Generative AI | Blog

Generative AI | Blog

Generative AI | Blog

Generative AI | Blog

Generative Conversation Intelligence: What It Is, and How It Works

Generative Conversation Intelligence: What It Is, and How It Works

Generative Conversation Intelligence: What It Is, and How It Works

Generative Conversation Intelligence: What It Is, and How It Works

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May 1, 2024

May 1, 2024

May 1, 2024

May 1, 2024

Since generative AI came onto the scene, interest has exploded in the technology’s promise to improve the customer experience.

With the ability to generate human-like text outputs, generative capabilities have been focused on enhancing conversational engines, improving live agent assistance, and even fully automating chat responses (for better or for worse).

Yet there are applications that extend far beyond these use cases that can vastly improve the way businesses unlock customer needs and desires. There’s one largely unexplored application that is picking up a lot of traction: pulling deep, self-generated insights from customer conversations.

Tapping into customer conversations is one of the most powerful levers you can pull to make an immediate, lasting impact on the business. By bringing the power of generative capabilities into data analysis, businesses can sift through millions of customer data points to find risks and opportunities across product, supply chain, marketing, and more. 

In this blog, we’ll break down generative conversation intelligence, how it works, and how leading brands are using it to improve customer service and speed up growth.


Since generative AI came onto the scene, interest has exploded in the technology’s promise to improve the customer experience.

With the ability to generate human-like text outputs, generative capabilities have been focused on enhancing conversational engines, improving live agent assistance, and even fully automating chat responses (for better or for worse).

Yet there are applications that extend far beyond these use cases that can vastly improve the way businesses unlock customer needs and desires. There’s one largely unexplored application that is picking up a lot of traction: pulling deep, self-generated insights from customer conversations.

Tapping into customer conversations is one of the most powerful levers you can pull to make an immediate, lasting impact on the business. By bringing the power of generative capabilities into data analysis, businesses can sift through millions of customer data points to find risks and opportunities across product, supply chain, marketing, and more. 

In this blog, we’ll break down generative conversation intelligence, how it works, and how leading brands are using it to improve customer service and speed up growth.


Since generative AI came onto the scene, interest has exploded in the technology’s promise to improve the customer experience.

With the ability to generate human-like text outputs, generative capabilities have been focused on enhancing conversational engines, improving live agent assistance, and even fully automating chat responses (for better or for worse).

Yet there are applications that extend far beyond these use cases that can vastly improve the way businesses unlock customer needs and desires. There’s one largely unexplored application that is picking up a lot of traction: pulling deep, self-generated insights from customer conversations.

Tapping into customer conversations is one of the most powerful levers you can pull to make an immediate, lasting impact on the business. By bringing the power of generative capabilities into data analysis, businesses can sift through millions of customer data points to find risks and opportunities across product, supply chain, marketing, and more. 

In this blog, we’ll break down generative conversation intelligence, how it works, and how leading brands are using it to improve customer service and speed up growth.


Since generative AI came onto the scene, interest has exploded in the technology’s promise to improve the customer experience.

With the ability to generate human-like text outputs, generative capabilities have been focused on enhancing conversational engines, improving live agent assistance, and even fully automating chat responses (for better or for worse).

Yet there are applications that extend far beyond these use cases that can vastly improve the way businesses unlock customer needs and desires. There’s one largely unexplored application that is picking up a lot of traction: pulling deep, self-generated insights from customer conversations.

Tapping into customer conversations is one of the most powerful levers you can pull to make an immediate, lasting impact on the business. By bringing the power of generative capabilities into data analysis, businesses can sift through millions of customer data points to find risks and opportunities across product, supply chain, marketing, and more. 

In this blog, we’ll break down generative conversation intelligence, how it works, and how leading brands are using it to improve customer service and speed up growth.


What is generative conversation intelligence?

Generative conversation intelligence (CI) uses multiple large language models, or LLMs, to analyze millions of customer data points at once and extract robust, AI-generated insights on critical business risks and opportunities. Generative CI surfaces topics, themes and keywords by itself at a level of detail that is unmatched by most other technologies on the market. 


