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. 

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.‍

Alt text: Echo AI's conversation intelligence tool to filter conversations.

2. 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.

3. 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.

4. 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. 

5. 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. 

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.