A few weeks ago I wrote an article about customer service in a world of ambient computing. This article looked at customer service from a customer’s point of view. In it I described how I see customer service getting humanized again by leveraging the advances in AI technologies like Natural Language Processing, speech-to-text- and text-to-speech generation along with intent determination.
Leveraging these technologies customer service will turn into a conversation and it won’t matter anymore whether service is delivered by a bot or by a human.
For the customer it will all appear to be the same. Instead of FAQs or web searches, bots will be the first line of support and escalate a problem to humans if they cannot solve it on their own.
The obvious question is whether there will be an impact on the customer service center?
And it probably does.
Call centers, and with it the service agents as well as their managers, already now are under intense pressure to deliver, and to deliver more efficiently.
With the increasing use of call deflection technologies like FAQs and communities there is a trend for the incidents facing the agents becoming more challenging. For example Helpshift states that already with its technology it is able to deflect about 90% of all incidents, which are solved via the native in-app FAQ that is delivered by the them. This statement basically says that the support staff is basically relieved of dealing with simple matters but has the chance to take up the more challenging ones. Still, in a world of ambient computing any given app can have hundreds of millions of users.
Let’s say that any given day just one per cent of 100 million users have an issue. The well working FAQ deflects 99% of these. That leaves the service center with 100,000 calls.
In one day.
And they are the harder ones.
Still, let’s be optimistic and say that an agent can solve 10 issues an hour, giving him 80 in an 8 hour shift. This would mean an overall call center size of 1,250 agents is needed to cope with this demand.
Each of them under a tremendous stress level.
With the systems behind bots becoming more and more intelligent the difficulty of raised issues will increase, even if FAQs and web searches are essentially hidden behind a bot interface that essentially makes the human agent the second point of contact again as opposed to the third, which likely means that the customer’s level of annoyance is slightly less elevated than in a third level scenario.
At the same time it seems that call center agents are not prepared for handling this stress level. The employee turnover rate remains high and is probably even rising.
Call centers are therefore facing a double challenge
- Contain cost. This is achieved by more automation, which in turn puts more strain on the employees
- Employ and retain a highly skilled set of service agents, which additionally have matching character traits, which drives cost. Skilled people tend to be more expensive than unskilled ones, and moving a call center into a low-salary country helps only so much – if at all. Training comes at an expense as well. This will be somewhat augmented by reduced hiring cost
The solution to it will be multi-faceted and increase a trend that is already visible.
Implement intelligent systems that more than offset the higher salaries demanded – and deserved – by the fewer call center agents, through an increased solution rate and through them being of more help to the service agents. These systems will significantly rely on machine learning out of a variety of sources
Employ communities by incentivizing to other users to help other users. These communities will be managed by community managers, with increasing support by AI-driven bots.
Highly data driven prioritization and intelligent grouping and routing of incidents to the best matching agent, bot or human. This will involve sophisticated Natural Language Processing capabilities but will help in solving multiple calls regarding the same problem in one process
Improved collaboration, bot – bot, bot – human, human – bot, human – human, to further increase the service center’s efficiency. Bot to bot collaboration and bot to human collaboration are for smooth handovers, as for the foreseeable future bots will stay focused on narrowly defined scopes. Human to human collaboration is again a smooth handover to the right expert, but is also about educating the colleague by helping out with own specialized experience. Finally, human to bot collaboration is about the human training the system on the go.
Last, but not least, by hiring the right people. An early 2017 study by Harvard Business Review on Kick-Ass Customer Service revealed that call center managers are hiring the wrong people. In scenarios that increasingly deflect calls it needs more highly trained controllers and rocks with a mindset for collaboration, rather than empathizers. While empathy is important what matters most when dealing with a customer in an aggravated mood is a fast and efficient resolution. With this, the role of the manager will change, too, into the direction of being a servant to the team and taking care of roadblocks for the agents and fostering collaboration.
This collaboration mandatorily extends into the product department. The best issue is the one that doesn’t even occur. Data from the call center, and from the app itself, gives unique insight into possible problem patterns. And the best problem to have is the one that doesn’t even occur. DevOps gives an idea on how this can get achieved.
Ah yes, don’t script too much. Scripts are a good guidance for someone unknowledgeable, which the future call center agent is not.
The future of the call center lies in a high degree of automation, powered by highly skilled and motivated agents. That brings the human back to customers and agents alike.
