Conversation Analytics

Conversation Analytics

Conversation Analytics

Conversation Analytics

Conversation Intelligence: What It Is and Why You Need It

Conversation Intelligence: What It Is and Why You Need It

Conversation Intelligence: What It Is and Why You Need It

Conversation Intelligence: What It Is and Why You Need It

|

|

|

|

Oct 16, 2023

Oct 16, 2023

Oct 16, 2023

Oct 16, 2023

Every customer conversation — whether it's a sales call, service ticket, or support chat — is a rich source of insights into consumer sentiment and product feedback. According to Gartner, 84% of customer service leaders believe customer data and analytics are important to achieve organizational objectives. 

If your contact center handles only a few calls, your team might review each interaction and improve the metrics for your contact center. But as call volumes increase, manually sourcing insights can be time-consuming and labor-intensive. That's where conversation intelligence (CI) comes into play.

Conversation intelligence has the capacity to turn everyday dialogues into invaluable insights. 

What Is Conversation Intelligence?

Conversation intelligence gathers and interprets customer interactions across various communication channels. Relying on artificial intelligence and machine learning, conversation intelligence tools perform three broad functions:

  1. It captures information from spoken and written conversations, even when they're messy and unorganized.

  2. It matches this information with structured metadata about the interaction.

  3. It analyzes emotions and feelings to understand the desires, needs, opinions, and expectations of customers.

Unlike external research and market intelligence strategies, CI tells exactly what your customers like and don’t like about your products. Turning this information into a positive customer experience is a significant opportunity to gain a competitive edge. PwC found that most companies need to improve their customer experience, according to 54% of U.S. consumers.

How Conversation Intelligence Works

Conversation intelligence platforms leverage natural language understanding (NLU) to evaluate the information from your call center. Here are the six key components of a conversation analytics platform:

  1. Speech-to-text conversion: Conversation intelligence begins by converting spoken words into written text. Advanced speech recognition technology ensures accuracy, even in noisy environments or with multiple speakers.

  2. Sentiment analysis: This component gauges the emotional tone of a conversation. It can determine whether the participants are happy, frustrated, satisfied, or dissatisfied. Understanding sentiment is crucial for improving customer experiences.

  3. Keyword and phrase recognition: Conversation AI tools identify specific keywords and phrases discussed during conversations to support teams looking for common customer issues.

  4. Call summarization: Instead of sifting through hours of recorded conversations, conversation artificial intelligence helps to provide concise summaries and highlight key takeaways.

  5. Performance metrics: Contact centers can track and measure key performance indicators (KPIs) related to conversations, such as call duration, response time, or customer satisfaction scores.

  6. Integration with CRM systems: Many CI tools integrate with customer relationship management (CRM) systems, ensuring that insights from conversations are directly linked to customer profiles and sales pipelines. They can also write back into the CRM to further enrich the data. 

Generative Conversation Intelligence

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. 

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, which we’ve been explaining in detail throughout this blog.

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 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.

Role of AI in Conversation Intelligence

AI, and specifically generative AI, enhances conversation intelligence by enabling more advanced and context-aware interactions. Here's how:

  1. Conversational analysis: Generative AI goes beyond basic keyword and sentiment analysis, enabling CI systems to grasp the subtleties of human-level interactions. For instance, it can identify sarcasm and humor that may be missed by conventional keyword-based approaches. 

  2. Faster time-to-value: Unlike traditional ML models that require extensive pre-training and configuration, generative models leverage transfer learning from large datasets. This approach reduces the time to set up a CI system, enabling organizations to realize value from conversation intelligence faster.

  3. Proactive analysis: While traditional systems rely on predefined rules and configurations to identify issues, genAI continuously adapts and detects emerging issues in conversations. This adaptability enables organizations to address challenges and opportunities proactively rather than reactively.

  4. Multilingual support: GenAI models handle multiple languages, making them invaluable for international call centers or businesses serving diverse customer bases.

  5. Analyzing trends: In addition to analyzing individual conversations, generative CI can identify and analyze overarching trends in the data. It can identify patterns, common themes, and emerging topics in conversations, providing valuable insights for decision-making and strategic planning.

Read our guide to learn more about how generative AI is transforming contact centers.  

Benefits of Conversation Intelligence

Call centers handle a steady flow of customer inquiries, issues, and requests on a daily basis. In this demanding environment, conversation intelligence emerges as a time-saving tool to: 

  1. Deliver Insights

With a vast amount of data at its disposal, a CI platform quickly identifies emerging trends and patterns, including frequently mentioned issues, common customer queries, and evolving customer preferences. 

Conversational AI also helps to assess and score agents based on predefined criteria, such as:

  • Adherence to scripts

  • Professionalism

  • Resolution efficiency

  • Compliance with company policies 

This scoring helps managers to evaluate performance intelligence objectively and reduce inconsistencies. 

With Echo AI's Conversation Intelligence platform, you can analyze the conversations that are relevant to your unique use case. You can apply filters such as ticket type, tags, customer segment, sentiment, data source, or any other personalized criteria.‍


  1. Increase Conversions

Conversion rates correlate to how well agents understand customer intent, their actions, and the information they share.

Conversation intelligence software provides real-time insights and suggestions to agents during customer interactions. For example, if a customer expresses interest in a specific product, CI prompts the agent to provide more information. This guidance ensures that agents make the most of every conversation to drive conversions. 

CI tools also assist with follow-up strategies if a customer expresses interest during a call but doesn't commit. These tools trigger automated follow-up emails or calls at the most opportune times, increasing the chances of conversion.

  1. Improve Revenue 

Conversation intelligence platforms help agents resolve customer inquiries and objections more efficiently. The result is shorter sales cycle times, enabling call centers to close deals faster and accelerate revenue generation.

By analyzing customer conversations and feedback, intelligent conversation tools help to make pricing strategy decisions such as:

  • Adjusting pricing models

  • Offering discounts

  • Bundling products and services.

