Can AI eradicate customer error from digital CX
In the early days of online search, three words characterised all that was wrong with the customer experience - ‘no results found’.
Nothing was more frustrating for users than knowing the information you wanted was at your fingertips, but not having the magic words to access it. The best part was Google made us think this was all par for the course. It wasn’t so much a pain point but an accepted part of the customer experience online.
As users, we didn’t wonder why search engines hadn’t understood our request, we simply told ourselves we’d not been precise enough to get the results we were after.
Over 20 years, we’ve become used to thinking like machines when we interact with technology. What a brilliant bit of cognitive conditioning by the tech giants!
This ‘think-like-a-machine’ mindset has underpinned the customer experience since the dawn of computers and coding, but proliferated with the explosion of the World Wide Web at the turn of the century.
Surely in 2020, getting the answers we want from technology should be as easy as asking a question of another human being?
Thanks to artificial intelligence, we now have the ability to recognise and process natural language in online search. Something which has fundamentally changed our relationship with technology.
From self-serve portals to chatbots and virtual assistants, automated customer service is now a staple of the way we engage with brands and content online. But there remains an expectation-reality gap in this process.
If the questions we ask are difficult to understand, then we’re asked to clarify with a return question, often sparking an entirely ancillary conversation to pin-point exactly what it is the customer wants.
This problem with current automated customer service tools stems from the fact that many providers use off-the-shelf chatbots or generalist natural language processing (NLP) engines from providers like Amazon and Google.
The result is effectively glorified FAQ systems that lack the depth of understanding to handle complex, specific customer queries in relation to business-critical services. But we can narrow the fields in which NLP platforms, and the machine learning capabilities associated with them, are deployed.
By focusing on specific areas of knowledge or industries, it’s now possible to harness the power of proprietary NLP platforms that better fulfil user requests using conversational AI. And, since NLP is context-driven, customers can get the answers they’re looking for, even if their question isn’t spelled or worded correctly.
Fundamentally, it’s this capability to emulate truly human-like conversation that will take the customer experience to the next level. But there’s another opportunity here for brands to think carefully not just about how their customers communicate, but the channels they use, an area in which AI is opening up new possibilities.
With rising app fatigue in an attention economy, forward-thinking businesses are looking to some of the most widely used apps on the planet, such as WhatsApp and Facebook Messenger, as the next frontier for customer engagement.
Combining NLP with the most popular messaging apps and companies’ existing IT infrastructure - including order management and customer communication systems - has the ability to transform the entire customer experience.
Lightning quick time to respond. Fully personalised and contextualised replies to queries. Intelligent suggestions that reflect what the customer actually wants - not what you assume they want. It’s an exciting new world, and we have the tools to make it a reality right now.
With machine learning capabilities helping brands harness technology that truly understands human behaviour, our imperfect nature and our natural ways of communicating, the era of ‘customer error’ is almost over.