Generative conversation intelligence (CI) uses multiple large language models, or LLMs, to analyze millions of customer data points at once and extract robust, AI-generated insights on critical business risks and opportunities. Generative CI surfaces topics, themes and keywords by itself at a level of detail that is unmatched by most other technologies on the market. 


Generative conversation intelligence (CI) uses multiple large language models, or LLMs, to analyze millions of customer data points at once and extract robust, AI-generated insights on critical business risks and opportunities. Generative CI surfaces topics, themes and keywords by itself at a level of detail that is unmatched by most other technologies on the market. 


Generative conversation intelligence (CI) uses multiple large language models, or LLMs, to analyze millions of customer data points at once and extract robust, AI-generated insights on critical business risks and opportunities. Generative CI surfaces topics, themes and keywords by itself at a level of detail that is unmatched by most other technologies on the market. 


So, how does it work?

The first step involves taking call recordings and running this data through speech-to-text AI models, followed by analyzing text data from emails, chats, surveys, social feeds, and more to make sense of the context of the conversations. This combined use of machine learning and natural language processing technology is called conversation intelligence.

From here, the data is routed through a pipeline of numerous LLMs, each designed to tackle a specific action or business question. Some common analysis points include:

  • Customer intent, or the reason behind why a customer has expressed a frustration, concern, or desire

  • Customer sentiment, which uncovers instances of negative and positive emotions expressed by the customer

  • Agent sentiment, which uncovers instances of negative and positive emotions expressed by the agent

  • Specific details about supply chain, operations, fulfillment, product quality, and more

Once the data has been passed through this second layer of analysis, generative insights surface key trends and themes that have been detected from the data. Generative insights refer to the learnings that have been autonomously detected across millions of data points without any manual tagging or categorization. These generative insights are summarized by category— providing users with helpful overviews of intent, sentiment, and resolution scores—and organized into high-level themes and trends. With these topic summarizations, business leaders can quickly uncover the reasons behind call surges, cancellations, returns, and more.

The result? Root causes of pressing business issues can be identified faster, even without pre-set keywords or tags.


The first step involves taking call recordings and running this data through speech-to-text AI models, followed by analyzing text data from emails, chats, surveys, social feeds, and more to make sense of the context of the conversations. This combined use of machine learning and natural language processing technology is called conversation intelligence.

From here, the data is routed through a pipeline of numerous LLMs, each designed to tackle a specific action or business question. Some common analysis points include:

  • Customer intent, or the reason behind why a customer has expressed a frustration, concern, or desire

  • Customer sentiment, which uncovers instances of negative and positive emotions expressed by the customer

  • Agent sentiment, which uncovers instances of negative and positive emotions expressed by the agent

  • Specific details about supply chain, operations, fulfillment, product quality, and more

Once the data has been passed through this second layer of analysis, generative insights surface key trends and themes that have been detected from the data. Generative insights refer to the learnings that have been autonomously detected across millions of data points without any manual tagging or categorization. These generative insights are summarized by category— providing users with helpful overviews of intent, sentiment, and resolution scores—and organized into high-level themes and trends. With these topic summarizations, business leaders can quickly uncover the reasons behind call surges, cancellations, returns, and more.

The result? Root causes of pressing business issues can be identified faster, even without pre-set keywords or tags.


The first step involves taking call recordings and running this data through speech-to-text AI models, followed by analyzing text data from emails, chats, surveys, social feeds, and more to make sense of the context of the conversations. This combined use of machine learning and natural language processing technology is called conversation intelligence.

From here, the data is routed through a pipeline of numerous LLMs, each designed to tackle a specific action or business question. Some common analysis points include:

  • Customer intent, or the reason behind why a customer has expressed a frustration, concern, or desire

  • Customer sentiment, which uncovers instances of negative and positive emotions expressed by the customer

  • Agent sentiment, which uncovers instances of negative and positive emotions expressed by the agent

  • Specific details about supply chain, operations, fulfillment, product quality, and more

Once the data has been passed through this second layer of analysis, generative insights surface key trends and themes that have been detected from the data. Generative insights refer to the learnings that have been autonomously detected across millions of data points without any manual tagging or categorization. These generative insights are summarized by category— providing users with helpful overviews of intent, sentiment, and resolution scores—and organized into high-level themes and trends. With these topic summarizations, business leaders can quickly uncover the reasons behind call surges, cancellations, returns, and more.