Adapting pricing strategies is a great strategy to maximize revenue while maintaining customer satisfaction. McKinsey found that, on average, a 1% increase in price leads to an 8.7% increase in operating profits.

  1. Automate Agent Onboarding 

Onboarding and providing performance coaching to agents is a significant bottleneck for call centers.

  • Balancing resource allocation between operations and training is a challenge that affects the contact center’s productivity.

  • Effective onboarding and coaching require time, which can be challenging in high-call volume environments.

  • Varied skill levels among new agents can complicate tailored training.

Conversation intelligence platforms facilitate agent training and help them get up to speed quickly. CI tools analyze past customer interactions and generate training materials for effective communication, objection handling, and best practices. 

A successful agent onboarding process also reduces the attrition rates, which, according to Medallia, are at an all-time high of 65% in contact centers. 

  1. Reduce Customer Churn

As data volume increases with each new customer, manually monitoring the health of every account is tedious and impractical. Neglecting the status of customer satisfaction, however, leads to dissatisfaction and increased churn.

  • Conversation intelligence tools can detect early warning signs of potential churn such as negative sentiment, frequent complaints, and declining customer engagement. Being aware of these insights helps to take prompt actions before customers leave. Here’s how:

  • By using generative AI, businesses can personalize their communication to align with individual preferences and needs, making customers feel heard and appreciated.

  • By analyzing customer feedback, call centers gain insights into the product’s scope for improvement. This knowledge helps in improving customer experience through new features, usability improvements, and bug fixes.

  • Conversation intelligence tools can identify areas where response times are lagging — often a driver of customer frustration. This information helps to streamline response processes, ensuring prompt resolution of customer concerns. 

  • Conversational AI creates a feedback loop to continuously learn from customer interactions and adapt communication strategies accordingly. This ongoing improvement helps build stronger customer relationships and improves customer retention.

We explore this topic in depth in our recent webinar. View the on-demand webinar to learn how to leverage Gen AI to reduce churn. 

Who can use conversation intelligence software?

Conversation intelligence tools can be used by a wide range of professionals across a number of industries and business functions, namely go-to-market teams. Sales teams benefit from it by analyzing calls to identify successful strategies and improve pitches, while customer service departments use it to monitor interactions, enhance response times, and reduce churn. Marketing teams leverage the insights to understand customer preferences and create more targeted campaigns. Product development teams gather direct feedback to prioritize feature development and address common issues. Human resources departments apply this software in recruitment processes, ensuring compliance during interviews and enhancing employee training programs. Even healthcare providers find it valuable for transcribing and analyzing patient interactions, thereby improving patient care and maintaining accurate medical records. Overall, any organization that relies on customer or client interactions can significantly enhance their performance and satisfaction using conversation intelligence software.

How managers can use conversation intelligence

Managers in contact centers can significantly enhance their operations using conversation intelligence software. Conversation intelligence tools allow them to analyze customer interactions in real-time, providing deep insights into common issues and areas where agents may need additional training. By monitoring these interactions, managers can identify patterns in customer complaints, track performance metrics, and ensure compliance with company standards. This leads to more targeted coaching and development programs for agents, improving overall service quality. Additionally, conversation intelligence helps in predicting potential churn by highlighting frequent pain points and dissatisfaction, enabling proactive measures to retain customers. Overall, this technology empowers managers to make data-driven decisions that enhance both agent performance and customer satisfaction, helping them better report on what’s happening on the ground to C-level executives.

“Echo AI quickly morphed into really a team comparison and a departmental directional tool... More than just an individual coaching tool.” —Hugo Munday, Director of CX, ThriftBooks

How frontline agents can use conversation intelligence

Frontline agents in customer service can greatly benefit from using conversation intelligence in their daily operations. CI allows them to access real-time transcriptions and analysis of their interactions, providing immediate feedback on their performance and helping them adjust their communication strategies on the fly. Agents can quickly identify and respond to customer emotions and concerns by recognizing sentiment patterns, which enhances their ability to provide empathetic and effective service.

Conversation intelligence also helps agents with knowledge management by surfacing relevant information and solutions during a call, reducing the time spent searching for answers and improving first-call resolution rates. Additionally, it can offer personalized coaching tips and highlight best practices based on previous interactions, enabling agents to continuously improve their skills and deliver superior customer experiences. Overall, conversation intelligence empowers frontline agents to be more responsive, informed, and effective in their roles, leading to higher customer satisfaction and operational efficiency.

How sales teams can use conversation intelligence

Sales teams in direct-to-consumer (DTC) business models can leverage conversation intelligence to enhance their effectiveness and drive better outcomes. By analyzing customer interactions, sales representatives can gain insights into customer preferences, pain points, and buying behaviors. This data enables them to personalize their sales pitches and tailor their communication strategies to meet individual customer needs, increasing the likelihood of conversions.

Conversation intelligence tools can automatically transcribe and analyze sales calls, providing real-time feedback and highlighting areas for improvement. This helps sales reps identify which approaches are most effective and refine their techniques accordingly. For example, if a particular product feature frequently comes up in successful sales conversations, reps can emphasize that feature in future pitches.

Conversation intelligence can track sentiment and detect when a customer is showing signs of hesitation or interest. This allows sales reps to adjust their approach in real-time, addressing concerns promptly and reinforcing positive aspects of the product. By understanding customer sentiment, sales teams can also identify potential objections before they become deal-breakers and prepare to counter them effectively.

These tools also facilitate better follow-up strategies by flagging key moments in conversations that indicate a high likelihood of future engagement or purchase. This ensures that no potential sales opportunities are missed and that follow-ups are timely and relevant.

In addition, conversation intelligence can be used to train new sales reps by providing examples of successful and unsuccessful calls. This accelerates the learning curve and helps new team members adopt best practices quickly.

What features should you look for in a conversation intelligence platform?

It is very important to consider whether insights surfaced in a conversation intelligence platform can be self-generated. 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.

