact, improved customer satisfaction is simply a natural result of implementing proper automation techniques and offloading most of the heavy lifting onto AI-powered solutions.
The simple fact is that consumers really are interested in a few simple things: they want their issues solved quickly and effectively. Everything else is just a bonus.
So where does AI come into play? Almost everywhere! Where there’s automation, there’s room for machine learning and AI to take charge.
Take routing, for instance. Don’t you just hate it when you try and contact a brand with your issue, only to be passed around like a hot potato between different customer service departments?
AI-powered routing algorithms can mitigate this issue. By analysing the contents of your first message, natural language understanding (NLU) algorithms can deduce its context and intent. From there, directing you to the appropriate team is just a matter of formality.
What’s more, algorithms like that learn and self-improve over time. Every time a customer contacts your brand, its training dataset grows, and the algorithm learns.
What’s more, an AI-powered routing algorithm can also choose the best customer service agent to answer any given query based on past experiences. This further increases the efficiency and cohesion of your customer service team
This, combined with a chatbot tasked with solving the most commonly encountered issues, ensures that the customer is in and out of the customer service pipeline as quickly and efficiently as possible.
Machine learning and AI-powered software solutions certainly have made themselves at home in the world of customer service automation. That’s a good thing! Despite what some naysayers claim, the machines won’t rise up and kill us all; instead, they’re making our lives much easier.
If we piqued your interest in customer service automation, we’d like to invite you to give it a try – with our fully featured, omnichannel automation package. SentiOne features all of the features we’ve written about in this article – and more! Get in touch with us and schedule your free trial today.
If you’re interested in learning even more about automation, read our Customer Service Automation Guide for a comprehensive introduction to the topic.