The result? Root causes of pressing business issues can be identified faster, even without pre-set keywords or tags.


The first step involves taking call recordings and running this data through speech-to-text AI models, followed by analyzing text data from emails, chats, surveys, social feeds, and more to make sense of the context of the conversations. This combined use of machine learning and natural language processing technology is called conversation intelligence.

From here, the data is routed through a pipeline of numerous LLMs, each designed to tackle a specific action or business question. Some common analysis points include:

  • Customer intent, or the reason behind why a customer has expressed a frustration, concern, or desire

  • Customer sentiment, which uncovers instances of negative and positive emotions expressed by the customer

  • Agent sentiment, which uncovers instances of negative and positive emotions expressed by the agent

  • Specific details about supply chain, operations, fulfillment, product quality, and more

Once the data has been passed through this second layer of analysis, generative insights surface key trends and themes that have been detected from the data. Generative insights refer to the learnings that have been autonomously detected across millions of data points without any manual tagging or categorization. These generative insights are summarized by category— providing users with helpful overviews of intent, sentiment, and resolution scores—and organized into high-level themes and trends. With these topic summarizations, business leaders can quickly uncover the reasons behind call surges, cancellations, returns, and more.

The result? Root causes of pressing business issues can be identified faster, even without pre-set keywords or tags.


The difference between CI and generative CI

The significant difference between conversation intelligence and generative conversation intelligence is the elimination of this tedious step of configuration. Most conversation intelligence platforms today still require the manual effort of inputting keywords, tags and user-driven category creation, which places the burden on the user to build an insights infrastructure. While this is great for already-known issues, it makes it very difficult to proactively find insights that businesses don’t know to look for, leaving hidden insights unseen.

Insights that cannot autonomously surface unknown problems place a heavy burden on support teams.

For example, if there is a surge in support tickets during peak season, like the holidays, where does a support lead start? Maybe it’s by searching for “return” or “cancellation” keywords conversations, and looking up specific products or services that have been a friction point in the past.

With manual search and no advanced analytics or conversation monitoring system in place, it may take days to find the cause and address it. 

With conversation intelligence, it may take an hour to run data analysis and surface the top possible reasons behind the issue assuming the right keywords, tags, and topics are already being monitored.

With generative conversation intelligence, it takes minutes to automatically see the top ten reasons from highest to lowest, get nuanced details on the context of the situation, and quickly alert the necessary teams on what is happening to prevent any further breakdown in the supply chain or delivery process. 


The significant difference between conversation intelligence and generative conversation intelligence is the elimination of this tedious step of configuration. Most conversation intelligence platforms today still require the manual effort of inputting keywords, tags and user-driven category creation, which places the burden on the user to build an insights infrastructure. While this is great for already-known issues, it makes it very difficult to proactively find insights that businesses don’t know to look for, leaving hidden insights unseen.

Insights that cannot autonomously surface unknown problems place a heavy burden on support teams.

For example, if there is a surge in support tickets during peak season, like the holidays, where does a support lead start? Maybe it’s by searching for “return” or “cancellation” keywords conversations, and looking up specific products or services that have been a friction point in the past.

With manual search and no advanced analytics or conversation monitoring system in place, it may take days to find the cause and address it. 

With conversation intelligence, it may take an hour to run data analysis and surface the top possible reasons behind the issue assuming the right keywords, tags, and topics are already being monitored.

With generative conversation intelligence, it takes minutes to automatically see the top ten reasons from highest to lowest, get nuanced details on the context of the situation, and quickly alert the necessary teams on what is happening to prevent any further breakdown in the supply chain or delivery process. 


The significant difference between conversation intelligence and generative conversation intelligence is the elimination of this tedious step of configuration. Most conversation intelligence platforms today still require the manual effort of inputting keywords, tags and user-driven category creation, which places the burden on the user to build an insights infrastructure. While this is great for already-known issues, it makes it very difficult to proactively find insights that businesses don’t know to look for, leaving hidden insights unseen.

