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How AI can improve customer loyalty
AI plays a central role in customer loyalty. Yet, organizations can have a greater ROI if they focus their AI strategies more on improving CX and less on cutting costs.
As predictive and generative AI tools can analyze large volumes of consumer data, they can help CX teams anticipate customers' needs and improve customer loyalty.
Customer loyalty initiatives remain central to meet increasingly steep customer expectations and can help companies stand out in competitive markets. At Forrester's 2024 CX Summit North America, analysts shared insights about AI strategy and offered best practices to meet these expectations.
In a session called "Leveraging AI to drive customer loyalty," Forrester Vice President and Principal Analyst Mary Pilecki and customer loyalty expert Phil Rubin shared best practices and challenges of using predictive and generative AI (GenAI) in customer loyalty. This technology lets organizations collect and analyze data, but many struggle to use it to increase customer loyalty.
"We've been doing a great job of paying attention, in the form of collecting data. We have not been doing a great job of treating [customers] accordingly … and that's where there's so much opportunity," Rubin said.
Predictive AI requires greater focus on contextual relevance
Predictive AI -- software that makes predictions based on patterns in data -- has been around for decades. Yet, advancements in computing power and large increases in customer data have made it more effective since the 1990s. Modern CX teams often use it to predict customer behavior, personalize experiences and proactively identify challenges along the customer journey.
Despite advancements in predictive AI, Forrester's "U.S. Customer Experience Index Rankings, 2024" report showed CX scores at an all-time low. These scores have declined for three years in a row, indicating a gap between consumer expectations and what brands offer.
In their conference session, Pilecki and Rubin suggested brands focus their AI strategies on creating contextual relevance, as opposed to cookie-cutter personalization. Contextual relevance, or contextual marketing, refers to delivering valuable messages at appropriate times, whereas personalization can simply mean using a customer's first name in an email. The former offers something of value, such as information the customer should know or a discount on a specific item, whereas personalization might only demonstrate that the organization knows the customer's name.
Simple personalization can enhance CX to some degree, but customers prefer value in the form of financial benefits, useful information and convenience, Pilecki said. Contextual relevance offers this value and can, therefore, boost customer loyalty.
GenAI video connects with customers
Brands have found fewer use cases for GenAI in customer loyalty compared to predictive AI. However, in the session, Pilecki and Rubin agreed that GenAI-created videos can enhance personalization, contextual relevance and, in turn, customer loyalty. Rubin shared his experience with a company that sent him one of these videos.
"After you make a purchase, you get a video from the head of the company, and he's using your name in it. And it's elegantly done -- to the point where you feel like he made the video for you. … It totally elevates the connection you have with the company," Rubin said.
Pilecki also shared a personal story of how an AI-generated video offered her contextual relevance.
"I got a video from my bank … [and] they addressed me by name [and] showed me how much I saved last year. They expounded on some of the benefits that I wasn't taking advantage of. It felt very personal," Pilecki said.
Although an overreliance on AI-generated communications can seem impersonal and potentially harm customer loyalty efforts, high-quality AI videos seem to evoke positive emotions from customers.
Advice for brands experimenting with GenAI
GenAI's ability to quickly create content lets CX teams automate different tasks, such as responding to customer emails and writing social media posts. Yet, more automation doesn't necessarily translate to a more profitable organization.
"We're running into issues with GenAI. Along with the hype, we're seeing inaccurate data, negative ROI in some cases where the technology costs more than the savings, and also there's a lack of consumer trust," Pilecki said.
To avoid these challenges, organizations should only implement GenAI where they think it can improve CX and revenue, not just cut costs. Many CX professionals mistake efficiency with effectiveness, which can lead to poor CX, according to Rubin.
"At the end of the day, you've got to be more effective and more profitable, not just more efficient. You can solve all your cost problems, but if you're not generating more revenue, you're still not growing," Rubin said.
Tim Murphy is associate site editor for TechTarget's Customer Experience and Content Management sites.
