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Conversation Analytics: What It Is and Why It Matters

Conversation analytics uses data from customer conversations to understand their sentiment, intent, and interests.
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What is conversation analytics?

Conversation analytics refers to the use of artificial intelligence (AI)—specifically natural language processing (NLP), to analyze customer conversations and use them to understand their sentiment, intent, and interests.

You can use it to identify opportunities for improvement so that you can enhance your overall customer experience. We can apply the technology to many industries and situations, but it's especially effective when paired with other forms of data collection like surveys or phone calls. In turn, it can help you measure the effectiveness of your marketing campaigns, social media strategies, and customer service.

A recent report by Grandview research found that the global conversation AI market will reach $41.39 billion by 2030—because of rising demand, reduced chatbot development costs, AI-powered customer support services, and omnichannel deployment. This finding only indicates how valuable it is when you need insights into how customers feel about the product or service they're receiving and how they feel about their experience interacting with your brand online, i.e., customer conversation analytics.

In this article, we'll explain how conversational analytics works and how you can leverage it for your business—and improve your overall customer experience.

How does conversational analytics work?

Typically, conversation analytics tools can identify trends and opportunities based on the information you provide in the form of chat transcripts, voice or video call transcripts, and other forms of customer data. It analyzes this data and provides reports that help improve your customer experience by identifying problems and potential customer behaviors you may not be aware of. 

For example, the customer may have chatted with your chatbot because their insurance plan has changed, and they're not sure what that  entails. Over time, companies will develop a large dataset containing all these stories from different customers about the same intent or behavior. You can use this in two ways—one to predict future customers' questions and the other, what concerns customers have with specific insurance plans.

Artificial intelligence helps the natural language processing model learn from past experiences to predict outcomes accurately based on new data input. In addition, the model eventually identifies positive or negative feelings from the written text by analyzing the tone and sentiment of the language. We can also train the model to extract specific keywords and match them to one particular intent.

It slowly makes associations between these words—enabling it to decode the sentiment and context behind these conversations—and predict outcomes more accurately. It can make these predictions by breaking down the conversation into fragments called vectors. The more customer data you feed it, the more it learns, and the better the output will be. By continually improving the model, it will unlock better customer experiences and higher satisfaction. 

Benefits of conversation analytics

  1. Understand the nuances of customer conversations

Conversation analytics is a new way of understanding the nuances of customer conversations. It combines the power of multiple features like sentiment analysis, key chatbot metrics, and more to offer a holistic overview. It's about looking at the entire context of an interaction, including the topics discussed, tone, language used, and emotion expressed.

For example, when analyzing conversations from two customer calls, we can see that one person is having trouble with something they ordered and went back and forth while the other customer just returned the product. By analyzing the conversation, it shows us that this particular product may be defective or have some other issue that needs to be addressed immediately.

  1. Predict customer behavior

The idea is simple: if you know what customers say about you, you can use that data to improve your business.

Also, it's not just about what customers say but how they say it. The goal of conversational analytics is to gain insights into what consumers want and need—before they even know it themselves—so you can offer them products or services that meet their needs. It helps you improve how your company responds to customer service requests and support tickets—transforming the customers' experience.

  1. Make data-driven business decisions

Conversation analytics tools can help companies make better decisions about allocating resources, optimizing processes, and adapting their business models over time. You can:

  • Identify your most influential customers over a specific period
  • Monitor customer satisfaction and loyalty over time
  • Track trends in social media conversations about your brand or product
  • Find out what types of content resonate best with your audience
  • Use social media monitoring tools to discover new leads

Conversational analytics lets you see which customers are likely to churn so that you can take action before they leave your business or service. You can make data-driven decisions and implement strategies to alleviate critical issues by measuring these metrics.

  1. Reduce your internal workload

Your employees are already pressed for time, so burdening them with the task of manually going through hundreds of chatbot conversations is often a waste of resources. With conversational analytics, you can identify common questions or requests outside of standard procedure and create automated responses that your employees can use instead of repeatedly answering the same question.

This process allows your employees to focus on high-value tasks that drive growth or productivity. For example, many teams waste time sorting through multiple data sources across different channels and blending them to create a view of their customer's journey. Instead, by using the right conversational tools, your team can automate the undifferentiated heavy-lifting of data work and spend their time focusing on identifying new use cases and improvements to their conversational channels/chatbots.

Use cases of conversational analytics

Understanding intent

Customer questions can be complex, making it harder to understand the intent, especially when you offer multiple products. For example, insurance companies usually provide various products (home, car, or boat insurance), and conversation analytics can help understand these complex intents (questions). When you process this information via a conversational data cloud—it becomes a lot easier to understand the intent behind popular customer queries.

It also helps mitigate the need for live agents by identifying routine questions customers ask, such as policy details, policy numbers, policy expiration dates, policy prices, account questions, etc. Using this data, you can optimize your chatbot's conversation flow by improving critical metrics like fallback rate, human escalation rate, and increasing customer engagement.

Integrating with traditional analytics tools

Traditional analytics tools are not meant to ingest and analyze conversational data (which is unstructured by nature)—making it hard to categorize and comprehend. They don't have enough context, which prevents you from understanding the bottlenecks that led to specific issues in the first place.

To resolve this, you must measure the bots' conversation/user journeys across various paths. For example, using tools like Power BI to generate data visualizations that go beyond the numbers and understand the user intent is difficult. You can only do this with technologies like sentiment analysis, as you need more sophisticated tools to uncover such insights.

Tools like Dashbot’s Report module can measure the health of your chatbot based on specific metrics and create customizable dashboards that provide an in-depth consolidated overview of the ground reality.

How Intuit reduced its human escalation rate by 57%

In 2017, when Intuit QuickBooks introduced its conversational chat experience, it wanted to reduce its human escalation rate. But at the time, they were dealing with a massive amount of unstructured data that would take weeks to analyze. As their customers were unsatisfied with mishandled or unhandled responses, this had to be done as quickly as possible.

To circumvent this issue, they turned to Dashbot’s Conversational Data Cloud, which gave them an in-depth insight into every customer conversation they had. Dashbot could generate full transcripts for each conversation and map the conversational paths. Based on that information, they optimized their chatbot’s conversation flow, reducing their fallback rate by 35% and human escalation rate by 57%.

“Our ultimate goal was speed-to-benefit. When someone wants to know how to do something or needs information, it should be faster than even the fastest human help.” — Scott Ganz, Principal Content Designer
The image shows that Intuit Quickbooks experienced a 35.3% reduction in not handled rate and 57% reduction in human escalation. Additionally, they derived this data from over 1 million sessions a month and over 50 chatbots.
Impact numbers from Intuit QuickBooks’ experience with Dashbot

Analyze your customer conversations using Dashbot

Conversation analytics is a powerful tool for companies who want to improve the customer experience. It can help you identify areas of improvement to enhance your business, so it’s worth taking some time to learn more about it.

Through your chatbot's conversational transcripts, you can access an invaluable stream of information for you to use. When you use your customers' words to understand them better and quickly resolve their issues, you can master your chatbot's performance and personalize it to match your customers' needs.

If you’re looking for conversational analytics software to help you uncover such insights, book a demo with Dashbot today.

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