Answers.Body: <p>This sounds to me like a use case for a chatbot. You would have different intents reflecting the types of user queries that your system can respond to. The intent matching can be done by pattern matching, machine learning (classification), or a combination of the two (hybrid). You can then use the chatbot to ask clarification questions or elicit more information to identify which live agent would be the best person to take over the call. Essentially each live agent would have a list of intents plus added information (such as geographical area etc) which you then compare against the data from the caller to find the best match.</p>
  
  <p>If this is a voice call you'd need to put an <a href="https://en.wikipedia.org/wiki/Speech_recognition" rel="nofollow noreferrer">ASR</a> system at the front of the pipeline. Chatbots can usually do live-agent handover to then pass control to a human agent at any time in the conversation.</p>
  
  <p><em>[Disclaimer: I work for a company that operates in exactly that area and whose system works as described above]</em></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-18T10:10:46.590Z
Answers.LastActivityDate: 2020-03-18T10:10:46.590Z
Answers.UserDisplayName: Oliver Mason
Answers.Body: <p>AI can transform the customer experience is by providing personalized content. For example, When you see video recommendation on YouTube, you'll know that it's from AI technology. I recommend you to read this article for knowing how they work: A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework (<a href="https://www.hindawi.com/journals/wcmc/2018/8263704/" rel="nofollow noreferrer">abstract</a>, <a href="https://www.ijrter.com/papers/volume-3/issue-5/analysis-of-movie-recommender-system-using-collaborative-filtering.pdf" rel="nofollow noreferrer">article</a>).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-28T00:43:34.820Z
Answers.Text: Welcome to SE:AI!  Could I ask you to provide a quick paragraph on why the fundamentals of a movie recommendation could apply to customer service applications in general?  (We especially like answers that discuss the underlying theory.)
Answers.UserDisplayName: DukeZhou
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Answers.CreationDate: 2020-04-21T11:32:45.920Z
Answers.LastActivityDate: 2020-05-14T21:57:53.830Z
Answers.LastEditDate: 2020-05-14T21:57:53.830Z
Answers.UserDisplayName: Rosfia Rani
Body: <p>I'm trying to find out how AI can help with efficient customer service, in fact call routing to the right agent. My usecase is given context of a query from a customer and agents' expertise, how can we do the matching?</p>
  
  <p>Generally, how is this problem solved? What sub-topic within AI is suitable for this problems? Classification, recommender systems, ...? Any pointers to open-source projects would be very helpful.</p>
  
Tags: <machine-learning><deep-learning><classification><recommender-system><efficiency>
Title: Using AI to enhance customer service
UserDisplayName: Mary
ThreadText: # Using AI to enhance customer service
  
  (Asked by: username_0 on 2020-03-18)
  
  I'm trying to find out how AI can help with efficient customer service, in fact call routing to the right agent. My usecase is given context of a query from a customer and agents' expertise, how can we do the matching?
  
  Generally, how is this problem solved? What sub-topic within AI is suitable for this problems? Classification, recommender systems, ...? Any pointers to open-source projects would be very helpful.
  
  **Tags:** machine-learning, deep-learning, classification, recommender-system, efficiency
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-03-18. Score: 1
  
  This sounds to me like a use case for a chatbot. You would have different intents reflecting the types of user queries that your system can respond to. The intent matching can be done by pattern matching, machine learning (classification), or a combination of the two (hybrid). You can then use the chatbot to ask clarification questions or elicit more information to identify which live agent would be the best person to take over the call. Essentially each live agent would have a list of intents plus added information (such as geographical area etc) which you then compare against the data from the caller to find the best match.
  
  If this is a voice call you'd need to put an [ASR](https://en.wikipedia.org/wiki/Speech_recognition) system at the front of the pipeline. Chatbots can usually do live-agent handover to then pass control to a human agent at any time in the conversation.
  
  *[Disclaimer: I work for a company that operates in exactly that area and whose system works as described above]*
  
  
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  ### Answer by username_2 on 2020-04-21. Score: 1
  
  AI can transform the customer experience is by providing personalized content. For example, When you see video recommendation on YouTube, you'll know that it's from AI technology. I recommend you to read this article for knowing how they work: A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework ([abstract](https://www.hindawi.com/journals/wcmc/2018/8263704/), [article](https://www.ijrter.com/papers/volume-3/issue-5/analysis-of-movie-recommender-system-using-collaborative-filtering.pdf)).
  
  > **Comments:**
  > **username_3**: Welcome to SE:AI!  Could I ask you to provide a quick paragraph on why the fundamentals of a movie recommendation could apply to customer service applications in general?  (We especially like answers that discuss the underlying theory.)  
  
  ---
  
  