Empower Your Team with Echo AI's Conversation Intelligence

Echo AI's Conversation Intelligence platform leverages the latest in generative AI to capture and analyze your customer conversations. The analyzed data enables you to identify trends, seize business opportunities, and make data-driven decisions.

Book a demo and turn conversations into insights.


Every customer conversation — whether it's a sales call, service ticket, or support chat — is a rich source of insights into consumer sentiment and product feedback. According to Gartner, 84% of customer service leaders believe customer data and analytics are important to achieve organizational objectives. 

If your contact center handles only a few calls, your team might review each interaction and improve the metrics for your contact center. But as call volumes increase, manually sourcing insights can be time-consuming and labor-intensive. That's where conversation intelligence (CI) comes into play.

Conversation intelligence has the capacity to turn everyday dialogues into invaluable insights. 

What Is Conversation Intelligence?

Conversation intelligence gathers and interprets customer interactions across various communication channels. Relying on artificial intelligence and machine learning, conversation intelligence tools perform three broad functions:

  1. It captures information from spoken and written conversations, even when they're messy and unorganized.

  2. It matches this information with structured metadata about the interaction.

  3. It analyzes emotions and feelings to understand the desires, needs, opinions, and expectations of customers.

Unlike external research and market intelligence strategies, CI tells exactly what your customers like and don’t like about your products. Turning this information into a positive customer experience is a significant opportunity to gain a competitive edge. PwC found that most companies need to improve their customer experience, according to 54% of U.S. consumers.

How Conversation Intelligence Works

Conversation intelligence platforms leverage natural language understanding (NLU) to evaluate the information from your call center. Here are the six key components of a conversation analytics platform:

  1. Speech-to-text conversion: Conversation intelligence begins by converting spoken words into written text. Advanced speech recognition technology ensures accuracy, even in noisy environments or with multiple speakers.

  2. Sentiment analysis: This component gauges the emotional tone of a conversation. It can determine whether the participants are happy, frustrated, satisfied, or dissatisfied. Understanding sentiment is crucial for improving customer experiences.

  3. Keyword and phrase recognition: Conversation AI tools identify specific keywords and phrases discussed during conversations to support teams looking for common customer issues.

  4. Call summarization: Instead of sifting through hours of recorded conversations, conversation artificial intelligence helps to provide concise summaries and highlight key takeaways.

  5. Performance metrics: Contact centers can track and measure key performance indicators (KPIs) related to conversations, such as call duration, response time, or customer satisfaction scores.

  6. Integration with CRM systems: Many CI tools integrate with customer relationship management (CRM) systems, ensuring that insights from conversations are directly linked to customer profiles and sales pipelines. They can also write back into the CRM to further enrich the data. 

Generative Conversation Intelligence

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. 

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, which we’ve been explaining in detail throughout this blog.

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 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.

Role of AI in Conversation Intelligence

AI, and specifically generative AI, enhances conversation intelligence by enabling more advanced and context-aware interactions. Here's how:

  1. Conversational analysis: Generative AI goes beyond basic keyword and sentiment analysis, enabling CI systems to grasp the subtleties of human-level interactions. For instance, it can identify sarcasm and humor that may be missed by conventional keyword-based approaches. 

  2. Faster time-to-value: Unlike traditional ML models that require extensive pre-training and configuration, generative models leverage transfer learning from large datasets. This approach reduces the time to set up a CI system, enabling organizations to realize value from conversation intelligence faster.

  3. Proactive analysis: While traditional systems rely on predefined rules and configurations to identify issues, genAI continuously adapts and detects emerging issues in conversations. This adaptability enables organizations to address challenges and opportunities proactively rather than reactively.

  4. Multilingual support: GenAI models handle multiple languages, making them invaluable for international call centers or businesses serving diverse customer bases.

  5. Analyzing trends: In addition to analyzing individual conversations, generative CI can identify and analyze overarching trends in the data. It can identify patterns, common themes, and emerging topics in conversations, providing valuable insights for decision-making and strategic planning.

Read our guide to learn more about how generative AI is transforming contact centers.  

Benefits of Conversation Intelligence

Call centers handle a steady flow of customer inquiries, issues, and requests on a daily basis. In this demanding environment, conversation intelligence emerges as a time-saving tool to: 

  1. Deliver Insights

With a vast amount of data at its disposal, a CI platform quickly identifies emerging trends and patterns, including frequently mentioned issues, common customer queries, and evolving customer preferences. 

Conversational AI also helps to assess and score agents based on predefined criteria, such as:

  • Adherence to scripts

  • Professionalism

  • Resolution efficiency

  • Compliance with company policies 

This scoring helps managers to evaluate performance intelligence objectively and reduce inconsistencies. 

With Echo AI's Conversation Intelligence platform, you can analyze the conversations that are relevant to your unique use case. You can apply filters such as ticket type, tags, customer segment, sentiment, data source, or any other personalized criteria.‍


  1. Increase Conversions

Conversion rates correlate to how well agents understand customer intent, their actions, and the information they share.

Conversation intelligence software provides real-time insights and suggestions to agents during customer interactions. For example, if a customer expresses interest in a specific product, CI prompts the agent to provide more information. This guidance ensures that agents make the most of every conversation to drive conversions. 

CI tools also assist with follow-up strategies if a customer expresses interest during a call but doesn't commit. These tools trigger automated follow-up emails or calls at the most opportune times, increasing the chances of conversion.

  1. Improve Revenue 

Conversation intelligence platforms help agents resolve customer inquiries and objections more efficiently. The result is shorter sales cycle times, enabling call centers to close deals faster and accelerate revenue generation.

By analyzing customer conversations and feedback, intelligent conversation tools help to make pricing strategy decisions such as:

  • Adjusting pricing models

  • Offering discounts

  • Bundling products and services.

