We have all been there. Punching numbers on our phones – bank account details say – then listening to a set menu that tells us to ‘Press 9 to speak to an agent’ and frantically hitting that number nine button.
More recently we have been talking to our phone in an attempt to reach our bank – we’ve stated our account number and sort code, and loudly shouted our first dog’s name to get through security.
So we’re used to automation, even if we don’t love it. Speech recognition goes back decades – the first workable systems arrived in the 1960s – but Artificial Intelligence (AI) has significantly upped the ante, offering a more conversational experience and personalised choices: ‘Your credit card balance is due – have you called to make a payment?’.
AI can deliver better experiences for customers. And that can translate into a more interesting and fulfilling role for contact centre agents, too. In a profession where levels of expertise are often too low, and job churn too high, that’s important.
The AI challenge
At its outset AI promised the world, relieving people from labour intensive hours of answering repetitive calls. Yet it hasn’t always delivered. In 2017, 76% of executives surveyed for a Deloitte report believed that AI could substantially transform their business within three years. This fell to 56% in 2018 as implementation proved trickier than expected.
And when AI-based systems don’t deliver the level of skill required it increases, rather than decreases, the pressure on contact centre staff. How do you feel when you finally get through to a real person? And how would you feel if you were on the other end of the call, listening to yet another frustrated customer let off steam?
Good contact centres are fundamental to delivering good customer experiences. Bad contact centres are notorious for high staff churn. People don’t tend to stay beyond six months, because the job is unrewarding. Because most contact centre workflows are complicated, employees can take that long just to become proficient. If you match inexperienced and unhappy contact centre staff with frustrated customers who can’t get their problems resolved, it’s a recipe for dissatisfaction all round.
So how do you get it right? Artificial intelligence is evolving rapidly, and it can deliver much more to organisations than a clunky call answering solution. It’s time to take a fresh look at AI, to see how you can make it work for all the people that matter to your business – customers and staff alike.
AI is still far away from the sci-fi we have all watched on TV, but it can do some pretty great stuff for the people on both sides of a customer call.
What can AI actually do?
For the customer looking for answers, AI can get them to the right place quickly and simply. For the person answering a call, it can give them access to all the relevant customer information, so they’re in a position to genuinely help.
“AI is still far away from the sci-fi we have all watched on TV, but it can do some pretty great stuff for the people on both sides of a customer call.”
From our experiences with Alexa and Siri we know that AI can understand some sophisticated natural language queries, and respond in a conversational fashion. The flip side of that is we’ve all had amusing, totally nonsensical dialogues with our favourite digital assistant, and we quickly learn not to go there (maybe the AI is training its humans as fast as the humans are training the AI?).
In an often emotional and always time-pressured customer service engagement, those cul de sacs have to be eliminated. How? We’re straying into very expert territory now, but here’s a simple and structured way to begin to think about that challenge.
What problems can an AI solve faster, or more accurately, than a human? And where can an AI boost performance when human-to-human experiences are essential? There is a balance of power here that’s shifting all the while.
When in the workflow does the AI do its thing? And when does it hand back to a human?
This is a bucket into which goes methods, technology, algorithms and training. Yep – big bucket.
A working example
Here’s an example from real life. The fully automated transaction we’re about to describe really does work with this level of skill. On second read, you’ll spot all the places where it could go wrong. We’ll talk a bit more about that later.
Picture the scene. You’ve missed your connecting flight. It’s late in the evening. You’re tired, stressed and stuck at the airport, not knowing what to do. You call your airline’s customer service, and the call is answered straight away. It’s a warm, friendly voice, with not a hint of stress or tiredness.
Not only is it picked up straight away, but you’re greeted with your own name. And the ‘agent’ is pretty sure it knows why you’re calling. So you’ve missed your flight? The next flight is at 9.30 tomorrow morning, would you like to rebook? Not convenient? How about midday? Okay, I’ve booked that for you.
The AI has recognised you from your voice ‘fingerprint’, and confirmed it from context and meta data such as your phone’s location and calling line identification (CLI). It knows you’ve missed the flight because you weren’t on the connecting one. It has pieced this together from booking records, flight manifests, and because you’re calling for help when you ought to be on an aircraft. That’s the only reason for this particular call, at this particular time. And the best solution is to get you booked onto the next flight, so you can get to your destination as quickly as possible. When the context of the call is as specific as this, the AI doesn’t need a person to figure out the best course of action.
Right place is definitely served by AI voice ID, because authenticating who you are is often challenging, especially when you’re stressed. Replacing the usual triage questions with a skilled prediction – have you missed your flight – falls into the same category. Since creating enriched experiences for your customers and your agents is your goal, then right time is well served too. We’ll get to the right AI piece in a bit.
Within a workflow, and within individual transactions, there has to be a capacity to shift the equilibrium between AI automation, and human interaction. A type of AI that makes its difference here is real-time speech analytics (RTSA). It monitors stress levels, speech clarity and workflow adherence, all while the call is in progress. So if the passenger in the story above was getting impatient with the AI or her voice was emitting stress levels above the threshold for AI-handled response, the AI would ask the caller whether they would like to be transferred to a human agent, who can provide some empathy, ensuring a fruitful human-to-human interaction.
At that point, having already captured a lot of information, the AI can deliver it to the human agent ahead of transferring the call so the customer does not have to answer the same questions all over again. It can then use the context it’s gathered to pull up guides and information that would help the agent to solve the customer’s problem. Imagine a concise, directional, and informative study guide placed at the agent’s fingertips. The interaction between the customer and agent is made more expert, more fluid, and allows more room for empathy. There is a much greater chance of a resolution without further escalation. Win-win.
Understand that AI will not fix all of your customer service issues - so figure out your right place, right time, and right AI . Then it will make a critical difference both to customer outcomes and staff retention.
Too long a topic to explore here, but a couple of points worth highlighting from the long list of things we could touch on. The first is method. It is nearly impossible to build the perfect AI-driven contact centre from a blueprint. The method of choice is fast prototyping and ongoing iteration, workflow by workflow. That needs a toolbox of AI building blocks, shaped for purpose. We’ll be writing more on this.
Second thing, it’s helpful to disclose whose AI toolkit delivered that working example above: Amazon Connect including Voice ID for voice recognition and authentication, Contact Flow Engine where the customer experience is both defined; and refined; and Amazon Wisdom harnessing RTSA to deliver agent guidance and deep knowledge.
In a nutshell
Contact centres are the hub of your customer experience and your ongoing challenge is to get them right. When they misfire, you’ll not only frustrate your customers but you won’t hang on to your staff either. AI enablement can greatly increase satisfaction within and with your organisation. The road to successful AI integration is about good process, fast prototyping and constant iteration.
Understand that AI will not fix all of your customer service issues – so figure out your right place, right time, and right AI . Then it will make a critical difference both to customer outcomes and staff retention.
If you would like to know more about Contact Centre capabilities and what the future of the technology looks like, please see the links below::