Insights that cannot autonomously surface unknown problems place a heavy burden on support teams.

For example, if there is a surge in support tickets during peak season, like the holidays, where does a support lead start? Maybe it’s by searching for “return” or “cancellation” keywords conversations, and looking up specific products or services that have been a friction point in the past.

With manual search and no advanced analytics or conversation monitoring system in place, it may take days to find the cause and address it. 

With conversation intelligence, it may take an hour to run data analysis and surface the top possible reasons behind the issue assuming the right keywords, tags, and topics are already being monitored.

With generative conversation intelligence, it takes minutes to automatically see the top ten reasons from highest to lowest, get nuanced details on the context of the situation, and quickly alert the necessary teams on what is happening to prevent any further breakdown in the supply chain or delivery process. 


The significant difference between conversation intelligence and generative conversation intelligence is the elimination of this tedious step of configuration. Most conversation intelligence platforms today still require the manual effort of inputting keywords, tags and user-driven category creation, which places the burden on the user to build an insights infrastructure. While this is great for already-known issues, it makes it very difficult to proactively find insights that businesses don’t know to look for, leaving hidden insights unseen.

Insights that cannot autonomously surface unknown problems place a heavy burden on support teams.

For example, if there is a surge in support tickets during peak season, like the holidays, where does a support lead start? Maybe it’s by searching for “return” or “cancellation” keywords conversations, and looking up specific products or services that have been a friction point in the past.

With manual search and no advanced analytics or conversation monitoring system in place, it may take days to find the cause and address it. 

With conversation intelligence, it may take an hour to run data analysis and surface the top possible reasons behind the issue assuming the right keywords, tags, and topics are already being monitored.

With generative conversation intelligence, it takes minutes to automatically see the top ten reasons from highest to lowest, get nuanced details on the context of the situation, and quickly alert the necessary teams on what is happening to prevent any further breakdown in the supply chain or delivery process. 


How generative CI is used today

Generative CI can be used to impact every layer of the business, not just the customer service org. From product to marketing to operations, business leaders can take action on key trends to prevent churn and drive growth. Here’s how.

Uncovering hidden customer insights

With analysis across every channel, businesses can get deep, detailed answers to unlock a new level of customer understanding. With generative insights, getting to the bottom of emerging issues is faster and enables customer service teams to more easily know what to do. If a customer expresses discontent around product quality, getting detailed information on the complaint, the specific product, and whether it’s a trend across several feedback channels can be surfaced in seconds.

Automated quality checks for agents (and bots)

For teams looking to use insights to inform a more automated quality process, automated quality scoring can help teams go from manually reviewing 1% of conversations to automatically reviewing 100% of interactions. AutoQA eliminates the need for manual scorecards, and enables businesses to quickly identify agent behaviors that need improvement. Plus, it can be used to automatically grade chatbot effectiveness, ensuring that broken dialogue flows don’t spiral out of control.

Taking automated corrective actions 

Insights are only as powerful as the actions they drive. Generative CI can be used to immediately detect subtle intent signals and trigger automated actions, connecting engagement tools and data platforms to improve customer segmentation and engagement strategies. For example, a trend detected across upgrade inquiries can be used to trigger a marketing promotion to customers who are great fit for it. This way, you can ensure that your insights are put to use immediately, and driving value well beyond the customer support organization.


Generative CI can be used to impact every layer of the business, not just the customer service org. From product to marketing to operations, business leaders can take action on key trends to prevent churn and drive growth. Here’s how.

Uncovering hidden customer insights

With analysis across every channel, businesses can get deep, detailed answers to unlock a new level of customer understanding. With generative insights, getting to the bottom of emerging issues is faster and enables customer service teams to more easily know what to do. If a customer expresses discontent around product quality, getting detailed information on the complaint, the specific product, and whether it’s a trend across several feedback channels can be surfaced in seconds.

