How to Reduce Customer Inquiries and Improve Satisfaction

A positive experience with your customer service has a significant impact on customer loyalty and overall business success. Many companies understand the importance of excellent service, yet contact centers are often under pressure. Long wait times, dissatisfied customers, and high employee turnover are common challenges that many managers face. In this blog, we’ll explain how Conversational Analytics can provide insights to help you systematically reduce customer contact and improve the overall customer experience.

The contact center plays a crucial role in daily operations and in driving the commercial success of the organization. It’s often the first point of contact for customers, which means expectations are high, but in practice, they’re not always met. There can be several reasons for this:

• High employee turnover due to lack of challenge and dissatisfaction with workload. The tight labor market also makes it difficult to find suitable replacements who can quickly make an impact.
• Service budgets are often planned with rising labor costs in mind, but rarely account for the impact of business growth on the contact center, increasing pressure over time.
• Investments in digitalization and automation may only deliver limited returns because the technology doesn’t directly solve customers’ key pain points.
• Due to the high workload, service employees focus on firefighting rather than recording customer interactions, making it hard to track when and why customers are reaching out, and you miss out on valuable information to implement improvements.
• Performance evaluations are often based on random conversation samples, which can give a skewed view and negatively affect morale. There’s often no alternative because companies don’t have the time or resources to monitor performance over extended periods.

In-Depth Knowledge for Prevention and Improvement

To tackle these challenges, it’s wise to first reduce the workload. This can be done by preventing avoidable customer contact. Think of repeated emails and phone calls on the same topics.

Example:

A municipal waste management company receives dozens of calls each week from residents wanting to apply for a new environmental pass. When customer service directs them to the website, residents complain that the form isn’t working. When this process runs smoothly, it significantly reduces the number of calls.

Questions like these take up time and resources without contributing to a better customer experience. How can you prevent this? By understanding the actions you can take to avoid such contact. For this, you need in-depth knowledge of your customer conversations. This knowledge helps you detect common themes in specific areas of the customer journey.

Once you’ve reduced unnecessary traffic, you can focus on improving the conversations that you do want, those that drive customer loyalty. Again, this requires deep insights, which you can gain through Conversational Analytics.

What is Conversational Analytics?

Conversational Analytics (CA) helps you identify patterns and trends in customer conversations that would otherwise go unnoticed—without having to manually log data. CA gathers data from phone, app, and chat conversations, emails, and other voice and text interactions. This data is then analyzed using AI (specifically, machine learning), resulting in actionable insights at the individual, team, and organizational levels.

Why is this important for customer service? It gives you a clearer understanding of customer pain points and sentiment. What topics do customers frequently contact you about? Why do these topics keep coming up? When do we see these themes reoccurring? What kind of sentiment surrounds these issues? Every conversation helps you pinpoint areas for improvement, reducing traffic to your contact center while delivering constructive feedback on the performance of your team.

Example:

An airline applied Conversational Analytics to understand why customers were contacting their customer service. They quickly uncovered a key insight:

The airline has an online portal where customers can manage their bookings, yet they continued to call the contact center. Through Conversational Analytics, the airline identified the core issue: customers often reported that their booking numbers were missing. Without this number, they couldn’t access the portal. The solution was simple—add the booking number to the confirmation email. Additionally, the chatbot was trained to recognize booking number inquiries and direct customers to check their bank statements.

By analyzing conversations, sentiment, and behavior, the airline uncovered trends in customer contact that were previously hidden. These insights helped them prioritize and address common issues, significantly reducing pressure on their contact center.

How Does Conversational Analytics Work?

You don’t need to have in-depth knowledge of the underlying technology to use Conversational Analytics, but it helps to understand the basics. CA uses natural language understanding (NLU) to interpret and translate data accurately. NLU is a form of AI and a subfield of natural language processing, familiar from tools like ChatGPT. Chatbots, for example, also use NLU.

Like any other AI model, Conversational Analytics needs to be trained. This is a step-by-step process where you collect data, and the model learns to label the data with one or more tags. For example, you could create a model to recognize conversations about vouchers by uploading past conversations about vouchers and labeling key elements. Or you could develop a model to monitor the performance of your contact center, such as detecting whether an agent ended the call by confirming the customer’s question was answered.

Where Should You Start?

The possibilities are vast, but the question is, where do you begin? Fortunately, you can quickly implement Conversational Analytics in your organization. With just one source of conversational data, you can get started. Plus, there are many ready-to-use models that you can apply and gradually expand with custom-built ones. The time-to-value for CA is minimal, meaning you can start reaping the benefits almost immediately.

Want to learn more about Conversational Analytics? Curious about what it can do for your organization? Download the e-book or request a demo today.