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6 Contact Center Conversation Analytics Best Practices

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  • Utilizing conversation analytics is growing in popularity because of the enormous impact data can make on one’s business.

    Since contact centers are focused on conversations, they are an ideal environment to leverage conversation analytics.

    What is Conversation Analytics?

    Conversations involve two or more people talking and interacting with one another. At the most basic level, conversational analysis is the study of talk. But on a broader level, it is the study of people talking together, oral communication, and the study of natural language use. 

    Conversation analytics stems from natural language processing, which is a field that combines linguistics, computer science, and artificial intelligence. Behind conversational analysis is the idea that conversations are structurally organized, have order, and help share understanding. 

    Therefore, conversation analysis approaches these everyday interactions using systematic analysis.

    Conversation analysis aims to understand the hidden rules, meanings, and structures that create order in a conversation. Conversation analytics is simply the data that comes from that analysis.

    Next-level analysis makes use of artificial intelligence. In a call center setting, this powerful capability may be used to study human speech and text-based interactions in a much more efficient and accurate way. The technology uses advances in natural language understanding and predictive analytics to pull insights from conversations that benefit the organization. 

    The Benefits of Conversation Analytics

    There are several benefits to implementing call center speech analytics capabilities, but they all center around improving the customer experience. Conversation analytics can open the door to anticipating customer needs and emotions. It can help create personalized solutions that enhance customer service. 

    Real-time analytics can help gauge customer frustrations so that agents can respond in a way that de-escalates tense situations. Responding in this manner can be accomplished by understanding emotion, which may change throughout an interaction. And this type of data also holds training value since it can help call center employees improve responses. 

    Conversational analytics can also be used to predict behavior in customer interactions. The combination of artificial intelligence and machine learning technologies enables this function. By examining past interactions, data can be used to predict how a current call may end, leading to better customer service and greater satisfaction. 

    Call Center Conversation Analytics Best Practices

    To truly leverage the full capabilities of conversational analytics, you must understand how to implement these systems effectively. The following tips can help you achieve greater accuracy and a more significant return on investment. 

    1. Analyze all customer interactions

    If possible, analyze as many customer interactions as you can. 

    While you must warn customers that the interaction may be reviewed or recorded, analysis is fair game if they consent. And the more interactions you can analyze, the greater the data will be.

    To truly understand how your customers interact with your agents, you need a wide swath of interactions to examine. It provides more natural language processing software opportunities to understand and contextualize the spoken or typed information.

    To get meaningful insights, you need to analyze all types of customer calls and interactions. For instance, the emotions tied to a financial services inquiry are often different from those of unsatisfied customers. This variance in data from unsatisfied and satisfied customers is determined based on the 4 Vs of conversational analytics – volume, variety, veracity, and velocity. 

    1. Don’t forget the end goal

    Remember that the goal here is not to implement speech analytics tech. Instead, it is to use the insights from this tech to improve customer service.

    It is very easy to deploy these capabilities and channel resources – like time and energy – into improving the analytical products. But the focus must be generating tangible business benefits. The tech becomes a financial drain on your company if you can’t do that.

    1. Involve your entire team 

    Contact center analytics exist to enhance customer service processes. However, these analytics don’t exist in a vacuum. It’s a great idea to include customer service agents and managers in the process as you identify ways to incorporate the data and insights into your customer interactions. Getting their input and incorporating it into your evolving customer service delivery is crucial in making the most of your analytics. 

    1. Identify key performance indicators

    Achieving a goal requires measuring progress, so identify the goals you want to achieve and develop an action plan to achieve them. Take time to understand how other business KPIs can be interpreted based on customer interactions. 

    1. Check-in on customer service

    While internal goals are essential for success, getting external validation is also important. 

    Ask customers how they feel about the customer interactions or working with the agent or voice assistant. You don’t have to go into great depth and ask highly technical questions about natural language processing or artificial intelligence. But you can ask about general satisfaction. And getting a response on that is one way to help you understand how well you are doing. 

    1. Coordinate with other departments

    In recent years, we all have learned that technology does not work best when siloed. At a minimum, one can get more out of their technology stack when tech is working together.

    Significant customer service insights can come from conversation analytics. But, you aren’t using the data to its full potential unless you share this information with other departments. For example – sales, product, and compliance departments can benefit from understanding data collected by all three departments. 

    Sharing data leads to better interdepartmental coordination. It is also another way to enhance the overall customer experience. 

    The Future of Contact Center Conversation Analytics

    There can be no doubt that conversation analytics is here to stay. As artificial intelligence, machine learning, and natural language processing capabilities grow, conversational analysis and the associated analytics are sure to become more common.

    Conversation analytics give you the perfect opportunity to learn more about your customers; you just have to listen to the conversation data. Proper analysis of conversation analytics can lead to reduced customer churn, customer satisfaction, improved agent performance, and even training programs improvements. 

    Organizations looking to improve agency performance and responses need to continue to strive for better ways to leverage customer insights.  Level AI can help.

    Level AI Conversation Analytics provides you with a unified view of everything that happens in your contact center and helps you slice and dice your data the way you want. Contact us to schedule a demo.

    Get a free demo today!

    Your customers will thank you for it!