We hear a lot of hype about how AI technology, such as chatbots, is advancing to minimise the amount of contact a customer has with a contact centre, so does that mean that the human customer services agent will become obsolete?
Well, underneath the hype of AI, the reality is that chatbot development has plateaued, and the understanding matured.
Alongside this limitation is the fact that customer interactions themselves are becoming more and more complex as customers learn to resolve more simplistic issues by themselves. There are also more communication channels as customers might try to resolve issues by a website, then chat, then phone.
So, this presents enormous challenges for Autonomous Customer Experience (ACE) and possibly leads to the fact that ACE will, or indeed should never, become a reality.
Here we look at the key roles of the customer, the human agent and the technology within the CX process and how they should evolve to deliver the optimal autonomous customer experience, not in 10 years’ time but right now.
The customer’s role
Input from the customer should become less, through good design of a CX process. Ideally the customer doesn’t want to interact to solve a problem, unless that interaction is part of the service itself or delights them in exceeding expectations. As mentioned earlier, it is unlikely to ever go to zero. The customer wants to interact on whatever channel they choose. Some customer segments (younger, and growing) prefer not to speak to a human, but everyone wants to know that there is the option to do so. In the Dimension Data Contact Center Survey 2017, phone contacts have dropped 17% from 2015 to 2017 (and non-telephone channels are up from 20% to c. 40%) but digital transformation has also slowed and is far from the desired states of CX leaders.
The customer wants a seamless journey, but along the way a huge amount of diverse and unstructured data might be generated across several departments. This clearly underlines the challenges with the technology keeping pace which we address below.
The human agent’s role
This role will decrease as technologies are able to automate and resolve many of the simpler, routine queries, but it is also important to understand that this role will also change. We will see reduced costs as the volume and average time of interactions declines. What will remain is a much richer role requiring higher levels of personalisation to pick up the most complex customer interactions that will always exist in a world where the number and complexity of channels is changing all of the time. They will use the intelligence that AI can’t do, i.e. solving rare, subtle problems. This human intervention, however, will need to be well informed by good insight distilled from the vast data generated by that customer and the problem.
The current best practice is to deploy rules-based text analytics to make sense of customer interactions. Yet it is not reliable as it is based on keywords and needs to be highly curated in a ‘rear-view-mirror’ fashion and it doesn’t have any quantitative metrics to show what the accuracy is. It also can’t be used for predictive or prescriptive analytics. Whilst it has solved the simplest queries, it cannot organically grow to deal with the next level and so reaches a plateau.
Machine learning is an alternative, often applied to classify text based on the computer ‘learning’ the common patterns to predict and classify without necessarily guessing keywords or using templates. The elegance of this solution is unfortunately mired by the complexity of the task which requires data scientists to spend a great deal of time training, building and testing predictive models, let alone cleaning the data in the first place. Again, this has reached a plateau due to the resource constraints.
The good news is that there are new disruptive technologies which are appearing which can change the rules of the game. One such technology is called Optimized Learning (OL) which generates machine learning models but when it’s not sure about something it asks a human to help validate in a highly efficient way, thus speeding up the process and increasing accuracy. It is really mimicking and supercharging human judgement on an automated, industrial scale. OL overcomes the disconnects because it doesn’t require any data scientists: The training and interaction can be done by a non-data scientist guided by OL. It is really marrying the best of both the human intelligence and artificial intelligence worlds. This can help to make sense of the disparate data and break down the corporate silos to affect a seamless, structured data stream which translates to a smooth customer journey.
ACE is a journey not a destination. To move along the journey, we need to recognise the changes in roles of the players and use the right kind of technology to enable these roles to evolve as part of a robust transformation within an organisation. Outwardly the organisation is customer-centric, inside it is data-centric. Just like the ‘swan’s feet’ the customer should never be aware of the power and efficiency ‘under the water’. They should just be able to glide effortlessly through their journey.
By Dan Somers, CEO at Warwick Analytics
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