How AI Provides Advantages in Call Centers
According to one survey, 94% of Baby Boomers say calls are the quickest way to reach customer care. Surprisingly, 71% of Gen Z say the same, despite their familiarity with alternative options such as text, email and direct social messaging.
No wonder, then, that 57% of customer care leaders expect call volumes to increase over the next two years despite available alternatives. And small businesses are no exception. Their small size is no bar to creating a substantial online presence, which in turn necessitates a reliable and responsive call center.
Integrating AI systems such as large language models (LLMs), chatbots and natural language processing (NLP) offers a path to improved call center operations that don’t break the bank. Key advantages include:
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Why Tier-One Support Is an Ideal Starting Place for AI
Tier-one agents are the first stop for callers. They’re also the ideal entry point for AI. This is because tier-one calls serve two common purposes: Answering simple questions and routing callers to higher-level support as needed.
AI tools excel at both tasks. Chatbots can be trained to answer simple questions using connected databases and can flag calls for escalation based on the content and context of customer calls.
For example, if a caller is looking for delivery updates, AI tools can ask for tracking numbers and fetch relevant data. If, however, a customer states that their item has arrived but isn’t working as intended, AI can escalate the call to human agents.
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Sentiment Analysis Improves Customer Service
Improved LLM and NLP frameworks help AI agents better understand customer sentiment. Consider two customers who call an SMB, reach a chatbot and say, “I need to speak with an agent.”
What the business (and the chatbot) don’t know is that customer #1 ran into a challenge with his new product and immediately picked up the phone. Customer #2, meanwhile, is dealing with the same problem but has spent the last few hours trying to solve it himself, getting increasingly agitated in the process.
While both customers say the same thing, their tone changes the meaning. Customer #1 might be responsive to a chatbot offering to help. Customer #2 will not be.
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