Where Artificial Intelligence and Customer Loyalty Come Together: Experts Weigh In

Across the globe, artificial intelligence (AI) and machine learning (ML) technologies are changing the way people interact — whether it’s in the pursuit of a product, service, need, or simply for pleasure. Without a doubt, the adoption and exploration of what AI can do has changed the world and will continue to do so as its capabilities are revealed.

Marketers are keenly interested in leveraging the power of AI and ML, but many considerations must be made — from internal assessments of existing technologies and data storage to the channels in which the brand seeks to enhance its presence to the expectations of an increasingly technologically savvy customer base. And that’s just the tip of the iceberg.

Loyalty360 spoke with supplier members and loyalty strategy experts about the impact AI is making on customer loyalty programs, the challenges of integrating technologies, and leveraging AI-powered chatbots and virtual assistants to create a personalized customer experience.

Article contributors: 

·       Ori Bauer, CEO, Dynamic Yield by Mastercard 

·       Bindu Gupta, Senior Director, Strategy for Phaedon, formerly part of ICF Next

·       Emily Merkle, Senior Partner, Analytics and Data Science for Phaedon, formerly part of ICF Next

·       Nate Thompson, Partner, Data Science for Phaedon, formerly part of ICF Next

·       Claire Cilip, Account Supervisor, The Lacek Group

·       Todd Hedberg, Director Digital Strategy, The Lacek Group


The Customer Loyalty Landscape Reshaped

The emergence of incredible technologies like AI has impacted customer loyalty programs and opened a field of possibilities to elevate the customer experience and drive a greater connection to the brands leveraging these new tools. As more brands embrace AI, its influence on shaping customer loyalty programs and strategies is evident.   

For Lacek’s Cilip, it’s also about the opportunity AI brings to loyalty teams, allowing a deeper dive into customer data and predictive analysis. With customer segmentation comes the ability to deliver more personalized messages to customers and loyalty program members. 

“AI has revolutionized customer loyalty programs by enabling personalized experiences through data analysis, prediction of customer behavior for tailored rewards, and more effective audience segmentation,” says Cilip. “Real-time engagement through AI-driven chatbots and feedback analysis has enhanced customer satisfaction and thereby the potential for deeper retention. Additionally, AI’s role in fraud detection, optimized rewards, and dynamic pricing has streamlined program management while fostering stronger customer-brand relationships.”

Major brands are leveraging these new capabilities now — some for several years, gaining an edge in the market as they build on what they can do and how to deliver a better experience to their B2C and B2B customers.  

Cilip’s colleague Hedberg shares, “Brands now have advanced AI capabilities at their disposal for customer engagement and loyalty campaigns. Major CRM platforms, like Adobe and Salesforce, have evolved their AI toolkits over the last few years to better enable marketers to deepen content personalization strategies. Recent launches of generative AI tools in these platforms will make precision marketing the norm.”

While many cautiously explore Gen AI for personalized engagement, Phaedon’s Merkle notes that in the broader landscape, AI has impacted her company’s work with clients around personalization and loyalty program optimization in three main ways:

·       Helping optimize loyalty designs.

·       The company’s Analytic Accelerators, fueled by AI, analyze customer behaviors and allow for crafting tailor-made experiences for customers.

·       AI can empower the deciphering of real-time customer data — facilitating agile, data-driven decisions.

“Technology activates and connects data to enable AI, creating a truly connected data ecosystem,” explains Merkle. “These new technologies have improved the connection of data and enabled AI insights to be activated across systems, from ecommerce to a call center to the front desk. That’s where the technology piece is playing a key role today. It’s making it possible to meet customers where they are via more personalized real-time engagement.” 


AI-Driven Personalization

Across industries, much discussion is happening around the role of AI-driven personalization in enriching customer loyalty efforts. Enhancements to personalization benefit both customers and brands, ultimately increasing customer retention and improving the bottom line for companies.

According to Dynamic Yield by Mastercard’s Bauer, the benefits to both are clear: Consumers’ trust and loyalty to a brand grow when they consistently receive tailored interactions. And in turn, businesses enjoy increased retention and optimized revenue potential. He finds that investing in AI-driven personalization ultimately benefits consumer satisfaction and is a strategic move for companies aiming for long-term growth.

“Personalization makes all the difference for a brand, and AI is crucial to delivering tailored experiences at scale,” says Bauer. “AI and machine learning are the engines behind more accurate predictions, better recommendations, and more satisfying experiences overall.” 

Merkle circles back to data — the ability to collect more and the value it provides in making better-informed decisions. She explains that while it’s both in the speed and the amount of data gathered, sometimes it’s even how much insight can be gained from a very small amount of data.

“AI can read new signals quicker than in the past,” she says. “It’s a dual reality — it can handle more data, yes, but it can also glean valuable insights from less data and still make great decisions. And that’s a dramatic change from just a few years ago.” 

With AI-driven campaign tools, Lacek’s Hedberg believes that content personalization possibilities are limitless. He sees an exciting opportunity with the individualization of content delivery, with key considerations around frequency, timing, and volume of communications that are optimized for every individual recipient.  

