Conversation Analytics: Definition, Benefits, and How To Use It to Your Advantage

Whether you’re trying to close a deal with a lead on the phone or leaning on your chatbot to mitigate a customer complaint, every interaction can provide your team with valuable insights into customer preferences, pain points, and sentiments so you can better tailor your offerings and support. 

With the help of artificial intelligence (AI), conversation analytics helps you uncover those insights in seconds and turn them into meaningful information your team can use to enhance the customer experience and make data-driven business decisions. 

In this guide, we’ll cover what conversation analytics is, how it works, and how teams can utilize this technology to improve their sales and customer support processes. 

What is conversation analytics? 

Conversation analytics is the process of utilizing AI to analyze data from customer conversations and turning them into valuable insights that can help businesses improve their customer experiences. This data can be extracted from human-to-human conversations, like phone calls, or AI-to-human conversations, like interactions through chatbots or voicebots.  

Analyzing conversational data is important because it allows businesses to uncover hidden patterns in customer interactions to help them understand and predict future behavior. It also helps inform strategies across all customer touchpoints — before, during, and after the buyer’s journey. 

How does conversation analytics work?  

Conversation analytics uses natural language processing (NLP) and machine learning algorithms to understand conversations and turn them into useful data. 

Machine learning algorithms help conversation analytics tools recognize patterns in the data given. Essentially, it’s the process of humans teaching robots how to do something (and even how to learn things themselves) by feeding them huge amounts of data. The more data these machines are fed, the better they understand conversations and can predict future outcomes. 

Natural language processing is a type of machine learning that uncovers the meaning behind words, phrases, and sentences. In conversation analytics, it’s used to process and interpret conversations.  

Conversational data can be pulled from a variety of customer touchpoints, including: 

• Phone conversations 
• Social media interactions 
• Chat conversations (AI-to-human and human-to-human chats) 
• Voice bot calls
• SMS conversations 
• Third-party review sites 
• Survey responses 

After the data is gathered, it’s converted into a machine-readable format. This involves cleaning up the transcribed text and turning it into tokens, usually individual words or phrases, to make it easier for the machine to process and analyze.  

NLP and machine learning algorithms then identify language sentiment and extract specific keywords to help uncover relationships within the text.

Conversational analytics software will then output the most important information and summarize it to provide comprehensive insights that help uncover customer behavior, pinpoint opportunities to improve customer support, or conduct market research.

To put this process into perspective, let’s say you had a conversation with a customer who complained about a product delivery delay. That conversation was recorded, transcribed into text, and stored in a database. 

Conversation analytics tools process that information and provide you with comprehensive insights based on the interaction. In this case, it identifies a predominantly negative customer sentiment, flagging the need to handle the issue promptly to mitigate customer dissatisfaction. It also extracts important keywords like “delivery delay,” flagging the need to make improvements in the logistics department.  

Based on those insights, your team can look at the data and take action. For example:

• Your team can review the reason for the delivery delay and improve logistics strategies to ensure the issue doesn’t happen with other customers.  
• Your team can offer a prompt solution to the customer and provide them with a refund, discount, or other form of compensation in an effort to retain the customer.

5 key benefits of conversation analytics   

Conversation analytics comes with many advantages that can help you make business decisions to improve the customer experience. Here are five key benefits. 

Identifies customer pain points 

Analyzing conversations with customers can help you uncover trends in customer pain points and devise a strategy to mitigate them. 

Historical data in conversation analytics platforms can also help teams personalize their customer interactions. For example, if a customer expresses their concerns during a conversation, the data can help ensure agents address and solve those concerns during the next interaction. 

Saves time on manual processes 

Before conversation analytics tools, teams had to manually review customer conversations and develop frameworks for determining issue severity and how to address them. Automating this process can save teams time and resources, reduce human error, and provide immediate relevant insights that they can apply to future customer interactions.    

Improves agent performance 

Conversation analytics tools aren't only useful for the customer side of things — they can also provide detailed feedback on agents’ responses, communication, and problem-solving skills. This feedback can help agents identify areas for improvement so they can become more skilled in their interactions with buyers and ultimately convert more leads.    

These insights can also be useful when creating sales scripts for agents or virtual receptionists to reference. Analytics can look at previous scripts and conversations and pull phrases, keywords, and other metrics from successful examples to help agents improve their pitches or call handling.  

Increases sales and revenue  

By analyzing customer interactions, you can identify selling opportunities and tailor your strategies accordingly. 

For example, conversation analytics can identify hot or warm leads who are more likely to convert by identifying conversion signals, like a demo request or increased time on your pricing page. With this information, you can prioritize following up with those prospects instead of those who aren’t showing signs of readiness. Conversation analytics can also analyze conversations with top-performing sales agents to uncover which strategies result in the highest conversion rates. 

In addition, identifying patterns in customer data — like purchase history, trends in customer needs, or customer feedback — can help teams identify cross-selling and upselling opportunities. 

