Contact Center: Enhancing Customer Support through Language Analytics

Contributing Author
8 min read
Jan 22, 2024
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Research shows that 60% of customers change brands because of poor contact center experience. That’s why your contact center performance needs to be impeccable if you want to surpass customer expectations and inspire customer loyalty. 

Adopting new-age technology like natural language analytics can provide you with the competitive edge you need for business success. It can enable you to gain useful insights about customer behavior that you can leverage to make data-driven decisions

This artificial intelligence-driven tech is a powerful contact center technology. What can it do for contact centers, and how can you leverage it? 

Let’s start digging. 

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What is natural language analytics?

Natural language analytics is a next-gen analytics technology that utilizes AI tech like natural language processing (NLP) and data analytics for data visualization. 

What’s so special about that?

It makes complex, unstructured data comprehensible and usable. 

While NLP can understand and analyze natural language queries, language analytics can process large and complex data sets into charts, graphs, or other visual representations. In doing so, it presents data in a simplified and legible format. This enables your center agents to draw contextual conclusions from data quickly and easily. 

In simpler words, language analysis delivers data in the most useful and efficient format. It can provide your contact center provider or call center agents with useful suggestions, anticipate questions, or make accurate predictions. 

To sum it up, language analytics empowers contact centers to “communicate” with customer data without additional effort. You can use its data visualization capabilities to drive seamless data-driven customer conversations. 

What’s the benefit of natural language analysis in contact centers? 

Imagine the data your customers generate when interacting with your business via support agents, self-service options, digital channels, inbound call centers, etc. This data is nothing but customer information that’s full of useful insights. 

But, your business intelligence (BI) system will be unable to make sense of such unstructured data, even if it has the power to improve your customer contact center performance. 

Natural language analytics can scour your customer communication channels and “read” normal human conversations to drive visual conclusions. 

The most obvious advantage of using natural language analytics is the ability to optimally leverage natural language data for contact centers. That, in turn, leads to increased agent productivity, faster resolution of customer concerns, and enhanced customer experiences

Natural language analytics can reduce your operational costs when implemented as a best practice and utilized to its full capacity. Apart from efficiency and cost gains, you can also establish yourself as a customer-first brand that prioritizes customer satisfaction

In the long run, enhancing customer support through language analytics can help with brand reputation management and customer retention.

Applications of language analytics in improving customer support processes 

Customer relationship management is anything but easy. So, when it comes to improving customer support processes, you must choose a solution that makes sense for your business. That’s why, before deploying natural language analytics, you need to be aware of its applications. 

What can language analytics do for contact centers?

It can help agents and customers 

Contact centers are responsible for omnichannel customer experience management. From customer calls to customer inquiries over chat, emails, social media, VoIP, and technical supportthere’s a lot to handle. 

Maintaining the quality of service and productivity at all times, especially during peak sales season, can be challenging. 

For successful customer experience management, you need to provide your tech support with resources enabling them to perform better. Natural language analytics facilitates organic problem-solving for contact center agents. 

Instead of spending hours studying the results of processed data or being clueless, center agents can ask contextual questions and get accurate answers. They can use this intelligence to interact with customers in a smart, seamless, and effective manner. 

The other application of language analytics is that it makes natural language data more accessible and usable. So, more and more of your employees can utilize natural language data for their daily activities. 

When empowered with such powerful technology, contact center agents can focus on problem-solving and brainstorming more than ever. 

AI-driven natural language analytics benefits customers as well. 

A BI system with natural language analytics at its core can accept more free-form inputs from end users. Customers can get data-driven responses to their queries that are easily understandable. 

Deploying language analysis can also help contact centers avoid miscommunication with customers. In a globally connected digital economy, being able to service customers who speak different languages with equal clarity is a superpower for any business. 

It can improve response times and query resolution

Language analytics is all about data visualization. But what makes it so powerful is that you can utilize these visualizations to expedite query resolution and improve your response time. 

When faced with a unique customer query, your agents can ask your BI system questions about past instances, use cases, or recommendations. Because language analysis can process data at lightning speed, you can get instantaneous support to resolve customer queries. 

You can even anticipate your customers’ needs and provide suitable suggestions as a response. Language analysis helps you do your job better, faster, and smarter. 

But there’s more to it. 

If needed, you can arrange the data visualization results within a narrative flow. You can create customer journeys that are meaningful and leave an impression. Storytelling is an underutilized but powerful application of language analytics. 

Along with empowering your contact center agents, it can help you develop a unique customer-first consumer experience management approach. 

It can help with quality assurance and compliance

Quality monitoring is critical to every contact center. It’s the quality of service that differentiates a successful contact center from an average one. 

Language analysis algorithms can study the response of contact center agents to identify weak spots. By suggesting improvements, this AI tech helps your agents maintain the quality of interactions and meet client expectations.

NLP algorithms can also easily identify possible violations or irregularities in natural language that may interfere with compliance. Flagging such entries/communications can prevent legal skirmishes that may negatively affect your reputation. 

As you can discern, natural language analytics can provide a nuanced solution to contact center challenges. Consider language analytics if you want to innovate your current contact center software or streamline your contact center solutions. 

Integrate language analytics with contact center systems 

A contact center typically runs on a contact center BI system that comprises a real-time contact management system, query routing, and agent stations for responding to queries. It’s a dynamic system that is deployed as a Software-as-a-Solution. However,  migrating from traditional Interactive Voice Systems (IVRs) to an AI-driven system is a complex process. 

To ensure you can integrate analytics with contact center systems, you need to upgrade to a contact center system that is perceptive to your organization's needs. It should be intuitive, easy to deploy, and offer granular integration capabilities. 

Instead of replacing your contact center BI with NLP-driven solutions at one go, you should pace out the integration with smaller upgrades. 

Here’s how:

  1. Use a third-party site or service provider that routes contact center traffic through a language analytics-based application server. Intelligent call routing through an application-based server is an excellent way of achieving that.

  2. Focus on customer relationship management (CRM) integration. Start by layering AI-driven tech into your contact center BI system.

At the same time, you also need to consider training your contact center agents to handle the technology efficiently. Contact centers provide omnichannel customer support. That’s why it is essential for your company to make sure you’re leveraging natural language analytics at every touch point. 

You should also remember your customers in your rush to implement language analytics in customer relationship management (CRM). 

If and when you decide to transition, you can deploy NLP chatbots and self-serving facilities at the front end of your contact center management portal. This should make it easy for your customers to submit queries in the most natural manner possible. 

But all of it will be futile without data, lots and lots of it. 

So, collect and retain the data for every interaction, query resolution, and crisis management. Use this data to train your AI algorithms so that you can refine them over time. Language analytics is a key enabler for contact centers, but to do its job, you have to provide it with the fodder it needs. 

To sum it up, take a multilayered approach to integrating natural language analytics into your contact center system, including people and processes. Only then can you make optimal use of this AI technology

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Use language analytics to supercharge your contact center 

Managing a contact center requires careful strategizing, planning, and execution. It goes beyond operational excellence and includes upskilling/empowering first responders to perform better. Natural language analytics can help you achieve both.

If enhancing customer support is your primary goal, consider making language analytics a part of your contact center strategy. You will be surprised by how seamlessly data-driven decisions can help you manage customer expectations

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