How can a large language model (LLM) improve customer issue resolution?

How can a large language model (LLM) improve customer issue resolution?

Efficient call routing matches customer calls with the most appropriate agents and/or IVR and is crucial for successful customer service. This approach reduces operating costs, enhances customer satisfaction, and decreases the average call duration.

Not all call routing strategies are created equal. Traditional call routing methods often rely on predefined rules and any customer data a company collects. These data limitations and outdated call routing structures often keep customers on the phone longer than necessary — or send them to the wrong agents altogether.

Recent advancements in Natural Language Processing (NLP) and LLMs are here to alleviate these issues. Let’s explore the specifics of an LLM-integrated call routing strategy, which optimizes call routing and improves overall agent productivity.

Leveraging machine learning for efficient call routing 

Traditional call routing methods leave a lot to be desired when it comes to satisfying customer challenges as quickly as possible. While large language models aren’t a prescribed solution to all call routing challenges, yet, they do represent a powerful potential solution that can reduce operating costs, improve internal productivity, and increase customer satisfaction.

LLMs synthesize vast amounts of data — including customer preferences, past feedback, and any historical information on customers’ interactions with a brand — to improve call routing operations. They can help call center leadership to identify distinctive patterns in customer behavior trends that should influence which agents or departments receive future calls. This data-driven approach ensures that customers are connected with agents with the knowledge and experience to promptly resolve all queries. 

LLMs also empower the agents receiving those calls. They provide real-time access to customer information and preferences, and then offer actionable recommendations that should influence the ways an agent interacts with a particular caller. This information allows agents to personalize outreach, proactively addressing customer concerns — even anticipating those concerns — before they escalate.

A large language model can integrate with existing call center infrastructure. Agents can enjoy improved access to customer data once an LLM integration is complete, without the need for extensive overhauls or complex reconfigurations.

While this integration process is powerful, it’s not simple. LLMs are incredibly complex. Before they can help agents unlock useful data behind customer concerns, they must undergo an incredibly comprehensive installation and setup process. That’s where an integration partner like Terazo can help, reducing an integration roadmap from many months to weeks.  In fact, Terazo recently worked with a customer to leverage AI to re-engage “dead” leads.  They put the solution into production in just 8 short weeks.  This solution helped the client realize a 3x increase in prospect re-engagement rate. 

Enhancing user experience through personalized assistance

Once installed, LLMs can help agents navigate vast amounts of customer information. Perhaps more importantly, they can highlight data that allows an agent to personalize customer interactions to a high degree. For example, suppose a customer has a history of contacting customer service about a particular product. In that case, an LLM can proactively identify this pattern and route the caller to an agent with experience in this field. This saves the customer time, frustration, and agents’ time by directing callers to the right place the first time.

LLMs can also improve a call center’s overall efficiency. Their trend analysis capabilities are useful in predicting when call volumes might be particularly high. This information helps call center management appropriately staff agents to reduce wait times during those periods.

A large language model is an effective tool to improve personalization across a call center — but it’s only a tool. Use of a LLMs and other AI solutions require expert integration before successful outcomes can be realized.   Partner with Terazo, an industry’s leading customer engagement solution builder, to reduce time to production and arm your agents with actionable data faster. 

Schedule your no-risk consultation with Terazo today to learn how you can leverage LLMs for your agents without grinding operations to a halt.

Torie Flood

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