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Machine Learning for Customer Service: What is it?

Wikipedia: Machine Learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task.

 

USING MACHINE LEARNING

There are many use-cases for ML in the context of contact center and customer service operations.

A common use-case is predicting the intent of customers, based on past conversations. This prediction can then be used to refine knowledge search and query routing across touchpoints. eGain’s powerful AI solution uses a combination of ML, curated learning, natural language processing, and evidence-based reasoning for fast, accurate, and compliant customer service and engagement.

Machine learning vs Curated learning: There is a time and a place

It is important to note that no one learning hammer works for all customer service use-case nails. ML can be more appropriate for some scenarios while you are better off going with curated learning in others.

ML is more appropriate where it is not possible to capture and maintain knowhow, whereas CL or SL (curated or supervised learning) is more appropriate where such knowhow can be captured from subject matter experts and maintained on an ongoing basis. One has to balance this against risk and how much is at stake—high-stakes or high-risk use-cases for more supervision and curation.


 

AI for Customer Service: More Resources

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Knowledge and AI Augmentation: The Essential Complement to Agent Training in the Modern Contact Center

Despite continued investment in onboarding and training, agents are struggling to answer customer questions. You can blame it on the “forgetting curve.” Or not. But the solution is in using AI knowledge. Know how.

Knowledge + AI for agent training

Knowledge Management and AI—Rx for Better CX and AX

Customers picked what pained them when getting customer service. Based on the symptoms, we provided the prescription.
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