Say you’re a company that provides TV and internet services, and your analytics platform shows that a certain customer segment consistently complains about their TV channel selection and signal quality. You could adjust your sales strategy by cross-selling a higher-tier cable package or offering add-ons like a signal booster to that segment.

Enhances customer experience 

With all of the above in mind, conversation analytics tools ultimately improve customer experience. Data from past customer conversations can inform how to personalize and improve future interactions.  

For example, you can tailor your product recommendations or even your communication style to align with your customer's preferences. Nurturing these customer relationships can increase satisfaction and ultimately improve customer retention. 

Understanding the challenges of conversation analytics 

Though conversation analytics is highly valuable, it can come with a few challenges.

• Speech nuances: The human language has many conversation nuances, like sarcasm, slang, and tone, which can make it difficult for a machine to accurately interpret a conversation’s meaning. 

• Limited keywords: Specific keywords and phrases help language models understand customer sentiment. However, if a model has limited keywords and isn’t properly trained on industry-specific ones, it can be harder to gather helpful data. 

• Sentence errors: Typos or other sentence errors, whether in voice conversations or text, can confuse language models and lead to inaccurate analysis. 

• Accuracy of speech recognition: Inaccuracies in speech-to-text technology caused by background noise or unfamiliar accents and dialects can lead to lower-quality data.  

To overcome these challenges, you’ll want to keep your model up to date with relevant language and conversational trends, which requires fine-tuning and consistent model training. The more information your model has stored, the better it can perform. 

For example, you might train your model on different accents and dialects to improve speech recognition accuracy, teach your model to pick up on specific industry-related keywords, or train it to utilize spell-check capabilities to decrease the number of typos before conducting an analysis. 

5 effective conversation analytics use cases 

Conversation analytics can be used to support a variety of different departments and industries, but it’s especially beneficial in sales and customer support. 

Here are five common conversation analytics use cases for sales and customer support teams: 

1. Customer retention 

As mentioned above, conversation analytics can help identify common customer pain points. When robots collect conversation data and draw insights from them, it becomes easier for agents to predict customer dissatisfaction and take proactive measures. 

These tools analyze the historical data of previously churned customers, identify their shared characteristics, and segment current customers based on these patterns to forecast which ones are at risk of churning. 

For example, let’s say you’re a software company, and past data uncovered that customers previously churned because they weren’t fully utilizing all of your software features. With that in mind, if you notice poor user engagement with current customers, you can act on it by improving usability and accessibility through UX modifications and providing customers with the resources they need to fully embrace your product, like demos and product guides.

By catching these behaviors ahead of time, teams can make improvements that will increase customer retention and overall satisfaction. 

2. Lead qualification and conversion 

Conversation analytics can also be helpful for lead scoring and qualification. Since this technology can uncover a customer's tone and sentiment, it can make it easier to identify purchase readiness. This helps teams accurately define lead scoring criteria to determine whether a prospect is worth pursuing, resulting in higher-quality leads. 

Because conversation analytics provides a glimpse into previously successful sales strategies, the insights can be impactful for lead conversion. Data from sales calls helps agents improve their communication methods and implement strategies that historically convert leads to customers.

3. Market research 

Customer preferences and industry trends are always changing, and conversation analytics can be a valuable way to stay on top of them. 

Let’s say you just released a new product. You can utilize conversation analytics to analyze responses on social media, and sentiment analysis can uncover customers’ opinions on your launch — both positive and negative. With this information, you can better tailor your sales strategy, marketing strategy, and products to ensure they align with the current needs of your target market. 

4. Customer journey mapping 

Customer journey maps give a visual representation of the entire customer journey and experience — from initial awareness to purchase and ongoing support — and provide insights into customer interactions across various touchpoints.

With conversation analytics, you can gather meaningful data-driven insights to build an effective customer journey map to better understand your customer’s pain points and needs at every stage of their journey. 

Customer journey maps are also helpful for training conversational AI tools. Whether you’re building a chatbot or training voice assistants, generate a customer journey map using data from conversation analytics, then use that map to train your conversational AI tools to provide customers with the right answers at every stage of their journey.   

5. Customer routing 

Analyzing agent-to-customer conversations can also be beneficial in creating an effective call-routing strategy. A call-routing strategy automatically sends customers to an appropriate agent or staff member based on your set parameters and criteria. 

Conversation analytics can help tailor your call-routing strategy by helping you uncover trends in customer responses and determine which agents can handle certain requests based on previous conversations. This ensures every customer gets routed to the person best suited for the job.  

Uncover customer insights with Smith.ai

Whether you’re utilizing these insights to convert leads, nurture existing customer relationships, or make strides in new product development, conversation analytics can uncover the data you need to make it happen. 

Smith.ai makes it easy to stay on top of customer insights. Our call intelligence and analytics feature provides real-time historical call data that you can use to make better business decisions related to your sales, marketing, and customer service efforts. 

Book a free consultation to learn more about how Smith.ai can provide you with meaningful customer data.