by Julia Cook
Times are rapidly changing. When we get in touch with an increasing number of organizations, we are more and more likely to find ourselves communicating with a bot, rather than a real live person. Recent tech developments have meant that A.I. technology is busy powering many of the best practice customer journeys and here we are examining how some of this new technology is being used.
If planned carefully and deployed correctly, A.I. technology can support all aspects of the customer journey. It can enable businesses to understand their customers, simplify processes, eliminate bad demand and free agents to respond directly to customers.
Analyst firm Gartner has already predicted that by 2020 a remarkable 85% of customer touchpoints will be offered without human assistance.1 Which is why a growing number of organizations are keen to find out how they can leverage A.I. and machine learning within their own operations.
So what A.I. tech is out there and how are organizations starting to use it?
Predictive Intelligence
This tech enables organizations to use machine intelligence to improve the customer experience. For example, it allows agents in contact centers to immediately see a caller’s activity on the company website, before and during a phone call.
Virtual Assistant
Analyst firm Gartner has another prediction – that the number of customer interactions handled by a virtual assistant, instead of a real person, is set to grow tenfold over the next three years.2
This will lead to a huge leap in demand for effective conversational virtual assistants. These can help by optimising the customer experience, tailoring their contributions according to whereabouts customers are on their journey, also bearing in mind their individual preferences.
Conversational Commerce
With continued improvements in natural language understanding, voice control is on its way to becoming ubiquitous, especially since research suggests customers prefer automated interactions where they can speak directly to an A.I. enabled assistant (or chat bot).
However for successful conversations, companies need access to the right IVR, natural language, UX and customer journey design skills.
Human-Assisted Service
A.I.-enabled customer service needs to work both ways: recognising when a human agent is needed to help the customer, and also when an agent might benefit from some additional support. Understanding the times which both of these are necessary, and successfully managing the interface between human and A.I. service, is going to be very important.
Speech Analytics
The latest speech analytics solutions take advantage of real-time analysis and machine learning to deliver contextual guidance. This has the potential to alter the outcome of interactions while a caller is still on the line.
Cognitive A.I.
By applying Big Data, captured in millions of customer conversations, organizations can use machine learning techniques to look beyond their common engagement scenarios. By more effectively analysing and understanding their customers’ motivations and behaviors, organizations are able to use A.I. to understand and respond to more complicated contact reasons which in the past have been too diverse and complex to automate.
Voice Biometrics
Biometrics technology has been using neural networks, i.e. technology modelled on the human brain, for a long time. This is starting to provide organizations with a more natural, effortless way of authenticating customers securely by allowing them to use their voice as their password. New facial recognition technology is also now being tested alongside voice biometrics to broaden the scope of A.I.-enabled authentication solutions.
1 – https://www.gartner.com/imagesrv/summits/docs/na/customer-360/C360_2011_brochure_FINAL.pdf
2 – https://arc.applause.com/2016/12/22/virtual-assistants-smartphones-gartner/