Adapting pricing strategies is a great strategy to maximize revenue while maintaining customer satisfaction. McKinsey found that, on average, a 1% increase in price leads to an 8.7% increase in operating profits.

  1. Automate Agent Onboarding 

Onboarding and providing performance coaching to agents is a significant bottleneck for call centers.

  • Balancing resource allocation between operations and training is a challenge that affects the contact center’s productivity.

  • Effective onboarding and coaching require time, which can be challenging in high-call volume environments.

  • Varied skill levels among new agents can complicate tailored training.

Conversation intelligence platforms facilitate agent training and help them get up to speed quickly. CI tools analyze past customer interactions and generate training materials for effective communication, objection handling, and best practices. 

A successful agent onboarding process also reduces the attrition rates, which, according to Medallia, are at an all-time high of 65% in contact centers. 

  1. Reduce Customer Churn

As data volume increases with each new customer, manually monitoring the health of every account is tedious and impractical. Neglecting the status of customer satisfaction, however, leads to dissatisfaction and increased churn.

  • Conversation intelligence tools can detect early warning signs of potential churn such as negative sentiment, frequent complaints, and declining customer engagement. Being aware of these insights helps to take prompt actions before customers leave. Here’s how:

  • By using generative AI, businesses can personalize their communication to align with individual preferences and needs, making customers feel heard and appreciated.

  • By analyzing customer feedback, call centers gain insights into the product’s scope for improvement. This knowledge helps in improving customer experience through new features, usability improvements, and bug fixes.

  • Conversation intelligence tools can identify areas where response times are lagging — often a driver of customer frustration. This information helps to streamline response processes, ensuring prompt resolution of customer concerns. 

  • Conversational AI creates a feedback loop to continuously learn from customer interactions and adapt communication strategies accordingly. This ongoing improvement helps build stronger customer relationships and improves customer retention.

We explore this topic in depth in our recent webinar. View the on-demand webinar to learn how to leverage Gen AI to reduce churn. 

Who can use conversation intelligence software?

Conversation intelligence tools can be used by a wide range of professionals across a number of industries and business functions, namely go-to-market teams. Sales teams benefit from it by analyzing calls to identify successful strategies and improve pitches, while customer service departments use it to monitor interactions, enhance response times, and reduce churn. Marketing teams leverage the insights to understand customer preferences and create more targeted campaigns. Product development teams gather direct feedback to prioritize feature development and address common issues. Human resources departments apply this software in recruitment processes, ensuring compliance during interviews and enhancing employee training programs. Even healthcare providers find it valuable for transcribing and analyzing patient interactions, thereby improving patient care and maintaining accurate medical records. Overall, any organization that relies on customer or client interactions can significantly enhance their performance and satisfaction using conversation intelligence software.

How managers can use conversation intelligence

Managers in contact centers can significantly enhance their operations using conversation intelligence software. Conversation intelligence tools allow them to analyze customer interactions in real-time, providing deep insights into common issues and areas where agents may need additional training. By monitoring these interactions, managers can identify patterns in customer complaints, track performance metrics, and ensure compliance with company standards. This leads to more targeted coaching and development programs for agents, improving overall service quality. Additionally, conversation intelligence helps in predicting potential churn by highlighting frequent pain points and dissatisfaction, enabling proactive measures to retain customers. Overall, this technology empowers managers to make data-driven decisions that enhance both agent performance and customer satisfaction, helping them better report on what’s happening on the ground to C-level executives.

“Echo AI quickly morphed into really a team comparison and a departmental directional tool... More than just an individual coaching tool.” —Hugo Munday, Director of CX, ThriftBooks

How frontline agents can use conversation intelligence

Frontline agents in customer service can greatly benefit from using conversation intelligence in their daily operations. CI allows them to access real-time transcriptions and analysis of their interactions, providing immediate feedback on their performance and helping them adjust their communication strategies on the fly. Agents can quickly identify and respond to customer emotions and concerns by recognizing sentiment patterns, which enhances their ability to provide empathetic and effective service.

Conversation intelligence also helps agents with knowledge management by surfacing relevant information and solutions during a call, reducing the time spent searching for answers and improving first-call resolution rates. Additionally, it can offer personalized coaching tips and highlight best practices based on previous interactions, enabling agents to continuously improve their skills and deliver superior customer experiences. Overall, conversation intelligence empowers frontline agents to be more responsive, informed, and effective in their roles, leading to higher customer satisfaction and operational efficiency.

How sales teams can use conversation intelligence

Sales teams in direct-to-consumer (DTC) business models can leverage conversation intelligence to enhance their effectiveness and drive better outcomes. By analyzing customer interactions, sales representatives can gain insights into customer preferences, pain points, and buying behaviors. This data enables them to personalize their sales pitches and tailor their communication strategies to meet individual customer needs, increasing the likelihood of conversions.

Conversation intelligence tools can automatically transcribe and analyze sales calls, providing real-time feedback and highlighting areas for improvement. This helps sales reps identify which approaches are most effective and refine their techniques accordingly. For example, if a particular product feature frequently comes up in successful sales conversations, reps can emphasize that feature in future pitches.

Conversation intelligence can track sentiment and detect when a customer is showing signs of hesitation or interest. This allows sales reps to adjust their approach in real-time, addressing concerns promptly and reinforcing positive aspects of the product. By understanding customer sentiment, sales teams can also identify potential objections before they become deal-breakers and prepare to counter them effectively.

These tools also facilitate better follow-up strategies by flagging key moments in conversations that indicate a high likelihood of future engagement or purchase. This ensures that no potential sales opportunities are missed and that follow-ups are timely and relevant.

In addition, conversation intelligence can be used to train new sales reps by providing examples of successful and unsuccessful calls. This accelerates the learning curve and helps new team members adopt best practices quickly.

What features should you look for in a conversation intelligence platform?

It is very important to consider whether insights surfaced in a conversation intelligence platform can be self-generated. 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.

