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Tuesday 27th July 2021 01:30 PM

Management AI: Sentiment Analysis Is Important And Actionable


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EDITOR'S PICK447 views

Management AI: Sentiment Analysis Is Important And Actionable

“That’s bad!” means something different to people of different generations and in different contexts. Sometimes it means something is bad, but in other uses it’s a statement of positivity. One of the key areas of natural language processing (NLP), a subset of artificial intelligence (AI) is sentiment analysis, the ability to understand emotional tones in speech and print. It is an area that is the focus for a number of different functional applications.

Customer Service

The most obvious need for sentiment analysis is in customer service. In a few recent articles, I’ve covered chatbots. In their basic version, NLP systems understand the basics of a question and then respond with a canned answer – with a basic sentence filled in with keywords such as a customer or product name. However, many people get quickly frustrated with and angry at chatbots and automated call distribution (ACD) systems that don’t understand how upset the person is, providing the same answer pattern to all people.

Unfortunately, this is not a problem that is limited to automated systems. Many call centers have live support personnel who are limited to very strict call scripts, limiting their ability to address customer concerns and frustrations. That means both more call time and higher customer turnover. The ability to analysis calls can help call center management.

One example, while humorous in theory, shows the real problems involved in call centers: Swearing. It is not limited to George Carlin’s Seven Words or even what most people would consider swear words. Some people use words such as “Dang!” the same way as others use swear words, and the emotions are just the same. When people have reached the level of swearing, it’s a sign of a serious customer service issue.

CallMiner is one of a number of companies focused on helping their customers to better analyze calls and improve call center performance.

They have added sentiment analysis to their offerings. Sentiment analysis is critical, but it is only a starting point in taking action. For instance, as mentioned in the opening sentence, the first step is to look at a phrase in full

context and figure out whether it is being used as a positive or negative interjection, but there’s more to see. For instance, is this the first call the customer has made or the second? If the second, is it that the person is unhappy with the results of the first call? Was a certain phrase or portion of a call script driving negative responses?

“Sentiment analysis is the foundation to good call center performance,” said Jeff Gallino, CTO and Founder, CallMiner. “The tools measuring negative sentiment should also drive action, empowering support personnel to monitor, comprehend and resolve situations by offering deeper insight into how the customer feels and what they need.”  Mr. Gallino also said that one of the keys to changing call center responses was to stress empathy with the customer.

Management AI: Sentiment Analysis Forbes


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Posted on : Tuesday 27th July 2021 01:30 PM

Management AI: Sentiment Analysis Is Important And Actionable


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Posted by  Tronserve admin
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EDITOR'S PICK447 views

Management AI: Sentiment Analysis Is Important And Actionable

“That’s bad!” means something different to people of different generations and in different contexts. Sometimes it means something is bad, but in other uses it’s a statement of positivity. One of the key areas of natural language processing (NLP), a subset of artificial intelligence (AI) is sentiment analysis, the ability to understand emotional tones in speech and print. It is an area that is the focus for a number of different functional applications.

Customer Service

The most obvious need for sentiment analysis is in customer service. In a few recent articles, I’ve covered chatbots. In their basic version, NLP systems understand the basics of a question and then respond with a canned answer – with a basic sentence filled in with keywords such as a customer or product name. However, many people get quickly frustrated with and angry at chatbots and automated call distribution (ACD) systems that don’t understand how upset the person is, providing the same answer pattern to all people.

Unfortunately, this is not a problem that is limited to automated systems. Many call centers have live support personnel who are limited to very strict call scripts, limiting their ability to address customer concerns and frustrations. That means both more call time and higher customer turnover. The ability to analysis calls can help call center management.

One example, while humorous in theory, shows the real problems involved in call centers: Swearing. It is not limited to George Carlin’s Seven Words or even what most people would consider swear words. Some people use words such as “Dang!” the same way as others use swear words, and the emotions are just the same. When people have reached the level of swearing, it’s a sign of a serious customer service issue.

CallMiner is one of a number of companies focused on helping their customers to better analyze calls and improve call center performance.

They have added sentiment analysis to their offerings. Sentiment analysis is critical, but it is only a starting point in taking action. For instance, as mentioned in the opening sentence, the first step is to look at a phrase in full

context and figure out whether it is being used as a positive or negative interjection, but there’s more to see. For instance, is this the first call the customer has made or the second? If the second, is it that the person is unhappy with the results of the first call? Was a certain phrase or portion of a call script driving negative responses?

“Sentiment analysis is the foundation to good call center performance,” said Jeff Gallino, CTO and Founder, CallMiner. “The tools measuring negative sentiment should also drive action, empowering support personnel to monitor, comprehend and resolve situations by offering deeper insight into how the customer feels and what they need.”  Mr. Gallino also said that one of the keys to changing call center responses was to stress empathy with the customer.

Management AI: Sentiment Analysis Forbes

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sentiment analysis actionable