What To Expect When Setting Up Topic Analysis

Imagine you could listen to all your call recordings and pick out the ones in which customers mention the new marketing campaign. Or those calls in which customers demand a refund because their perishable dog food was delivered a day late again and is now spoiled. Or those calls where your agents don't read the compliance statement. Or..., okay, you get the point.

Imagine being able to identify, categorize, and analyze calls based on a list of keywords or key phrases. That's the power of Topic Analysis. Where would you start? At MiaRec, we have worked with hundreds of large contact centers around the world, and we know this can be intimidating.

In this article, we will share what you should expect when setting up your topic analysis features, what common fears and misconceptions contact center managers have regarding topic analysis, and best practices to keep in mind to set yourself and your team up for success.

Understanding Topic Analysis

At its core, topic analysis is a method for categorizing conversations based on what's discussed during the conversation. It's like having a magnifying glass over every call, pinpointing the subjects that matter most to your customers. But why is this so crucial for modern contact centers?

The answer lies in the sheer volume of data. With thousands of calls pouring in daily, contact center managers need a way to sift through the noise, identify patterns, and understand customer sentiments. Topic analysis provides this lens, offering a structured way to analyze and interpret conversations.

MiaRec's Topic Analysis is powered by Large Language Models (LLM) and Generative AI, enabling it to automatically identify and extract key topics from customer conversations or call recordings with remarkable accuracy. This advanced technology goes beyond traditional keyword-based methods, allowing contact centers to gain deeper insights, uncover hidden trends, and respond to customer needs more effectively than ever before.

Screenshot of MiaRec interface, featuring the Topic Analysis of a customer call.

Using LLM-based Topic Analysis allows you to move beyond predefined keywords and instead provide AI with natural language descriptions of the topics you want to identify. Rather than specifying exact phrases, you simply describe the types of interactions or customer sentiments you're interested in. The AI then understands and identifies these themes across your call transcripts. For example, you can describe scenarios where a customer is frustrated, and the AI will automatically detect and categorize any related conversations, even if the exact words you mentioned aren’t used. This method enables a more flexible and accurate understanding of customer interactions, adapting to the natural flow of conversation.

Here is a 3-minute walk-through of how to set it up:

The Sky Is the Limit with Topic Analysis: Where to Start

Topic Analysis is a very powerful tool because it allows you to identify all kinds of things across your entire call volume. But where do you start? Some contact center managers get 'blank canvas syndrome' or decision paralysis as they try to identify which topics they want to track. Below are just a few examples of what you can do with Topic Analysis, but the sky is the limit when it comes to your creativity.

For example, you can better train your team and improve agent performance by:

• Identifying potential training gaps or additional training needs
• Surfacing agent insecurities, lack of knowledge, and other detracting performance indicators
• Measuring the impact agent training is having on customer happiness
• Tracking agent performance over time
• Developing better call scripts
• Monitoring agent adherence to call scripts
• Enforcing compliance rules (e.g., announcing that the call is being recorded)

Or you can make more informed business decisions by using Topic Analysis to:

• Analyze sales and product trends
• Identify reasons for a sudden increase in cancellations
• Get to the bottom of return requests
• Track reactions to a marketing campaign
• Identify and proactively address potential issues before they become bigger problems

And even improve customer satisfaction, loyalty, and retention by:

• Detecting customer sentiment associated with particular topics
• Monitoring reactions to pricing changes or new product introductions
• Measuring customer loyalty and engagement levels, helping build more effective loyalty programs
• Predicting and improving customer retention by identifying disappointed customers early
• Improving your overall customer experience by better understanding what makes a call successful and what derails it

In addition to extensive and customized onboarding and training, you will have access to dozens of commonly used topics already included within MiaRec. You will also have plenty of resources available, such as comprehensive and up-to-date documentation and tutorials that can help in setting up expressions faster and more efficiently.

Screenshot of MiaRec application section showing sentiment score, the associated topics, and key phrases.

Best Practices for Implementing Topic Analysis

While implementing Topic Analysis is very straightforward, we have found that there are five common-sense best practices that ensure success:

1. Start with a few topics and expand gradually

You might think to yourself, "that's obvious," but it is very tempting to try dozens of scenarios once you have implemented the tool and see how easy and powerful it is. Nevertheless, we recommend that you start slowly by identifying a few topics to start with.

Our most successful customers start with topics that have exact wording to listen for, e.g., script adherence and compliance. Keep fine-tuning those for a couple of weeks and then slowly expand from there.

2. Manually review call data before using Topics

Even before detailed Topic Analysis, most contact centers have some data or insights into common issues based on call records, notes from agents, or even customer feedback forms. You can use this existing data to identify potential high-volume topics.

3. Consider industry-specific Topics

Each industry and niche has unique considerations that will have to be taken into account to ensure that businesses can gain maximum insight into their customers' experiences and improve overall service quality along the way. For example, as a retail business, you might get a lot of "Refund Request" calls, while an insurance business will receive a lot of calls to process a "Policy Renewal."

4. Have a dedicated person to manage Topics

It's crucial to regularly update and refine your topics to stay aligned with evolving industry trends and shifts in customer expectations or behaviors. As conversations evolve, the way customers and agents express themselves can vary significantly.

With LLM-based Topic Analysis, a dedicated resource is essential to continuously monitor and adjust the AI's understanding of these interactions, ensuring that the system accurately captures the most relevant insights. This role is key to maximizing the ROI from your Topic Analysis by keeping it dynamic and responsive to real-world changes.

5. Don't operate in a silo

While it might make sense to start implementing Topic Analysis for internal topics, such as agent performance and customer experiences, it is crucial to share insights across different business units in the organization. The companies that see the biggest impact of implementing Topic Analytics have gradually expanded their use cases across the entire organization.

By keeping these best practices in mind, businesses can make sure that their Topic Analysis is designed and implemented in the most effective way to gain maximum insight into customer experience.

Addressing Common Concerns

Contact center managers considering utilizing Topic Analytics for the first time usually share some common concerns about the set-up:

How much manual labor is required when setting up and configuring Topic Analysis?

Setting up and configuring topics has become much simpler with LLM-based Topic Analysis. Instead of manually identifying and entering specific keywords, you only need to provide descriptions for each topic, allowing the AI to understand and categorize conversations accurately. This drastically reduces the time and effort involved, even for larger companies with complex data needs. MiaRec offers a range of pre-configured, commonly used topics to get you started quickly. Additionally, we will guide you through setting up any additional topics you need and train your Voice Analytics admin to easily manage and update these topics in the future.

Can you only set up one topic per call?

While some contact center managers worry that only one topic can be assigned per call, this isn’t the case. With MiaRec's LLM-based Topic Analysis, multiple topics can be automatically assigned to a single call based on the AI's understanding of the conversation. This allows you to capture different aspects of the interaction, such as customer sentiment, potential training gaps, and the impact of agent training on customer satisfaction.

The key takeaway is that, although setting up Topic Analytics might seem daunting at first, it’s now as simple as providing descriptions of what you want the AI to detect, making the process intuitive and efficient.

Conclusion

Implementing Topic Analysis allows organizations to gain invaluable insights into customer experiences and, despite the initial impression that it may be overwhelming to set up and maintain, it is actually surprisingly easy and fast to set up. MiaRec provides comprehensive onboarding and customized training, helpful resources and guidance to ensure you get the most out of your Topic Analysis journey. More importantly, MiaRec customers always have peace of mind knowing that we are there to help if they ever get stuck.