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AI in Your Contact Center: Understanding the Basics: Page 3 of 4
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Contact centers have several ways to use AI-based analytics. In the contact center, AI-based analytics usually uses metadata about an interaction, not the interaction itself. On the other hand, NLP-based analytics apply to the content of the interaction itself. In order to set context, NLP comprises two components: natural language understanding (NLU) and natural language generation (NLG). NLU can do the following, but is not limited to: discern intent, disambiguation, terminology extraction, translation, parsing, stemming, named entity extraction, topic segmentation, discern sentiment (emotion), summarize content, and tag content and taxonomy (classification). NLG simply generates language-based responses. (NLP will be the subject of another article.)
The operative decision criteria regarding which to use is: Do you care about what the customer has done or what the customer is doing right now?"
In my opinion, knowing what a customer is doing right now is more important than knowing what the customer did last week, or even just a moment ago. An example is when a bank customer uses an IVR to transfer money, and then presses "0" to reach an agent. In most cases, the IVR will transfer the customer to the call queue dealing with money transfers. However, the reality is that the money-transfer has most likely completed and the customer probably wants to do something else, like apply for a mortgage. Metadata, in this case, has its limitations. An NLU-based routing solution would ask the customer what he or she would like to do next and then route to the appropriate call queue -- or maybe even invoke NLP to process the loan application.
What surprises most of my clients is that they can perform analytics with minimal lag time (some might call this real-time analytics, but it's better described as near-real-time, I think).
Metadata analytics, such as possible with the Altocloud platform (now Genesys), can be performed with sub-second delay. Analyzing text can take a little longer because the machine may not understand the real intent until a recitation is complete. Amazon Lex, Gridspace Sift, and IBM Watson are examples of solutions that can perform NLP analytics.
Sentiment analysis is also of interest to most of my clients, Typically, these analysis are implemented once a conversational interface is establish. The only place I see customer implementing NLP analytics in advance of conversational interfaces is for the study of compliance. An example of this would be for analyzing agents' performance related to statutorily required recitations in the financial business.
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