Empower Your Team with Echo AI's Conversation Intelligence

Echo AI's Conversation Intelligence platform leverages the latest in generative AI to capture and analyze your customer conversations. The analyzed data enables you to identify trends, seize business opportunities, and make data-driven decisions.

Book a demo and turn conversations into insights.


Every customer conversation — whether it's a sales call, service ticket, or support chat — is a rich source of insights into consumer sentiment and product feedback. According to Gartner, 84% of customer service leaders believe customer data and analytics are important to achieve organizational objectives. 

If your contact center handles only a few calls, your team might review each interaction and improve the metrics for your contact center. But as call volumes increase, manually sourcing insights can be time-consuming and labor-intensive. That's where conversation intelligence (CI) comes into play.

Conversation intelligence has the capacity to turn everyday dialogues into invaluable insights. 

What Is Conversation Intelligence?

Conversation intelligence gathers and interprets customer interactions across various communication channels. Relying on artificial intelligence and machine learning, conversation intelligence tools perform three broad functions:

  1. It captures information from spoken and written conversations, even when they're messy and unorganized.

  2. It matches this information with structured metadata about the interaction.

  3. It analyzes emotions and feelings to understand the desires, needs, opinions, and expectations of customers.

Unlike external research and market intelligence strategies, CI tells exactly what your customers like and don’t like about your products. Turning this information into a positive customer experience is a significant opportunity to gain a competitive edge. PwC found that most companies need to improve their customer experience, according to 54% of U.S. consumers.

How Conversation Intelligence Works

Conversation intelligence platforms leverage natural language understanding (NLU) to evaluate the information from your call center. Here are the six key components of a conversation analytics platform:

  1. Speech-to-text conversion: Conversation intelligence begins by converting spoken words into written text. Advanced speech recognition technology ensures accuracy, even in noisy environments or with multiple speakers.

  2. Sentiment analysis: This component gauges the emotional tone of a conversation. It can determine whether the participants are happy, frustrated, satisfied, or dissatisfied. Understanding sentiment is crucial for improving customer experiences.

  3. Keyword and phrase recognition: Conversation AI tools identify specific keywords and phrases discussed during conversations to support teams looking for common customer issues.

  4. Call summarization: Instead of sifting through hours of recorded conversations, conversation artificial intelligence helps to provide concise summaries and highlight key takeaways.

  5. Performance metrics: Contact centers can track and measure key performance indicators (KPIs) related to conversations, such as call duration, response time, or customer satisfaction scores.

  6. Integration with CRM systems: Many CI tools integrate with customer relationship management (CRM) systems, ensuring that insights from conversations are directly linked to customer profiles and sales pipelines. They can also write back into the CRM to further enrich the data. 

Generative Conversation Intelligence

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. 

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, which we’ve been explaining in detail throughout this blog.

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 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.

Role of AI in Conversation Intelligence

AI, and specifically generative AI, enhances conversation intelligence by enabling more advanced and context-aware interactions. Here's how:

  1. Conversational analysis: Generative AI goes beyond basic keyword and sentiment analysis, enabling CI systems to grasp the subtleties of human-level interactions. For instance, it can identify sarcasm and humor that may be missed by conventional keyword-based approaches. 

  2. Faster time-to-value: Unlike traditional ML models that require extensive pre-training and configuration, generative models leverage transfer learning from large datasets. This approach reduces the time to set up a CI system, enabling organizations to realize value from conversation intelligence faster.

  3. Proactive analysis: While traditional systems rely on predefined rules and configurations to identify issues, genAI continuously adapts and detects emerging issues in conversations. This adaptability enables organizations to address challenges and opportunities proactively rather than reactively.

  4. Multilingual support: GenAI models handle multiple languages, making them invaluable for international call centers or businesses serving diverse customer bases.

  5. Analyzing trends: In addition to analyzing individual conversations, generative CI can identify and analyze overarching trends in the data. It can identify patterns, common themes, and emerging topics in conversations, providing valuable insights for decision-making and strategic planning.

Read our guide to learn more about how generative AI is transforming contact centers.  

Benefits of Conversation Intelligence

Call centers handle a steady flow of customer inquiries, issues, and requests on a daily basis. In this demanding environment, conversation intelligence emerges as a time-saving tool to: 

  1. Deliver Insights

With a vast amount of data at its disposal, a CI platform quickly identifies emerging trends and patterns, including frequently mentioned issues, common customer queries, and evolving customer preferences. 

Conversational AI also helps to assess and score agents based on predefined criteria, such as:

  • Adherence to scripts

  • Professionalism

  • Resolution efficiency

  • Compliance with company policies 

This scoring helps managers to evaluate performance intelligence objectively and reduce inconsistencies. 

With Echo AI's Conversation Intelligence platform, you can analyze the conversations that are relevant to your unique use case. You can apply filters such as ticket type, tags, customer segment, sentiment, data source, or any other personalized criteria.‍


  1. Increase Conversions

Conversion rates correlate to how well agents understand customer intent, their actions, and the information they share.

Conversation intelligence software provides real-time insights and suggestions to agents during customer interactions. For example, if a customer expresses interest in a specific product, CI prompts the agent to provide more information. This guidance ensures that agents make the most of every conversation to drive conversions. 

CI tools also assist with follow-up strategies if a customer expresses interest during a call but doesn't commit. These tools trigger automated follow-up emails or calls at the most opportune times, increasing the chances of conversion.

  1. Improve Revenue 

Conversation intelligence platforms help agents resolve customer inquiries and objections more efficiently. The result is shorter sales cycle times, enabling call centers to close deals faster and accelerate revenue generation.

By analyzing customer conversations and feedback, intelligent conversation tools help to make pricing strategy decisions such as:

  • Adjusting pricing models

  • Offering discounts

  • Bundling products and services.

Adapting pricing strategies is a great strategy to maximize revenue while maintaining customer satisfaction. McKinsey found that, on average, a 1% increase in price leads to an 8.7% increase in operating profits.

