Evolving customer expectations have led to a phenomenal increase in the number of companies leveraging innovative technology to optimize buyer journeys. Artificial Intelligence has emerged as one of the most valuable tools business leaders can access to boost satisfaction rates, streamline contact center processes, and access valuable insights.
AI technology gives organizations the power to deliver personalized 24/7 service to consumers on a range of channels, through bots and virtual agents. It can reduce operational costs, allowing agents to automate various tasks, and even provide insights into customer preferences and sentiment.
However, AI also comes with risks to consider, particularly in regard to ethics and security. Here’s your guide to the best ways you can leverage AI to enhance customer support, without falling victim to common implementation issues.
The 3 Main Use Cases for AI Customer Support
As AI solutions grow more advanced, with new algorithms and frameworks to explore, the use cases for AI in customer support are evolving. Today’s companies can leverage AI for everything from increasing conversions with proactive outreach, to generating responses for customer queries.
Most of the top use cases for AI customer support, however, fall into three main categories:
1. Improving Agent Efficiency and Productivity
Today’s customer service agents face increasing pressure to deliver expert support across multiple channels, at speed. AI can boost agent efficiency and productivity in a range of ways. For instance, chatbots can handle simple requests, and automate processes for employees, like scripting or call transcription, allowing employees to focus on more valuable tasks. In fact, 30% of customer service reps are expected to use AI to automate processes by 2026.
AI solutions can also support and guide employees throughout customer support tasks. They can use sentiment analysis to detect positive and negative feelings, and provide real-time insights to employees on how to de-escalate or improve a situation. They can even help with ensuring conversations are routed to the right employee, based on sentiment and intent analysis.
2. Delivering 24/7 Service on Multiple Channels
Perhaps one of the biggest use cases for AI in customer support, is that it allows companies to offer 24/7 assistance to customers on a range of channels. AI chatbots, for instance, are available to answer questions and deliver self-service resources to customers around the clock.
They can handle an unlimited number of conversations simultaneously, and can even leverage advanced analytical tools and data to personalize interactions. Chatbots can also hand crucial information about a customer over to an agent when a conversation is escalated, reducing the need for a customer to repeat themselves.
In the evolving world of customer experience, companies can also leverage AI to build voice bots, capable of interacting with users over the phone through speech recognition. They can understand natural language, interpret intentions, and minimize call queues. These 24/7 solutions enhance customer experiences, reduce strain on employees, and minimize operating costs.
3. Enhancing Business Insights
AI is a powerful tool for companies who want to gather more insights into their target audience, and the opportunities they have to grow. AI solutions can process huge volumes of data from thousands of conversations across different channels, offering insights into topic trends and customer preferences.
AI sentiment analysis solutions can help businesses understand which factors influence the thoughts and feelings of their customers. This helps businesses to better understand customer needs and wants, paving the way for the creation of better products and services. It can also ensure companies have the insights they need to improve retention rates and reduce churn.
AI solutions can even leverage machine learning to make accurate predictions about call volumes and customer requirements. This helps businesses make more intelligent decisions about resource allocation and optimization over time.