The definitive guide to conversation intelligence: Definitions, benefits and examples

Conversation intelligence, sometimes called conversational intelligence, represents an increasingly important strategy in the quest to deliver effortless customer experiences.

At a foundational level, conversation intelligence is the process of collecting customer feedback and conversation data from all customer support channels and integrating it with first- and third-party data to drive deeper customer insights using proprietary algorithms and generative AI. The goal behind conversation intelligence is to establish an always-on predictive analytics engine that can solve critical contact center desired outcomes such as lowering costs, improving customer outcomes, reducing friction throughout the customer journey, and even identifying key product or service issues before they can damage brand reputation.

Why is conversation intelligence important?

Customers in 2024 have high, and steadily rising, expectations. Increasingly, these expectations are for effortless experiences – like fast resolutions, straightforward conversations, and minimal channel jumping. In fact, the best customer outcome would be to not have to reach out to your support team at all.

For organizations that fail to deliver on these experience mandates, the outcomes can be costly. According to a 2021 survey by Qualtrics, nearly 80% of customers switched brands due to poor customer experiences. Even worse, nearly 45% of customers did so after just a single bad experience.

To compete in the era of high customer expectations, organizations are tasked with reducing friction across the customer journey, but often they don’t know where friction currently exists. Conversation intelligence provides customer support teams with the always-on experience monitoring they need to identify key topics, complaints, and sentiments as they emerge.

Here are a few categories of common customer friction and how conversation intelligence can help address them:

Product and service issues

The single best thing an organization can do for its customer retention is to not have any issues at all. While eliminating all product issues or service disruptions is not usually possible, conversation intelligence can help identify and triage these issues faster than traditional call monitoring tools – which can reduce the number of people affected by the issue and how long they are affected by it. In some cases, conversation intelligence might even unearth an opportunity for a more proactive strategy to save customers from having to call into the support line in the first place.

Multiple conversations before resolution

For years, first contact resolution (FCR) has been a vital metric to measure contact center success. While this is still a valuable indicator, many brands are starting to also think about the trail of conversations that might take place along a typical customer journey and are choosing to focus on next-issue avoidance instead. For example, if a customer buys a computer, they might call in for assistance logging in for the first time. But even if the agent successfully helps the customer to log in, there are likely to be more issues later like setting up an email account or downloading an important piece of software, and so on. Conversation intelligence can help organizations understand the customer journeys that come after a single conversation – setting up opportunities to address the long-tail challenges that might come with a particular journey.

Required channel switches

In the big push to automate as many aspects of the customer experience as possible, many brands turned to chatbots as the first line of defense. This can work great when these chatbots are equipped to solve many of the most common problems. But for customers who have to explain the issue to their chatbot and then re-explain the issue to a live agent, this friction can be incredibly frustrating. Conversation intelligence can help inform more personalized approaches to issue resolution by inspiring entirely new journeys for many customer problems, while directing customers with noted complex issues to start with a live agent from the very beginning.

How contact center managers can use conversation intelligence in the contact center

We’ve addressed a few limited use cases already, but the business opportunities for conversation intelligence are plentiful. To help demonstrate the full impact of conversation intelligence, let’s group its outcomes into three main categories: identification, integration, and enablement.

Problem identification

First and foremost, conversation intelligence serves as a diagnostic tool – always searching for potential topics, sentiments, and issues that are trending. Through established analytics dashboards or custom search tools, conversation intelligence empowers contact center leaders with the ability to track macro-level trends in the contact center like never before. Within identification, conversation intelligence solutions can help monitor:

• Operational issues
• Process issues
• Product issues
• Customer problems

Feedback integration

Additionally, conversation intelligence captures customer sentiment at an aggregate level. Powered by generative AI, algorithms can measure customer sentiment across all channels, from both established customer feedback tools and raw conversation data. This data becomes a powerful indicator to help with issue identification and generally keep a pulse on customer satisfaction.

Strategy enablement

Because conversation intelligence connects customer conversation data to other critical customer data points, it also enables organizations to learn more about the events both before and after an interaction. This can inform new training strategies and journey enhancements that might not have been visible otherwise. Conversation intelligence can enable things like:

• Strategic decision making
• Product enhancements
• Product innovations
• Targeted coaching and training

Which customer data should you use for conversation intelligence?