“Now, with generative AI capabilities, marketers can personalize the copy — both in length and tone — as well as the imagery to meet the distinct preferences of each recipient,” Hedberg says. “In addition, the channel mix can be optimized to deliver communications on the channels that perform the best for every individual recipient. The best part of all these AI-driven personalization capabilities is that they occur automatically through the application of real-time campaign tracking data to new campaign launches.”

Campaign launches can take time, but Merkle believes AI is leading to substantial enhancements in the ability to swiftly create new processes and initiatives and is helping to eliminate the need for extended ramp-up periods.

“There is a significant impact on speed to market,” she affirms. “That’s really a game-changer for brands from a holistic customer experience perspective.”  


Leveraging AI-Powered Chatbots and Virtual Assistants without Losing the Human Touch

AI-powered chatbots and virtual assistants are becoming more prevalent in customer interactions. Brands can successfully leverage these technologies to address customer inquiries and improve overall customer satisfaction — and do. Still, when employing AI, there is a concern about losing the personalized connection that comes from human-to-human interaction.

Phaedon’s Thompson offers this perspective. “Leveraging chatbots enables human interactions to be more meaningful and robust. Wait times won’t be as long because the chatbot addresses all these other easy-to-answer questions. This means human interactions are more meaningful when you have them, improving the overall customer experience. Once customers get to an actual person, they have probably already given some preliminary details to the chatbot. Now, the representative is informed in advance. It also lets the customer choose chatbot and virtual assistants for speed and convenience, tailoring the experience to individual preferences.”

Additionally, Thompson notes that brands could further enhance their use of this technology by building machine learning on top of the data to understand when a question should be sent to a chatbot for answering or if it’s better to route the customer straight to a human agent to optimize the customer experience.  

Cilip agrees that brands can preserve personalized connections — similar to human interactions — by facilitating seamless transitions to human agents for complex issues. She also points out that integrating empathetic language and offering personalized recommendations retains those connections.

“Incorporating customer feedback, maintaining historical context, and allowing user customization all contribute to a strategy that enhances customer satisfaction without sacrificing the human touch,” Cilip says. “Providing 24/7 availability through AI-powered chatbots and virtual assistants, continuous learning through customer interactions, and multi-lingual support can enhance accessibility and user satisfaction.”

Building on this concept, Bauer offers the example of a personal shopping assistant backed by advanced technologies like deep learning and affinity models. This can allow consumers to pose questions using their own terminology, making the product discovery process much more intuitive. By understanding and processing natural language, these virtual assistants can guide consumers through extensive product assortments, ensuring they find exactly what they are looking for without feeling overwhelmed. At its most effective, Bauer sees AI as helping people do their jobs more efficiently and successfully. 

However, Bauer reminds brands that a balance between automation and genuine human connection is vital. While AI-driven tools can handle inquiries efficiently, they should match the frictionless experience of interacting with the existing human support functions. This can be achieved by training these systems on diverse datasets, enabling them to recognize a wide array of expressions and respond in a manner that feels tailored to the individual.  


Faster Data Analysis Means Brands Can Respond More Quickly

Real-time analytics and insights can vastly improve brands’ understanding of customer behavior and preferences. AI can facilitate the quick analysis of customer data, allowing businesses to quickly adapt and tailor loyalty program experiences. The rapid return of data can help agile marketers to swiftly respond and elevate their loyalty programs from good to great.

“The key lies in AI's capability to instantly interpret new customer behaviors and autonomously determine the next best actions without the need for human intervention,” says Thompson. “This augmentation streamlines the process, automatically identifying trends and events that call for specific responses. This agility is transformative, allowing brands to dynamically tailor loyalty program experiences to each customer’s unique journey. The fusion of real-time analytics and AI not only expedites decision-making but also ensures that loyalty strategies are aligned with evolving customer needs.”

With AI-driven analytics tools, Hedberg proposes that the application of customer data to campaigns has been supercharged and allows marketers to identify key personalization opportunities in real-time. For example, these tools can guide automation platforms to adapt the imagery on key channels to reflect recent browsing activity or shopping actions for every customer.  

“AI-driven analytics tools can also accelerate customer loyalty through advanced predictive analytics,” asserts Hedberg. “These tools will look at recent engagement signals to help service the right next best action or promotional offer recommendations. Generative AI analytics tools allow for instantaneous segment creation based on behavioral data to focus loyalty program experiences on the customers that are most likely to engage and repurchase.” 

Bauer describes manual management and analysis of data as inefficient and impossible to scale, especially as the number of audiences, experiences, and data collection points increase. He sees machine learning as a way to neutralize the challenge, but not without consistent and meaningful inputs from different touchpoints. He adds, “The right internal governance and design must be in place to protect consumer privacy and ensure responsible data use.”   


Understanding Challenges and Reluctance

New technologies bring new challenges, and brands must determine how to successfully integrate them into existing programs and platforms — or even begin from scratch. AI and ML are no exceptions. However, brands can learn to leverage new technologies with the right internal and/or external teams.

