There’s a wealth of insights buried in your customer conversations–but extracting those insights from thousands (or millions) of call recordings, chat transcripts, and emails is no small feat. That’s where conversation analytics come into play.
Conversation analytics is the science of analyzing your customer conversation data through the power of machine learning. That’s the short answer, but there’s a lot more to unpack.
Below, we’ll explore how conversation analytics works, its benefits, and how companies are using it to reduce customer churn, drive sales, and reduce costs in their contact centers.
What is conversation analytics?
Your company’s frontline representatives talk to customers to make sales, deliver products or services, and provide assistance. Those conversations take place over the phone, in email, or in chat format, and together form your customer conversation data. Taken on its own, this raw data lacks meaning–it doesn’t become valuable to your business until it’s analyzed and interpreted.
Conversation analytics uses technology to organize and translate that data into information your company can act on. Customer experience leaders might look for common call reasons to uncover opportunities for more self-service options for customers, contact center managers might review common sources of agent confusion to improve their coaching, and sales leaders might look at what rebuttals are most effective in response to common objectives (just to provide a few examples).
How does conversation analytics work?
Conversation analytics relies on machine learning and natural language processing (NLP).
Programmers create machine learning models that ingest a large amount of data for training–in other words, the model uses the data to find patterns or make predictions. The more data the model ingests, the more it “learns” or adapts (people can also make manual tweaks to the model to improve its accuracy).
NLP uses computational linguistics and machine learning to turn spoken or written words into data. A model using NLP can then make sense of that data and use it to complete tasks such as analyzing sentiment in a written statement or generating text summaries (ChatGPT is probably the best-known example).
In conversation analytics, a model processes phrases in customer conversations to determine their meaning, allowing businesses to uncover actionable insights without having to assign employees to review thousands of phone calls or chat transcripts. For example, our platform, Creovai, applies relevant category labels to both customer and agent phrases so businesses can identify trends in difficult calls, causes of churn risk, areas where agents need coaching, and more.
What are the benefits of conversation analytics?
Conversation analytics plays a crucial role in understanding your customers, improving the performance of your frontline representatives, and streamlining your operations.
Uncovering new customer insights
Traditionally, customer service departments have relied on feedback surveys and quality assurance (QA) evaluations to measure customer sentiment and agent performance. But survey response rates are typically low (and skew towards people who either had a very positive or very negative experience), and QA teams can only manually review a small percentage of call recordings or chat transcripts.
Conversation analytics unlocks the insights across all your customer conversations, giving you the information you need to improve your service, products, or overall customer experience.
Improving agent performance
To help your sales or customer service agents be as successful as possible, you need to provide them with specific, data-backed guidance. Conversation analytics lets you track agent performance across every call or chat so that you can see what they’re doing well and what they need additional coaching on. Providing your agents with this level of feedback improves their confidence and performance, allowing them to better assist your customers.
Improving operational efficiency
Conversation analytics can surface information to help your department–or entire business–run more smoothly. For instance, tracking the most common reasons for repeat calls may help you identify opportunities to improve self-service options–or train agents on proactive steps they can take to reduce the likelihood of a customer calling back about the same issue.
Improving operational efficiency can help you reduce your contact center’s average handle time (AHT), improve your first call resolution rate, and reduce the need to hire additional agents–all of which contribute to significant cost savings.
Use cases for conversation analytics [with real-world examples]
The insights uncovered by conversation analytics can have a broad range of applications, including:
- Coaching sales or customer service representatives
- Automating quality assurance (QA)
- Identifying and addressing common points of friction for customers
- Optimizing chatbot performance
- Identifying the best ways to deflect customer service calls
Ultimately, all the ways businesses use conversation analytics tie into three core goals:
- Reducing customer churn
- Reducing costs
- Increasing sales
Reducing churn
We all want our customers to stick around. One of the great benefits of a conversation analytics platform is that it allows you to see when–and why–your customers are at risk to churn. With insight into the full range of the customer’s experience with your company, you can see trends in customer churn–and use that information to address the problem.
When you eliminate sources of friction, your customers are far less likely to leave. They may even be inclined to repurchase and recommend you to their friends.
Example:
Connexus, a credit union with over 430,000 members, implemented Creovai to gain insights into points of friction and the scope of issues their members were calling in about. By uploading their contact center call recordings to Creovai, they can measure dozens of variables related to both customer and agent behavior, giving them actionable insights about the overall member experience. For instance, after identifying a trend in agents using phrases such as “I’m not sure, hold on” as a stall tactic when accessing member information, they were able to coach consultants to use advocacy-focused language (e.g., “Let me access your account so I can tell you”) instead.
Within one month of implementing Creovai, Connexus saw a 41.7% increase in agents using advocacy-focused language, one of the largest factors in positive customer experiences. By improving their member experience, Connexus is reducing the risk of members churning due to negative service interactions.
Reducing costs
There’s a real cost to every customer service interaction. With an average cost per call of $2.70-$5.60 (or higher, depending on the industry), it’s no wonder that reducing average handle time and improving the first call resolution rate are top priorities in every contact center.
A conversation analytics platform allows you to see what agent behaviors, points of friction, or customer issues are leading to longer handle times or repeat calls. From there, you can identify changes (e.g., agent coaching on common issues, improvements to self-service options) to reduce your AHT and deflect unnecessary calls.
Example:
A leading telecom service provider used Creovai to track and measure agent behavior so they can provide a seamless customer experience. They used the insights Creovai surfaces to hold agents accountable and identify coaching opportunities to improve their call quality. They also ran a monthly challenge that focused on one key area of agent behavior and award a prize to the agent who has the most occurrences of that behavior on their calls.
Using conversation analytics, this company provides concrete feedback to agents and offers targeted coaching. This has helped them achieve a 35% decrease in escalated calls and a 28% decrease in repeat calls, leading to an 11% reduction in their contact center’s operating costs.
Increasing sales
A conversation analytics platform allows you to gain sales conversation insights, such as the most common objections and the most successful rebuttals. With that information, you can make the changes you need to increase your sales conversion rate. Analyze the top performers, identify the most successful pitches, and then have all your agents learn from them to scientifically improve your close rate.
When we ran the data, we found that if you implement behaviors observed in top-performing sales reps in all of your calls, your close rate for inbound sales jumps to 70-75%.
Example:
Lawn care company TruGreen uses Creovai to analyze their sales conversations and find answers to key questions: why people are calling, why customers are canceling or signing up for service, and why some agents are successful while others are not. They leverage this information to provide targeted training to their sales reps, create better marketing offers, and build stronger scripts to increase their close rate. As a result, they have seen a 10% improvement in call conversions and a 7% increase in sales retention rates–a record high.
Final takeaways
Your conversation data is incredibly valuable–but only if you’re able to organize it, glean insights from it, and take action. Conversation analytics allows you to make sense of that data and use it to make informed decisions, improve agent performance, and provide a better customer experience. And those actions have the power to increase customer loyalty, improve operations, and grow your business’s revenue.
