In a recent research project, Deloitte gathered 30 CX leaders who serve more than a billion customers across sectors to talk about the future of AI for the customer experience. And almost all of them shared an optimistic outlook on AI. In fact, a huge portion of them said that AI has the potential to improve customer experience. How? By empowering employees to do better work.

Up to 78% of service leaders say AI helps them focus on the important aspects of their jobs. And another 71% say AI and automation help them spend more time doing what they enjoy the most. Overall, companies using AI experience a more engaged workforce. Why? Because AI provides employees with the tools necessary to support their customer experience.

The truth is customer experience remains a key differentiator for businesses across all industries. And, delivering exceptional CX is imperative to maintaining customer loyalty.

AI can help.

Discover how these tools revolutionize the way companies interact with customers. Let’s explore some real-life AI customer experience examples so you can up your CX game.

Real-World AI Customer Experience Examples to Help Shape Your Strategy

Personalized Customer Experiences with AI-Powered Recommendations

Personalization is the new norm. In fact, it’s expected.

Almost 60% of customers expect businesses to use their collected data for personalization. And AI makes hyper-personalization possible. First, it helps companies gather data. Then, it generates tailored recommendations for your customers. This extra insight boosts loyalty, sales, and CX.

AI-powered personalization can be particularly useful in customer service. AI helps agents assist customers based on their past behaviors or inquiries. Using AI, teams can update customers about new products that are best aligned with their history. With these recommendations, you can drive upselling, cross-selling, and engagement.

>> See it in the real world: Netflix

Netflix uses machine learning algorithms to analyze user behavior, viewing history, and preferences. It then generates personalized content recommendations for its customers. With this data, the platform identifies patterns. Netflix then suggests movies and shows that align with individual tastes. This personalized approach enhances the customer experience and keeps Netflix competitive.

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Instant Support through AI-Driven Chatbots and IVAs

AI chatbots let contact centers provide instant, 24/7 support. These tools use natural language processing and machine learning to converse with customers. They tackle simple customer interactions, relieving pressure on human agents and expanding self-service.

Some companies have added chatbots and virtual assistants to handle routine inquiries. These tools can also resolve issues and even guide customers through complex processes. All without the need for human intervention. Not only do chatbots speed up response times. They also allow human agents to handle more complex queries. The result? A better customer experience across every channel.

>> See it in the real world: Domino’s Pizza

Domino’s Pizza added a chatbot to make the pizza ordering process smooth and effortless. With the chatbots, customers can reorder or submit a new pizza order. They can also track their pizza delivery by chatting with Dom, the company’s chatbot. Besides providing engaging and straightforward communication, the chatbot makes ordering pizza convenient. No need to download an extra app to your phone, dial a phone number, or open a website to order pizza. This system lowers the bar for entry. In turn, Domino’s improves pizza sales and supports a better customer experience.

Discover how to add virtual agents to your CX in this blog.

Proactive Customer Service with Predictive AI

Predictive AI and analytics let you anticipate your customer needs before they arise. In effect, you can prevent churn, delight customers, or get ahead of potential issues using AI. AI can not only forecast potential problems, but it can track trends in customer sentiment. It looks at data from various touchpoints to tag repeat behaviors and anomalies. Then, service teams can proactively send messages to offer solutions before a customer even realizes the problem.

>> See it in the Real World: Volvo

Volvo uses predictive analytics and machine learning to power its Early Warning System. This technology analyzes millions of events weekly. It can then predict each vehicle part’s breakdown rates and signal when cars need servicing. With this information, Volvo can recommend service plans to customers. Or they can suggest a replacement part before a part breaks or creates bigger problems. By being proactive and communicative, Volvo builds a lasting and loyal customer base.

Understanding Customer Emotions With Sentiment Analysis

Understanding customer emotions is critical to delivering a positive experience. AI-powered sentiment analysis tools make finding your customer’s voice easy. These tools can analyze customer feedback and social media mentions. Then, using this data, they generate insights to help businesses respond more effectively. During interactions, sentiment analysis also lets agents respond to emotional shifts. Together, these tools help companies produce a better customer experience.

>> See it in the Real World: Butternut Box

Dog food company Butternut Box struggled with fragmented customer feedback. They couldn’t properly track data. And so, they had limited insight into their customer experience. Using sentiment analysis tools, the company consolidated and analyzed its feedback. They automated their survey process, allowing for more accurate and consistent insights. In the end, they gained a better sense of how their customers felt. And that lead to improved marketing and strategic changes in their offerings.