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.

Callout defining AI-driven customer experience

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.

Graphic enumerating 8 ways in which AI applications enhance customer experience

1. Behavioral segmentation for targeted products and marketing

According to The 2023 State of Social Media report, 49% of business leaders think AI will be critical for behavioral segmentation to identify and target specific customer segments.

AI capabilities scan millions of data points from various sources such as social media and review websites to spot hidden patterns. This is how they provide insights beyond traditional demographic stereotypes (like, all gamers are male), allowing you to narrow down segmentation as much as you want. These insights help you develop more effective targeted marketing campaigns and a higher level of personalization in products and services.

For example, this makeup company has a targeted Facebook marketing campaign for women above 50 for a section of their makeup line, based on audience profiling.

Facebook reel screenshot of a makeup line aimed at women over 50.

2. Predictive analytics to forecast future customer behavior

Per the same report, 45% of leaders believe using predictive analytics to indicate future customer behavior will be an essential AI application.

Predictive analytics uses machine learning to analyze data, both internal (sales and customer data) and external (current events, competitor data, review and social media comments) for insights. These are critical for anticipating market trends and informing decisions around inventory control, marketing spend and other investments.

For example, alcoholic beverage company Diageo uses AI to get real-time forecasts of customer demand, commodity prices and creditor payments. It also uses AI insights to inform investment decisions based on factors like the timing, length and reach of a marketing campaign.

3. Optimize pricing based on demand

Forty-five percent of business leaders say that AI and ML will be critical for building dynamic pricing models in the future.

This is not surprising given that dynamic pricing is common in industries such as hospitality and tourism with fluctuating customer demand (e.g., the popularity of a flight/destination) and seasonality (weekends or weekdays).

Screenshot of a tweet from Bloomberg Markets about travel spiking amidst the drop in airfares

AI algorithms analyze both historical and real-time data (e.g., inventory, demographic-based sales, competitor pricing and social media posts) to pull highly relevant, time-sensitive insights. With this information, teams can customize product pricing and messaging proactively so you can increase your competitiveness and meet revenue goals.

4. Sentiment analysis to understand customer feedback

Among the business leaders we surveyed, 44% report AI-driven sentiment analysis will be key to understanding customer feedback and responding to customer issues more efficiently.

A sentiment analysis software can specify what customers like and dislike about your brand by giving you targeted negative and positive metrics on a topic or aspect of your business. For example, a health system can use social media sentiment analysis to identify which aspects of their organization patients are happy with and which need to be improved.

In this manner, sentiment analysis can identify factors affecting your brand image, customer retention rate or brand loyalty.

In Sprout, you can do so from a variety of social listening sources like Twitter and Instagram. You can monitor and organize social mentions in real-time and measure sentiment based on terms and hashtags you want to track, all in one unified platform.

Screenshot of Sprout Social's sentiment analysis report that shows trends in audience sentiment on various topics found in the social listening data.

5. Personalize content and improve customer engagement

Forty-four percent of survey respondents feel using content recommendation engines to improve personalization is one of the most promising applications of AI.

AI tools provide customer-specific insights from purchase histories, website behavior (searches, scrolls and clicks) and comments to predict what they may be interested in so you can tailor and optimize your content for maximum impact.

You can also drive customer engagement and improve customer response rates significantly with personalized, pre-approved suggested replies using tools like Sprout, as ice-cream brand Carvel did for an enhanced customer experience.

A screenshot of suggested replies in Sprout Social. These tools can speed up your social media response time and reduce time spent writing manual messages.

6. Image recognition to analyze visual content

With visual content dominating everything from social media to web search, 43% of business leaders believe AI will help with image recognition to identify and analyze visual content.

Visual AI algorithms identify patterns in visual content, analyze search histories and provide targeted suggestions for design ideas or variations. Many popular brands such as Canva and social networks like Pinterest have already integrated this AI feature into their platforms for a richer user experience.

Visual AI is equally critical in sentiment mining, competitor analysis and personalized marketing and advertising tactics. For example, while searching for “gray wall bedroom ideas” on Pinterest, I also received targeted ads from home decor brand, Wayfair.

Screenshot of Pinterest pins of gray-walled interior designs

AI for visual content also includes video content analysis.

Videos are just a series of images or frames shown at an accelerated speed. AI algorithms break down these frames and scan for celebrity faces, brands, logos, locations or other elements they’ve been trained to look for.

This ability is a game-changer because it enables you to measure sentiment in videos as easily as in text data. You can measure customer sentiment and conduct competitive analysis on competing brands from videos on platforms like TikTok, Instagram and YouTube.

7. Improve customer service through improved chatbot interactions

Forty-one percent of business leaders think NLP will play a key role in improving customer interactions via virtual assistants and intelligent chatbots.

NLP enables virtual agents and chatbots to understand conversational language and respond to customers by automatically generating responses based on set parameters.

Unlike rules-based chatbots, AI-driven algorithms have the ability to understand semantics and therefore identify customer issues more easily. They can even recommend next steps like directing the customer to a live agent.

Brands like Walmart are already adopting conversational AI capabilities with ChatGPT to enrich their customer experience. Apart from having access to intuitive customer service, customers will also be able to add products to their cart by texting or using voice commands, via the Walmart mobile app.

8. Optimized voice search for better customer experience and SEO ranking

Last but not least, 40% of leaders believe voice search optimization is one of the most important applications of AI going forward.

AI-based voice search optimization improves your website’s content and structure to boost visibility so you fare better in voice search rankings. This is a growing need for brands, given that voice-enabled purchases through smartphones and smart devices in the home are anticipated to grow by 400% within two years (2021 to 2023).

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.