AI Customer Experience: Explore 4 Cutting-Edge Strategies


Editor’s Note: This article has been updated on June 30, 2023, to include new data and information. 

The Gist:

  • Role of AI in Decision-Making: AI improves real-time and predictive decision-making, helping brands provide a hyper-personalized customer experience and foster deeper customer-brand connections.
  • AI Chatbots and Challenges: AI-powered chatbots complement human customer service. However, challenges such as data silos and cost misconceptions can impede AI adoption in customer experience.

Artificial intelligence (AI) — especially with advancements in generative models — continues to gain popularity for marketers and sales professionals. The easy availability of AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software has brought AI to the enterprise without the high costs previously associated with the technology.

Nearly half of US marketers increased customer experience-related AI investments in 2022, according to a report from Insider Intelligence and eMarketer. This increase in AI customer experience spending mirrored the increase in CX technology spending overall, a market that reached $641 billion in 2022.  

Companies that want to stay ahead of the curve, according to the report, will need to adopt AI technology for customer experience purposes over the next four years. 

How Is AI Used for Customer Experience? 

AI for customer experience is a way to use artificial intelligence technologies like machine learning, chatbots, conversational user experience (UX) and advanced analytics to analyze customer data in an effort to personalize customer interactions, boost customer service efficiency and increase self-service options. AI can also optimize business processes and improve key performance indicators like customer loyalty and engagement. 

AI can improve the customer experience with tools like chatbots.

How AI Is Changing Customer Experience? 

Today’s customers have high expectations when it comes to customer experience. Consider these key stats: 

  • 61% of customers say they’ll pay more if they know they’ll get a good customer experience, according to an Emplifi report
  • 86% of consumers will walk away from a brand they’d previously been loyal to after as a few as three bad experiences. 
  • 56% of customers say quality of customer service has the highest impact on how positively they view a brand compared to any other criteria, according to another Emplifi report

How is artificial intelligence reshaping it all? AI for customer experience is making it easier for companies to craft personalized, positive experiences at all customer touchpoints. 

Various touchpoints, like email and call center contact, along the customer journey.

Let’s dive a little deeper into how AI improves customer experience. 

Related Article: Customer Experience Is the No. 1 Focus for Generative AI Investments

1. AI Enhances the View of the Customer 

The combination of artificial intelligence and machine learning for gathering and analyzing social, historical and behavioral data enables brands to gain a much more accurate understanding of its customers. 

Unlike traditional data analytics software, AI can analyze customer behavior and data and continuously learn and improve throughout the process. With that information, it can then predict future customer behavior. This technology allows brands to provide highly relevant content, increase sales opportunities and heighten customer satisfaction.

Sven Feurer, senior director of product strategy & operations at SAP Customer Experience, shared his thoughts on using AI for customer experience: “When it comes to customer experience, there is promise for broad impact. With the exponential growth of data arises an opportunity for both B2B and B2C brands to utilize it along with AI to improve everyday experiences for customers,” he said. 

CRM platforms with integrated AI customer experience features often boast capabilities like real-time decisioning, predictive analysis, sentiment analysis, conversational assistants and more to help sales teams better understand and engage customers. CDPs have also integrated AI elements to unify customer data and provide real-time functionality and decisoning for marketers.

According to Mike Orr, senior advisor and mentor at Nobellum and former CEO of Grapevine6, in an increasingly digital world, customer engagement often centers on digital content. “[By] combining natural language processing (NLP) to the content,” said Orr, “we can develop insights into each individual customer experience and commit those to a customer data platform — not only do these insights into the interests of the customers provide context for the next human interaction, but also the next content experience.”

AI, he added, can also be applied to recommend next-best actions for the customers by learning how interests and insights reflect their needs from similar customers. 

2. AI Improves Real-Time and Future Decision-Making 

Real-time decisioning is the ability to make a decision based on the most recent data available, such as data from the current interaction a customer is having with a business — with near-zero latency.

Real-time decisioning can be used for more effective marketing to customers. Some examples include: 

  • Identifying customers using ad blockers and providing them with alternative UI components that can continue to engage them. 
  • Personalized recommendations, which are used to present more relevant content to the customer. 

By using AI and real-time decisioning to recognize and understand a customer’s intent through the data that they produce, in real-time, brands can present a hyper-personalized customer experience. 

Predictive analytics refers to the process of working with statistics, data mining and modeling to make predictions. Because AI is able to analyze large amounts of data in a very short amount of time, it uses predictive analytics to produce real-time, actionable insights that guide the next interactions between a customer and a brand. This is often referred to as predictive engagement, and it requires the knowledge of when and how to interact with each customer, something AI is very good at.

AI and predictive analytics are able to go further than historical data alone, providing deeper insights into what has already occurred, and what can be done to facilitate a sale through suggestions for related products and accessories, making the customer experience more relevant and more likely to generate a sale, as well as providing the customer with a greater sense of emotional connection with a brand.

Related Article: Relevant, Real-Time CX: Consumer Perception vs. Brand Reality

3. AI Chatbots Meet Customers Where They Are 

Chatbots were one of the earliest types of AI technology adopted by organizations. At the end of 2021, 64% of US executives in a Coresight Research survey said they used AI chatbots to offer personalized experiences to customers. And today, with the release of faster, smarter generative models, that number is likely higher. 

Feurer recognizes the usefulness of AI chatbots for providing personalized assistance to customers but does not believe that they are a replacement for human contact. 

“Modern businesses should view chatbots not as a replacement for humans but rather as supplementing the human workforce to help their employees be as efficient as possible,” he said. “Notably, organizations must strike the important balance between self-service and human interaction to deliver the most convenient experience possible.”

AI-powered chatbots can save businesses money while allowing customers to self-serve minor issues, said Feurer. But it’s important to remember that chatbots should only be used to tackle a select number of topics — like invoice management, order tracking and account management.  Orr understands the value AI chatbots bring to customer interactions but reiterated what Feurer said about the need for customer service reps and chatbots to work in conjunction. 

“Chatbots and really all autonomous customer experience ‘robots’ have the potential to solve a bunch of transactional problems, often related to information discovery,” said Orr. However, he added, ”there’s an inflection point though where the complexity of the answer requires a person as a trusted intermediary — you may find the advisor using a robot, but you’re not going to take financial advice from a robot unless it’s very simple.”


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