Customer servicing is on the one hand a repetitive process, but on the other an activity requiring an individual approach. Machine learning methods are able to support customer service by enabling the classification of documents encountered in communication with customers.
It is also possible to keep constant track of customer opinions, making it possible to take necessary actions without any delay. Machine learning methods also enable customer profiling, so that the product information presented to customers can be optimised to correspond to their individual needs.
Continued progress in natural language processing now also enables “machine understanding” of the information contained in incoming messages. Digital communication, universally employed in today’s world, makes it easier to design a system that will understand the meaning of text. Meanwhile, voice-to-text technologies enable the effective conversion of speech into written text, which can then undergo computer analysis.
The ability to recognise the meaning of our customers’ messages makes it possible to classify them, which is a point of departure for designing an automated customer service system. Machine learning methods can be used to forward cases to the appropriate departments and people, to generate preliminary replies, or even to prepare the documentation related to particular matters. If actions are repetitive in nature, they can be fully automated – practically up to the stage of making recommendations or even taking decisions. A good example of this is automatic credit decisions, which are made based on a customer’s digital application, an automatically generated credit score, and verification of the data available in numerous public sources (such as social media and mobile operators’ databases), and are ultimately communicated to the customer in a fully automated manner. Time-consuming processes of this type can be handled by a computer in just a few seconds. By automating customer servicing in this way, you can guarantee a stable quality of service and enable all cases to be handled in a very short time.
Just like with credit decisions, almost every major process in customer servicing can be similarly automated. Since it is possible to apply automation to the lending of money and collection of repayments, why not use it in a similar way, for example, in the customer service department of a large telephone operator or insurance company?
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