  1. Automate Agent Onboarding 

Onboarding and providing performance coaching to agents is a significant bottleneck for call centers.

  • Balancing resource allocation between operations and training is a challenge that affects the contact center’s productivity.

  • Effective onboarding and coaching require time, which can be challenging in high-call volume environments.

  • Varied skill levels among new agents can complicate tailored training.

Conversation intelligence platforms facilitate agent training and help them get up to speed quickly. CI tools analyze past customer interactions and generate training materials for effective communication, objection handling, and best practices. 

A successful agent onboarding process also reduces the attrition rates, which, according to Medallia, are at an all-time high of 65% in contact centers. 

  1. Reduce Customer Churn

As data volume increases with each new customer, manually monitoring the health of every account is tedious and impractical. Neglecting the status of customer satisfaction, however, leads to dissatisfaction and increased churn.

  • Conversation intelligence tools can detect early warning signs of potential churn such as negative sentiment, frequent complaints, and declining customer engagement. Being aware of these insights helps to take prompt actions before customers leave. Here’s how:

  • By using generative AI, businesses can personalize their communication to align with individual preferences and needs, making customers feel heard and appreciated.

  • By analyzing customer feedback, call centers gain insights into the product’s scope for improvement. This knowledge helps in improving customer experience through new features, usability improvements, and bug fixes.

  • Conversation intelligence tools can identify areas where response times are lagging — often a driver of customer frustration. This information helps to streamline response processes, ensuring prompt resolution of customer concerns. 

  • Conversational AI creates a feedback loop to continuously learn from customer interactions and adapt communication strategies accordingly. This ongoing improvement helps build stronger customer relationships and improves customer retention.

We explore this topic in depth in our recent webinar. View the on-demand webinar to learn how to leverage Gen AI to reduce churn. 

Who can use conversation intelligence software?

Conversation intelligence tools can be used by a wide range of professionals across a number of industries and business functions, namely go-to-market teams. Sales teams benefit from it by analyzing calls to identify successful strategies and improve pitches, while customer service departments use it to monitor interactions, enhance response times, and reduce churn. Marketing teams leverage the insights to understand customer preferences and create more targeted campaigns. Product development teams gather direct feedback to prioritize feature development and address common issues. Human resources departments apply this software in recruitment processes, ensuring compliance during interviews and enhancing employee training programs. Even healthcare providers find it valuable for transcribing and analyzing patient interactions, thereby improving patient care and maintaining accurate medical records. Overall, any organization that relies on customer or client interactions can significantly enhance their performance and satisfaction using conversation intelligence software.

How managers can use conversation intelligence

Managers in contact centers can significantly enhance their operations using conversation intelligence software. Conversation intelligence tools allow them to analyze customer interactions in real-time, providing deep insights into common issues and areas where agents may need additional training. By monitoring these interactions, managers can identify patterns in customer complaints, track performance metrics, and ensure compliance with company standards. This leads to more targeted coaching and development programs for agents, improving overall service quality. Additionally, conversation intelligence helps in predicting potential churn by highlighting frequent pain points and dissatisfaction, enabling proactive measures to retain customers. Overall, this technology empowers managers to make data-driven decisions that enhance both agent performance and customer satisfaction, helping them better report on what’s happening on the ground to C-level executives.

“Echo AI quickly morphed into really a team comparison and a departmental directional tool... More than just an individual coaching tool.” —Hugo Munday, Director of CX, ThriftBooks

How frontline agents can use conversation intelligence

Frontline agents in customer service can greatly benefit from using conversation intelligence in their daily operations. CI allows them to access real-time transcriptions and analysis of their interactions, providing immediate feedback on their performance and helping them adjust their communication strategies on the fly. Agents can quickly identify and respond to customer emotions and concerns by recognizing sentiment patterns, which enhances their ability to provide empathetic and effective service.

Conversation intelligence also helps agents with knowledge management by surfacing relevant information and solutions during a call, reducing the time spent searching for answers and improving first-call resolution rates. Additionally, it can offer personalized coaching tips and highlight best practices based on previous interactions, enabling agents to continuously improve their skills and deliver superior customer experiences. Overall, conversation intelligence empowers frontline agents to be more responsive, informed, and effective in their roles, leading to higher customer satisfaction and operational efficiency.

How sales teams can use conversation intelligence

Sales teams in direct-to-consumer (DTC) business models can leverage conversation intelligence to enhance their effectiveness and drive better outcomes. By analyzing customer interactions, sales representatives can gain insights into customer preferences, pain points, and buying behaviors. This data enables them to personalize their sales pitches and tailor their communication strategies to meet individual customer needs, increasing the likelihood of conversions.

Conversation intelligence tools can automatically transcribe and analyze sales calls, providing real-time feedback and highlighting areas for improvement. This helps sales reps identify which approaches are most effective and refine their techniques accordingly. For example, if a particular product feature frequently comes up in successful sales conversations, reps can emphasize that feature in future pitches.

Conversation intelligence can track sentiment and detect when a customer is showing signs of hesitation or interest. This allows sales reps to adjust their approach in real-time, addressing concerns promptly and reinforcing positive aspects of the product. By understanding customer sentiment, sales teams can also identify potential objections before they become deal-breakers and prepare to counter them effectively.

These tools also facilitate better follow-up strategies by flagging key moments in conversations that indicate a high likelihood of future engagement or purchase. This ensures that no potential sales opportunities are missed and that follow-ups are timely and relevant.

In addition, conversation intelligence can be used to train new sales reps by providing examples of successful and unsuccessful calls. This accelerates the learning curve and helps new team members adopt best practices quickly.

What features should you look for in a conversation intelligence platform?

It is very important to consider whether insights surfaced in a conversation intelligence platform can be self-generated. 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.

Empower Your Team with Echo AI's Conversation Intelligence

Echo AI's Conversation Intelligence platform leverages the latest in generative AI to capture and analyze your customer conversations. The analyzed data enables you to identify trends, seize business opportunities, and make data-driven decisions.