When it comes to establishing a conversation intelligence solution, the more quality data the better. The modern customer experience takes place across a multitude of channels, which means building a predictive analytics strategy around just one channel – say voice, for example – produces an incomplete picture of the customer experience and is unlikely to deliver meaningful insights.

Think about your customer data landscape like a dart board. At the bullseye are the actual conversations you’re having with your customers. These conversations might be documented in:

• Chatbot transcripts
• Call recordings
• Social media interactions
• Digital messages
• And other channels

While these moments are the ones where your brand will directly engage with a customer, they aren’t the only aspect of the customer experience you should be aiming for. Focusing just on these direct engagements at the bullseye means the insights your conversation intelligence solution generates will mostly be reactive ones.

However, by aiming for other areas of the dartboard, you can begin to unearth new ways to score points with customers and meet your operational objectives – some of which can generate outcomes that surpass the ones you would get by focusing on the bullseye alone. Arming your conversation intelligence solution with additional data sources, like transaction data and marketing touches, can help predict specific problems, situations, or circumstances you can proactively address for your customers before they reach out.

How conversation intelligence works

To successfully set up a conversation intelligence solution, an organization will need access to data and analytics expertise – either in-house or through consultant teams. After the initial goals and customer service needs are decided, here is how the raw data transforms into powerful data-driven insights:

Step #1: Collect the data

In this first step, the analytics team will pull together all transcripts across chat, email, surveys, call details, and other metadata. The most important factor in this preliminary step is that the right data sources are pulled to maximize both conversation insights and the appropriate context to activate them.

Step #2: Categorize the data

Once the appropriate data sources have been ingested, data engineers categorize the raw data across a variety of factors including topic, extraction entity, sentiment, complexity, and more.

Step #3: Activate the insights

Finally, this sorted data flows through personalized dashboards for agents, managers, and business leaders to enable organizational action. These dashboards can include a variety of features such as call summaries, dynamic insights, granular topic identification, text-search functions, call topic filters, natural language question and answer, and user interface drill downs.

Examples of conversation intelligence applications

Let’s take a look at four ways conversation intelligence can be used to enhance customer and business outcomes.

1. Identify key topics that lack visibility. By deploying generative AI to parse incoming conversation data, contact centers can automate the detection – and in some cases alleviation – of important friction points that may go undetected today.
2. Establish agent training protocols for complex topics. Where highly complex topics are identified, managers can organize training opportunities to support agents and simplify challenging conversations. Similarly, where topics tend to become emotional, managers can build routing strategies that forward these calls to agents who are most successful in these high-stakes situations.
3. Track changes in topic, sentiment, and complexity by stakeholder group. This example might be most common in traditional retail environments where a service, product, or solution might be owned by a product team. By monitoring product groupings and creating dashboards and notifications for their respective teams, contact centers can create a feedback loop that quickly alerts product managers to concerning changes in customer feedback so they can investigate.
4. Determine if and when agents use quality assurance phrases. Contact center managers can automate quality assurance and measure the impact of new protocols on both customer behaviors and outcomes.

Exploring one practical use case for conversation intelligence

Let’s say a direct-to-consumer electronics brand noticed a significant increase in call volume and call duration over a specific week, but they’re not sure why. Since they have a conversation intelligence solution in place, they can search for trending customer topics, as well as the complexity and sentiment of these conversations.

In this case, a contact center manager notices many of these calls include references to overheating laptop batteries. Since these conversations appear to be complex, it could be worthwhile to identify a knowledge article strategy to better equip agents to respond to these problems efficiently. Better yet, it could trigger a notification to the product team, who can work quickly to address the specific cause of the issue and prevent a widespread product recall.

Why TTEC Digital is a strong partner for conversation intelligence

In a consumer environment where experience friction leads to customer attrition, TTEC Digital delivers powerful customer experience solutions at the point of conversation. By combining deep CX consulting expertise with decades of experience innovating on the leading contact center platforms, TTEC Digital is uniquely qualified to deliver custom data and analytics strategies that drive agent productivity, customer satisfaction, and real business value.

Our data and analytics team offers an expansive knowledge of artificial intelligence and can provide a proven roadmap for implementation that supports contact center teams with all different levels of AI familiarity. No matter your objectives, our experts will help you identify how prepared your company is to leverage AI, gain a foundational understanding of AI, build an action plan with our AI roadmap, and help guide you along the way.