Thanks to Big Data, AI and Analytics Contact Centres Are Changing
Contact centres, by their nature, are all about people – people calling in with a query or order, and people in the centre dealing with the calls. The contact centre many not, therefore, be the obvious candidate when it comes to automation, but that’s starting to change.
New technologies like machine learning and access to new analytic techniques to gain insights from big data are all being used to improve the contact centre for customers and to extract useful data for the business.
Mind Your Language
One of the big advances that is set to make a huge difference to contact centre effectiveness is natural language processing (NLP). This allows the call to be automatically directed to the right department based on what the caller says. While it used to only recognise certain clear words like ‘sales’ or ‘accounts’, it’s now becoming possible to understand sentence speech.
The next step is to go beyond understanding the words and look at the sentiment. In other words, looking at how a caller is speaking can allow the system to detect if the caller is angry or if they’re old or young, enabling more accurate direction.
Analytics and Prediction
Artificial intelligence can be applied to many areas of contact centre operation. It can help detect when a caller may be trying to commit fraud, for example, or to spot patterns such as a series of complaints about similar issues or from the same area.
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With so many staff and so much to concentrate on with the analytics and data an office environment can get messy and unorganised. This will slow the productivity down when your trying to speed it up. A solution could be hiring a Cheltenham Commercial Cleaning company to come and sort it for you from once a week to how ever many times is needed. Why not have a look and ask some questions from website options including http://cleaningcompanycheltenham.co.uk. As contact centres move to the cloud and make use of IP-based systems from an international provider it becomes possible to integrate them much more with other systems. This opens the way to huge amounts of data surrounding customers and their history.
In fact, so much data is available that it’s hard for humans to handle, so once again the use of analytics and machine learning comes into play. These technologies can be used to extract and highlight pertinent details and ensure that operators have them at their fingertips when speaking to a customer. This helps deliver a higher level of customer service, ensuring that queries are answered quickly and efficiently. That means less time spent dealing with calls, greater agent efficiency and job satisfaction and ultimately an improved bottom line.