“Many of the challenges brands face when trying to integrate new technologies is just that — the actual integration,” Bauer confirms. “Legacy systems are often walled gardens, incompatible with new capabilities that leave brands stuck between the old and the new, unable to successfully connect the two. For example, a brand might have a sophisticated AI system that generates product or content recommendations, but if it can’t be customized with specific rules and unique logic, the brand will miss out on opportunities for enhanced engagement.”

Beyond integration into existing technologies, brands must navigate the complex environment of data collection and storage, privacy concerns, and newly enacted state laws and regulations impacting how they can gather customer data. This field only promises to become even more complex as consumers and governments decide how customer data can be used.

“With the implementation of AI and ML technologies, several challenges have emerged, often requiring innovative solutions,” begins Merkle. “In highly regulated industries, data residency and compliance concerns, such as HIPAA and China, have posed significant obstacles. The need to ensure data privacy while harnessing AI’s power has compelled brands to find ways to personalize experiences within regulatory boundaries. AI’s ability to use less data can help in these areas.”

Furthermore, brands can be challenged when leveraging AI and ML technologies as disparate departments within an organization might control access to data needed to fully round out ideal responses to customer trends and activities.

Merkle understands this obstacle. “The complexity of data environments has also been a hurdle. Advanced analytics and AI demand a connected data environment, which can be hindered by existing data silos. Integrating data streams and cleaning data become essential to unleash the potential of these technologies. The quality of insights hinges on the quality of input; AI can supply answers, but poor data inputs yield unreliable results.”

The need for additional time for training and testing, which is often extremely limited, is key. Hedberg acknowledges that in addition to resource constraints, emotional triggers may play a role in reluctance or resistance when it comes to leveraging a new tool. When it comes to the new AI and ML capabilities, both dynamics are present.  

“What’s been interesting with AI-driven marketing tools is how the broader marketing community seemingly went from skeptical about the benefits of the capabilities to suddenly scared about the overpowering force of generative AI,” says Hedberg. “Marketers who have viewed generative AI as the thought starter and timesaver tool are suddenly flourishing. After all, generative AI still lacks the imagination of a marketer, so these efficiency tools can only help to accelerate big thinking and innovation across organizations of all sizes.” 

Merkle believes certain industries are reluctant to fully embrace AI and ML due to perceived risks and lack of familiarity. And yet, she sees the landscape as shifting, explaining that AI itself is stepping in to address some challenges.

“AI’s ability to identify outliers, clean datasets, and automatically handle missing data is becoming a game-changer,” says Merkle. “With AI’s intervention, organizations can refine and prepare their data for analysis, resulting in more accurate insights.”

Bauer shares that according to data referenced in Mastercard’s latest Signals report on commerce in the age of generative AI, 50% of companies reported using AI for at least one business application in 2022. He adds, “And per Dynamic Yield’s State of Personalization Maturity Report, 98% of businesses surveyed plan to invest more in personalization in 2023.”   


Concerns about Using AI

As some concerns about the use of AI in specific industries have come to light — for example, the entertainment industry and the recent writer’s strike/SAG-AFTRA strike — questions about the overuse of AI have been raised.

“The writers’ strike in the entertainment industry brings up concerns around the loss of creative control, credit sharing, and displacement of artists — which can be hot-button issues in other industries and brand organizations as well,” affirms Cilip. “These concerns echo ongoing debates about AI’s role in authenticity, personalized customer interactions, data privacy, and the preservation of the human touch that customers highly value.”

Cilip advises that as brands leverage AI, it becomes crucial to strike a careful balance between harnessing its benefits and ensuring genuine connections and ethical practices remain at the forefront of customer interactions. 


The Future of Loyalty Programs with Technologically Savvy Customers

As customer expectations evolve and loyalty programs and strategies adapt, loyalty programs must adjust and respond as customers become increasingly technologically savvy.

“Flexibility will define success. Integration with payment apps will be an expectation, enabling seamless utilization of loyalty points,” says Phaedon’s Gupta. “Customers will demand experiences that intuitively sync with their daily lives, and any dissonance in interactions will be swiftly noticed. This shift isn't confined to specific sectors. Evolving customer expectations have permeated industries across the board, leaving no room for subpar engagement.” 

Gupta goes on to describe how loyalty programs must align with this trajectory, embracing the latest technologies to simplify customer interactions. Seamlessly enabling purchases across various platforms and knowing customers’ preferences irrespective of their location or channel will be essential as customers expect data continuity, personalized ease, and consistency. 

“This demand is particularly pronounced among millennials and Gen Z, who anticipate tailored experiences with seamless convenience,” says Gupta. “As these generations form a larger part of the consumer base, their expectations will shape loyalty programs profoundly. The future entails loyalty strategies that redefine personalization, harness the power of innovative technologies, and seamlessly integrate into customers' tech-driven lives.” 

In sum, as customers become increasingly technologically savvy, loyalty programs will be defined by how personalized they are. Cilip theorizes that blockchain and tokenization could create unified ecosystems while AR, VR, and voice interfaces offer immersive experiences.

“Sustainability, biometric authentication, and instant gratification will shape programs, further enforcing the necessity of transparent data practices,” Cilip finishes. “Continuous innovation and a strong focus on customer-centricity to meet evolving expectations will be critical to a successful loyalty program.” 

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