Book a demo and turn conversations into insights.


Every customer conversation — whether it's a sales call, service ticket, or support chat — is a rich source of insights into consumer sentiment and product feedback. According to Gartner, 84% of customer service leaders believe customer data and analytics are important to achieve organizational objectives. 

If your contact center handles only a few calls, your team might review each interaction and improve the metrics for your contact center. But as call volumes increase, manually sourcing insights can be time-consuming and labor-intensive. That's where conversation intelligence (CI) comes into play.

Conversation intelligence has the capacity to turn everyday dialogues into invaluable insights. 

What Is Conversation Intelligence?

Conversation intelligence gathers and interprets customer interactions across various communication channels. Relying on artificial intelligence and machine learning, conversation intelligence tools perform three broad functions:

  1. It captures information from spoken and written conversations, even when they're messy and unorganized.

  2. It matches this information with structured metadata about the interaction.

  3. It analyzes emotions and feelings to understand the desires, needs, opinions, and expectations of customers.

Unlike external research and market intelligence strategies, CI tells exactly what your customers like and don’t like about your products. Turning this information into a positive customer experience is a significant opportunity to gain a competitive edge. PwC found that most companies need to improve their customer experience, according to 54% of U.S. consumers.

How Conversation Intelligence Works

Conversation intelligence platforms leverage natural language understanding (NLU) to evaluate the information from your call center. Here are the six key components of a conversation analytics platform:

  1. Speech-to-text conversion: Conversation intelligence begins by converting spoken words into written text. Advanced speech recognition technology ensures accuracy, even in noisy environments or with multiple speakers.

  2. Sentiment analysis: This component gauges the emotional tone of a conversation. It can determine whether the participants are happy, frustrated, satisfied, or dissatisfied. Understanding sentiment is crucial for improving customer experiences.

  3. Keyword and phrase recognition: Conversation AI tools identify specific keywords and phrases discussed during conversations to support teams looking for common customer issues.

  4. Call summarization: Instead of sifting through hours of recorded conversations, conversation artificial intelligence helps to provide concise summaries and highlight key takeaways.

  5. Performance metrics: Contact centers can track and measure key performance indicators (KPIs) related to conversations, such as call duration, response time, or customer satisfaction scores.

  6. Integration with CRM systems: Many CI tools integrate with customer relationship management (CRM) systems, ensuring that insights from conversations are directly linked to customer profiles and sales pipelines. They can also write back into the CRM to further enrich the data. 

Generative Conversation Intelligence

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. 

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, which we’ve been explaining in detail throughout this blog.

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 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.

Role of AI in Conversation Intelligence

AI, and specifically generative AI, enhances conversation intelligence by enabling more advanced and context-aware interactions. Here's how:

  1. Conversational analysis: Generative AI goes beyond basic keyword and sentiment analysis, enabling CI systems to grasp the subtleties of human-level interactions. For instance, it can identify sarcasm and humor that may be missed by conventional keyword-based approaches. 

  2. Faster time-to-value: Unlike traditional ML models that require extensive pre-training and configuration, generative models leverage transfer learning from large datasets. This approach reduces the time to set up a CI system, enabling organizations to realize value from conversation intelligence faster.

  3. Proactive analysis: While traditional systems rely on predefined rules and configurations to identify issues, genAI continuously adapts and detects emerging issues in conversations. This adaptability enables organizations to address challenges and opportunities proactively rather than reactively.

  4. Multilingual support: GenAI models handle multiple languages, making them invaluable for international call centers or businesses serving diverse customer bases.

  5. Analyzing trends: In addition to analyzing individual conversations, generative CI can identify and analyze overarching trends in the data. It can identify patterns, common themes, and emerging topics in conversations, providing valuable insights for decision-making and strategic planning.

Read our guide to learn more about how generative AI is transforming contact centers.  

Benefits of Conversation Intelligence

Call centers handle a steady flow of customer inquiries, issues, and requests on a daily basis. In this demanding environment, conversation intelligence emerges as a time-saving tool to: 

  1. Deliver Insights

With a vast amount of data at its disposal, a CI platform quickly identifies emerging trends and patterns, including frequently mentioned issues, common customer queries, and evolving customer preferences. 

Conversational AI also helps to assess and score agents based on predefined criteria, such as:

  • Adherence to scripts

  • Professionalism

  • Resolution efficiency

  • Compliance with company policies 

This scoring helps managers to evaluate performance intelligence objectively and reduce inconsistencies. 

With Echo AI's Conversation Intelligence platform, you can analyze the conversations that are relevant to your unique use case. You can apply filters such as ticket type, tags, customer segment, sentiment, data source, or any other personalized criteria.‍


  1. Increase Conversions

Conversion rates correlate to how well agents understand customer intent, their actions, and the information they share.

Conversation intelligence software provides real-time insights and suggestions to agents during customer interactions. For example, if a customer expresses interest in a specific product, CI prompts the agent to provide more information. This guidance ensures that agents make the most of every conversation to drive conversions. 

CI tools also assist with follow-up strategies if a customer expresses interest during a call but doesn't commit. These tools trigger automated follow-up emails or calls at the most opportune times, increasing the chances of conversion.

  1. Improve Revenue 

Conversation intelligence platforms help agents resolve customer inquiries and objections more efficiently. The result is shorter sales cycle times, enabling call centers to close deals faster and accelerate revenue generation.

By analyzing customer conversations and feedback, intelligent conversation tools help to make pricing strategy decisions such as:

  • Adjusting pricing models

  • Offering discounts

  • Bundling products and services.

Adapting pricing strategies is a great strategy to maximize revenue while maintaining customer satisfaction. McKinsey found that, on average, a 1% increase in price leads to an 8.7% increase in operating profits.

  1. Automate Agent Onboarding 

Onboarding and providing performance coaching to agents is a significant bottleneck for call centers.