Automated quality checks for agents (and bots)

For teams looking to use insights to inform a more automated quality process, automated quality scoring can help teams go from manually reviewing 1% of conversations to automatically reviewing 100% of interactions. AutoQA eliminates the need for manual scorecards, and enables businesses to quickly identify agent behaviors that need improvement. Plus, it can be used to automatically grade chatbot effectiveness, ensuring that broken dialogue flows don’t spiral out of control.

Taking automated corrective actions 

Insights are only as powerful as the actions they drive. Generative CI can be used to immediately detect subtle intent signals and trigger automated actions, connecting engagement tools and data platforms to improve customer segmentation and engagement strategies. For example, a trend detected across upgrade inquiries can be used to trigger a marketing promotion to customers who are great fit for it. This way, you can ensure that your insights are put to use immediately, and driving value well beyond the customer support organization.


Generative CI can be used to impact every layer of the business, not just the customer service org. From product to marketing to operations, business leaders can take action on key trends to prevent churn and drive growth. Here’s how.

Uncovering hidden customer insights

With analysis across every channel, businesses can get deep, detailed answers to unlock a new level of customer understanding. With generative insights, getting to the bottom of emerging issues is faster and enables customer service teams to more easily know what to do. If a customer expresses discontent around product quality, getting detailed information on the complaint, the specific product, and whether it’s a trend across several feedback channels can be surfaced in seconds.

Automated quality checks for agents (and bots)

For teams looking to use insights to inform a more automated quality process, automated quality scoring can help teams go from manually reviewing 1% of conversations to automatically reviewing 100% of interactions. AutoQA eliminates the need for manual scorecards, and enables businesses to quickly identify agent behaviors that need improvement. Plus, it can be used to automatically grade chatbot effectiveness, ensuring that broken dialogue flows don’t spiral out of control.

Taking automated corrective actions 

Insights are only as powerful as the actions they drive. Generative CI can be used to immediately detect subtle intent signals and trigger automated actions, connecting engagement tools and data platforms to improve customer segmentation and engagement strategies. For example, a trend detected across upgrade inquiries can be used to trigger a marketing promotion to customers who are great fit for it. This way, you can ensure that your insights are put to use immediately, and driving value well beyond the customer support organization.


Generative CI can be used to impact every layer of the business, not just the customer service org. From product to marketing to operations, business leaders can take action on key trends to prevent churn and drive growth. Here’s how.

Uncovering hidden customer insights

With analysis across every channel, businesses can get deep, detailed answers to unlock a new level of customer understanding. With generative insights, getting to the bottom of emerging issues is faster and enables customer service teams to more easily know what to do. If a customer expresses discontent around product quality, getting detailed information on the complaint, the specific product, and whether it’s a trend across several feedback channels can be surfaced in seconds.

Automated quality checks for agents (and bots)

For teams looking to use insights to inform a more automated quality process, automated quality scoring can help teams go from manually reviewing 1% of conversations to automatically reviewing 100% of interactions. AutoQA eliminates the need for manual scorecards, and enables businesses to quickly identify agent behaviors that need improvement. Plus, it can be used to automatically grade chatbot effectiveness, ensuring that broken dialogue flows don’t spiral out of control.

Taking automated corrective actions 

Insights are only as powerful as the actions they drive. Generative CI can be used to immediately detect subtle intent signals and trigger automated actions, connecting engagement tools and data platforms to improve customer segmentation and engagement strategies. For example, a trend detected across upgrade inquiries can be used to trigger a marketing promotion to customers who are great fit for it. This way, you can ensure that your insights are put to use immediately, and driving value well beyond the customer support organization.


Ready to See Generative Conversation Intelligence in Action?

Discover what Echo AI has to offer in helping your business uncover critical business insights that can positively impact your entire organization, and not just the contact center. Learn More.

Discover what Echo AI has to offer in helping your business uncover critical business insights that can positively impact your entire organization, and not just the contact center. Learn More.

Discover what Echo AI has to offer in helping your business uncover critical business insights that can positively impact your entire organization, and not just the contact center. Learn More.

Discover what Echo AI has to offer in helping your business uncover critical business insights that can positively impact your entire organization, and not just the contact center. Learn More.