In a world where social media and technology have leveled the playing field between brands large and small, leaders know the only true differentiator is customer experience (CX). They know it’s one of the most human aspects of running a business—and they’re exploring innovative technologies like artificial intelligence (AI) to enrich it.

Besides workflow efficiencies, AI tools provide nuanced insights that can transform your customer journeys to become more engaging and supportive. They enable you to develop a compelling customer experience strategy to serve customers better, provide personalized offerings and build meaningful relationships.

In this article, we’ll break down what AI customer experience is all about and the technologies that power it. You’ll also see eight practical applications of AI for creating a memorable, personalized customer experience.

What is AI-driven customer experience?

AI customer experience is the use of AI technologies like natural language processing (NLP), text analysis and sentiment analysis to delight customers wherever and however they interact with your brand.

AI tools not only help replace cumbersome processes with highly efficient workflows, they seamlessly analyze unstructured data to extract important business intelligence. These valuable insights empower employees to make better decisions that improve the overall customer experience and satisfaction.

In fact, per The 2023 State of Social Media Report, 96% of business leaders believe AI will help companies significantly improve their decision-making processes going forward.

Technologies powering the AI customer experience

There are many AI-based technologies that work in conjunction with each other to elevate the customer experience. These are the most prominent ones.

Natural language processing

NLP helps a computer understand human language through text analysis, complete with colloquialisms, language-based nuances and emojis. To do this, NLP uses two other AI subtasks: natural language understanding (NLU) and natural language generation (NLG). NLU and NLG power smart assistants and AI-driven chatbots so they can be used for round-the-clock, enhanced customer service.

Sentiment analysis

Sentiment analysis detects emotions or sentiments in data, which can be used to gauge how customers perceive your brand or your services. The technology identifies sentiment in feedback from a wide range of sources such as platforms like Trustpilot or Google My Business, social media comments and direct mentions, surveys and news sources.

Predictive analytics

Predictive analytics understands patterns in customer behavior to anticipate future customer needs. It is used to optimize sales, plan logistics and supply chain, or boost brand promotions for maximum impact. For example, by studying customer data, retailers can anticipate ebbs and flows in footfalls based on location, events or seasons and allocate resources accordingly.

Predictive analysis can also be used to stem customer churn by identifying contributing factors based on voice of customer data.

Machine learning

Machine learning (ML) is used to mine insights from huge amounts of data automatically. AI systems use machine learning to automate subtasks such as topic extraction, feature classification and text parsing necessary for text analysis and sentiment analysis.

These models analyze data through artificial neural networks (ANNs) to understand and correlate patterns in data and learn as they go. This means, when they process customer experience data they can dig into audience demographics, interests, trending topics and other factors to provide increasingly accurate insights over time.

An example of this is how Spotify uses machine learning to improve content recommendations. It predicts what consumers may like based on their current listening choices and offers personalized suggestions across musical genres, playlists and podcasts.

Named entity recognition

Named entity recognition (NER) allows a computer to identify important names that occur in data. These named entities could be people, businesses, currencies or locations and are necessary for competitive analysis. An NER model can be trained to recognize millions of data points and apply them to industry-specific contexts.

Computer vision

Computer vision helps in image recognition and optical character recognition (OCR), which helps a system detect patterns in image-based big data. This technology is often used to identify celebrities, brands and products on social media platforms for targeted advertising and competitive analysis, and to diagnose customer issues.

8 ways to apply AI to the customer experience

According to our research, business leaders see vast potential for AI to make their brands more customer-centric. Here are the most useful applications of AI and machine learning that executives feel will build a richer, more effective customer experience.

Similarly, AI is helping replace tedious interactive voice recording (IVR) systems with intelligent voice automation to increase customer service efficiency.

Build a more human customer experience with AI

AI tools can fast-track your way to a richer customer experience built on personalized care, quicker support and authentic engagement.

Conducting a customer experience audit is a good place to start so you can identify what’s currently working and what areas need your attention. It will also give you a better idea of what AI capabilities will best serve your business goals.

Take a look at some of the templates we’ve developed to help you audit and optimize your customer experience.

By: Annette Chacko

Annette Chacko is a Content Specialist at Sprout where she merges her expertise in technology with social to create content that helps businesses grow.

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