  • Balancing resource allocation between operations and training is a challenge that affects the contact center’s productivity.

  • Effective onboarding and coaching require time, which can be challenging in high-call volume environments.

  • Varied skill levels among new agents can complicate tailored training.

Conversation intelligence platforms facilitate agent training and help them get up to speed quickly. CI tools analyze past customer interactions and generate training materials for effective communication, objection handling, and best practices. 

A successful agent onboarding process also reduces the attrition rates, which, according to Medallia, are at an all-time high of 65% in contact centers. 

  1. Reduce Customer Churn

As data volume increases with each new customer, manually monitoring the health of every account is tedious and impractical. Neglecting the status of customer satisfaction, however, leads to dissatisfaction and increased churn.

  • Conversation intelligence tools can detect early warning signs of potential churn such as negative sentiment, frequent complaints, and declining customer engagement. Being aware of these insights helps to take prompt actions before customers leave. Here’s how:

  • By using generative AI, businesses can personalize their communication to align with individual preferences and needs, making customers feel heard and appreciated.

  • By analyzing customer feedback, call centers gain insights into the product’s scope for improvement. This knowledge helps in improving customer experience through new features, usability improvements, and bug fixes.

  • Conversation intelligence tools can identify areas where response times are lagging — often a driver of customer frustration. This information helps to streamline response processes, ensuring prompt resolution of customer concerns. 

  • Conversational AI creates a feedback loop to continuously learn from customer interactions and adapt communication strategies accordingly. This ongoing improvement helps build stronger customer relationships and improves customer retention.

We explore this topic in depth in our recent webinar. View the on-demand webinar to learn how to leverage Gen AI to reduce churn. 

Who can use conversation intelligence software?

Conversation intelligence tools can be used by a wide range of professionals across a number of industries and business functions, namely go-to-market teams. Sales teams benefit from it by analyzing calls to identify successful strategies and improve pitches, while customer service departments use it to monitor interactions, enhance response times, and reduce churn. Marketing teams leverage the insights to understand customer preferences and create more targeted campaigns. Product development teams gather direct feedback to prioritize feature development and address common issues. Human resources departments apply this software in recruitment processes, ensuring compliance during interviews and enhancing employee training programs. Even healthcare providers find it valuable for transcribing and analyzing patient interactions, thereby improving patient care and maintaining accurate medical records. Overall, any organization that relies on customer or client interactions can significantly enhance their performance and satisfaction using conversation intelligence software.

How managers can use conversation intelligence

Managers in contact centers can significantly enhance their operations using conversation intelligence software. Conversation intelligence tools allow them to analyze customer interactions in real-time, providing deep insights into common issues and areas where agents may need additional training. By monitoring these interactions, managers can identify patterns in customer complaints, track performance metrics, and ensure compliance with company standards. This leads to more targeted coaching and development programs for agents, improving overall service quality. Additionally, conversation intelligence helps in predicting potential churn by highlighting frequent pain points and dissatisfaction, enabling proactive measures to retain customers. Overall, this technology empowers managers to make data-driven decisions that enhance both agent performance and customer satisfaction, helping them better report on what’s happening on the ground to C-level executives.

“Echo AI quickly morphed into really a team comparison and a departmental directional tool... More than just an individual coaching tool.” —Hugo Munday, Director of CX, ThriftBooks

How frontline agents can use conversation intelligence

Frontline agents in customer service can greatly benefit from using conversation intelligence in their daily operations. CI allows them to access real-time transcriptions and analysis of their interactions, providing immediate feedback on their performance and helping them adjust their communication strategies on the fly. Agents can quickly identify and respond to customer emotions and concerns by recognizing sentiment patterns, which enhances their ability to provide empathetic and effective service.

Conversation intelligence also helps agents with knowledge management by surfacing relevant information and solutions during a call, reducing the time spent searching for answers and improving first-call resolution rates. Additionally, it can offer personalized coaching tips and highlight best practices based on previous interactions, enabling agents to continuously improve their skills and deliver superior customer experiences. Overall, conversation intelligence empowers frontline agents to be more responsive, informed, and effective in their roles, leading to higher customer satisfaction and operational efficiency.

How sales teams can use conversation intelligence

Sales teams in direct-to-consumer (DTC) business models can leverage conversation intelligence to enhance their effectiveness and drive better outcomes. By analyzing customer interactions, sales representatives can gain insights into customer preferences, pain points, and buying behaviors. This data enables them to personalize their sales pitches and tailor their communication strategies to meet individual customer needs, increasing the likelihood of conversions.

Conversation intelligence tools can automatically transcribe and analyze sales calls, providing real-time feedback and highlighting areas for improvement. This helps sales reps identify which approaches are most effective and refine their techniques accordingly. For example, if a particular product feature frequently comes up in successful sales conversations, reps can emphasize that feature in future pitches.

Conversation intelligence can track sentiment and detect when a customer is showing signs of hesitation or interest. This allows sales reps to adjust their approach in real-time, addressing concerns promptly and reinforcing positive aspects of the product. By understanding customer sentiment, sales teams can also identify potential objections before they become deal-breakers and prepare to counter them effectively.

These tools also facilitate better follow-up strategies by flagging key moments in conversations that indicate a high likelihood of future engagement or purchase. This ensures that no potential sales opportunities are missed and that follow-ups are timely and relevant.

In addition, conversation intelligence can be used to train new sales reps by providing examples of successful and unsuccessful calls. This accelerates the learning curve and helps new team members adopt best practices quickly.

What features should you look for in a conversation intelligence platform?

It is very important to consider whether insights surfaced in a conversation intelligence platform can be self-generated. 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.

Empower Your Team with Echo AI's Conversation Intelligence

Echo AI's Conversation Intelligence platform leverages the latest in generative AI to capture and analyze your customer conversations. The analyzed data enables you to identify trends, seize business opportunities, and make data-driven decisions.

Book a demo and turn conversations into insights.