How_machine_learning_is_helping_Call_Centres_improve_customer_experience

How Machine Learning is helping Call Centres improve Customer Experience

http://insights.principa.co.za/how-machine-learning-is-helping-call-centres-improve-customer-experience

The call centre world, unsurprisingly, ranks as one of the highest adopters of data analytics platforms year on year. This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre.  With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds.

Whether you’re running an inbound or outbound contact centre, the interactions between your company representatives and your customers is a crucial area for customer success. Thanks to machine learning algorithms, businesses are able to manage those customer-facing moments more efficiently. According to techtarget.com, “Emotion analysis through text and speech analytics can paint a more complete picture when combined with the overall first call resolution (FCR) metric, indicating the level of confidence customers feel about whether the answer they received has resolved the issue at hand.”

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Machine learning algorithms are also helping customers reach the right representative in a shorter space of time (and alleviating much of their frustration in the process) by smartly routing calls based on their nature to the right person with the appropriate knowledge and skill-level. This, in turn, reduces call duration periods, repeat calls or call abandonment rates by unhappy callers. On-screen prompts based on the machine learning analyses of callers’ moods, or other indicators, are also allowing contact centre agents to more effectively deal with customer queries or problems and reduces the need for customers to make often frustrating repeat calls to the business.

Machine learning can be used to reduce call volumes by eliminating the need for customers to call when there is, for example, a network fault for a telecommunications company / ISP or reception issues with a satellite company. By analysing voice / speech patterns, emotions and words from incoming calls, machine learning can identify 1) that there is a an issue (anger or irritation based on tone of voice), 2) what the issue might be (“slow line speed” or “no reception”) and 3) where it might be (based on the caller’s location).;

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