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Leveraging AI for Next-Level Customer Loyalty Programs

Building Trust and Engagement Through Hyper-Personalized Experiences

Artificial intelligence (AI) and machine learning (ML) have become powerful tools in our everyday lives, so it’s no surprise they’re revolutionizing the modern marketplace. At the same time, organizations across the board are leveraging AI and ML within their brand strategy in pursuit of a competitive edge.

It’s no different in loyalty program management, where AI and ML are being applied with the goal of achieving hyper-personalization – the key to amplifying customer loyalty and engagement. There’s plenty of evidence to support the value of hyper-personalization:

  • This eBook from Comarch shows that 52% of customers say they won’t read messages that don’t feel personalized, and 72% won’t engage with any content that isn’t personalized.
  • Research from Deloitte indicates up to 80% of customers are more likely to buy from a company that offers a personalized customer experience.

To create and execute an effective brand strategy, the approach to utilizing AI and ML must go beyond simply adopting the technology—it should be built on a comprehensive, methodical, and data-driven foundation including the elements discussed here.

Fueling the Right Kind of Data

The basics of AI and ML begin with the understanding that data is their fuel. High-quality data that covers the gamut of customer transactions, app behavior, responses to emails, social media engagement, and contextual factors like weather and location is crucial.

AI can analyze these complex patterns and make predictions that would be too intricate for people to process manually. However, raw data alone isn’t enough – especially when using these tools to create a personalized brand strategy.

As consumers rapidly adopt AI for product discovery and research, they increasingly expect brands to use AI to deliver relevant, responsive experiences. A Storyblok study highlights this shift:

  • 40% of consumers report using AI tools, such as ChatGPT, to research products, with 17% identifying AI as their primary information source, trailing only Google (45%) and Amazon (26%).

Brands are recognizing this shift as well.

  • 63% are already using AI in their marketing strategies, and 47% anticipate that AI will significantly reshape their SEO tactics.
  • AI-powered search engines are transforming consumer behavior, with 40% of brands predicting more personalized search experiences and 25% expecting greater trust in search results.

However, a significant challenge to obtaining the quality of data needed for AI to work effectively remains:

  • Only 4% of Chief Marketing Officers feel their data is adequately prepared to fuel AI technologies effectively.

This gap reveals that AI and ML aren’t simple solutions. Successful implementation requires data that is clean, relevant, and strategically aligned with business objectives, which demands both technical expertise and ongoing optimization. Elements of “how” to apply AI to your business include Deep Neural Networks, Product Embedding, and Association Rule Mining.

Now That You Have Data, You Must Protect It

Another important consideration is that, as data is collected, it must be stored according to regulatory standards—particularly regarding personal data. Proper data governance is not only about compliance but also essential for building customer trust.

With only 17% of consumers believing their personal data is secure with organizations, strict data privacy measures are vital for maintaining customer confidence in today’s data-driven environment. The goal is to organize and prepare data effectively, creating a foundation for seamless AI integration and powerful personalization.

Brands also must prioritize customer privacy. This means getting clear consent from customers before using their data, being transparent about how customer data will be used, and ensuring full compliance with all privacy regulations.

Get the Right Resources

AI and ML aren’t off-the-shelf solutions, nor are they meant to replace human intelligence. Instead, they require training and customization guided by human expertise. To achieve meaningful results with AI, brands must engage specialists who understand both data analytics and technical requirements, enabling them to align AI with the business’s strategic objectives.

Investing in these specialists allows brands to gather and analyze relevant data from diverse sources—such as purchase histories, behavioral patterns, and customer interactions—ensuring that AI resonates with targeted audiences.

These experts are tasked to continuously test, refine, and adapt AI systems to align with unique brand data and customer demographics, creating highly personalized experiences. Through ongoing assessments, they help brands adjust to evolving data patterns, honing AI to anticipate customer needs and boost engagement and loyalty.

Partnering with a loyalty service provider can further accelerate AI adoption. These providers offer reliable insight and support, as they routinely manage complex AI elements such as algorithm training, data storage, and model testing.

For brand marketers to effectively use data in their loyalty programs, specialists and loyalty service providers that use AI and ML become a strategic brand marketing tool to boost engagement and loyalty.

The Importance of Testing and Learning Environments

Brands should be mindful of the need for testing and learning environments with customer-oriented safeguards in mind. Thoughtful AI adoption means testing new features, prioritizing customer privacy, and committing to continuous improvement.

Building a culture of learning within your organization is essential for successfully integrating AI and building trust with customers. It’s important to create testing environments that keep customer safety top-of-mind. By taking a careful and responsible approach, brands can develop personalized loyalty programs that truly connect with customers.

One way to guide this process is through A/B testing. Testing different approaches helps brands identify the most effective personalized experiences while allowing strategies to evolve with time.

It might take some extra effort upfront, but by thoughtfully integrating AI into a brand’s strategy, organizations can create loyalty programs that offer a truly revolutionary customer experience. Using AI can be a game-changer, but it’s not just about adopting the latest technology. To make it truly effective, brands need to take a comprehensive approach to data acquisition, management, and usage.

Focus on Specific Objectives with your AI-Driven Hyper Personalization Strategy

When done properly, brands can foster lasting trust with their audience—delivering a loyalty experience that genuinely resonates. A range of business outcomes are possible with your AI strategy and it’s likely that your objectives will fall into one of these categories: reduced churn, CLV Prediction, Loyalty Fraud Prediction, and more.

AI empowers brands to hyper-personalize in several key areas:

  • Churn Prediction—One of the most critical metrics from a business perspective, churn prediction identifies the percentage of customers who stop using a brand’s services or products within a specific period. Lower churn rates indicate stronger customer retention and predictive AI models can help brands identify customers at risk of leaving and proactively engage them.
  • Customer Lifetime Value (CLV) Prediction—CLV prediction helps brands understand the long-term value of customer relationships. By identifying high-value customers, brands can create targeted strategies to retain and nurture these key groups, maximizing their contributions over time. Often, a small percentage of engaged customers generate the highest profits, so it’s essential to recognize and prioritize these relationships.
  • Product Recommendation & Next Transaction Prediction—AI-driven product recommendations can enhance the customer experience by tailoring suggestions based on behaviors like “customers who bought XYZ also bought ABC” or “customers like you might like XYZ.” These insights encourage more meaningful interactions with the brand.
  • Send-Time Optimization—AI can determine the best times to send communications to customers based on their habits and behaviors, maximizing engagement by reaching customers when they’re most likely to respond.
  • Loyalty Fraud Prevention—AI can also help prevent loyalty program fraud by detecting unusual patterns in member behavior, safeguarding the program’s integrity and ensuring rewards go to genuine customers.

Invest time in building a solid foundation of responsible data practices for the right AI or loyalty service provider with AI expertise. This is the key to creating loyalty programs that truly connect with customers and inspire long-term loyalty.

Editor’s Note

We have tapped a wide range of resources to create this series on Personalization and Customer Loyalty. At the center of our inspiration is the new eBook from Comarch How AI Personalization Drives Customer Loyalty, which you can download here. We encourage you to read this eBook and combine its findings with fresh thinking in this series to accelerate your strategy to apply AI to your customer strategy.