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AI NLP & NLU engine for better CX in Call Centers

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Bhagvan Kommadi's picture
Founder of First Quantum Computing Startup in India Architect Corner

Bhagvan Kommadi is the Founder of Architect Corner - AI startup and has around 20 years of experience in the industry, ranging from large-scale enterprise development to help incubate software...

  • Member since 2019
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  • May 24, 2019
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This item is part of the Special Issue - 2019-05 - Customer Care, click here for more

Around the globe, call centers have the same issue as other types of client service centers: they have too much information to investigate. A single call center unit could produce thousands of hours per day of call recording. While it may be feasible to manually listen to prior recordings to verify the performance of a single rep's calls, it is not feasible for a company to obtain business understanding from its broad range of customer call recordings. While it may be feasible to manually listen to prior call recordings to verify the performance of a single representative's calls, it is not feasible for a company to obtain business understanding from its broad range of customer call recordings. 

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The company has these following questions regarding their customers:

What do my clients report about our latest product's durability?

After significant changes to our phone script, do we receive more or fewer refunds?

A sentiment not only suggests clients ' potential intervention, but it can also be combined with other information–such as buying or canceling information. The communication of the client can be good or bad and ultimately affect the bottom line. Angry clients may have a greater chance of churning or refunding. This enables us to uncover the "why" and the "who" behind regular contact subjects and offer a definite, quantified financial action strategy. NLP is used to comprehend and forecast text-based communications trends. Speech Analytics is used to encode the text material of the recorded language.

This encoding allows us to recognize themes for customer support calls, monitor patterns over time, and evaluate operator performance. The NLP engine based on AI tests and analyzes calls and emails. Threads and discussions can be identified by the NLP engine. This will assist in understanding and analyzing the discussions ' feelings, sound, mood, feelings, utterances and other parameters. This will assist to use the clients ' historical information and their attitudes to target campaigns. Historical data helps the next best product to be predicted and recommended. Increasing attention has been paid to the rise of chatbots by natural language processing. These robots can respond quickly to basic customer requests and questions.

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Thank Bhagvan for the Post!
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