Smarter rewards: the AI revolution in loyalty programs and promotions

Brands like Starbucks and Puma are already showing what’s possible. Starbucks uses predictive analytics to tailor its rewards program, while Puma seamlessly synchronizes offers across online and offline channels.

The result? Stronger customer connections, higher retention, and a competitive edge.

Are you considering adding AI to your loyalty program or promotions strategy? Success starts with understanding its capabilities, its limits, and the critical role of high-quality data in driving results.

Let's start by looking at the differences between generative AI and predictive AI. If you're aiming to enhance your personalization efforts and incentives, success lies in knowing when and how to use each type to achieve the best results.

When combined, these models empower brands to deliver timely, relevant offers that resonate. 

Businesses are rapidly integrating AI into their personalization strategies. A recent Talon.One survey revealed that 65% of retailers are already leveraging AI to enhance customer experiences, while another 30% are actively exploring its potential. The takeaway? Personalization has shifted from a "nice-to-have" to a business necessity, with AI as a key enabler.

Regional adoption trends highlight some interesting disparities. The UK leads the charge, with 72% of retailers using AI for personalization, closely followed by Germany (70%) and Canada (66%). Surprisingly, the U.S. lags behind, with just 53% of retailers incorporating AI into their strategies.

Despite regional differences, AI’s transformative potential in retail personalization is clear. From tailoring product recommendations using demographic and behavioral data to automating the creation of personalized ads or shopping experiences, AI enables businesses to deliver relevance at scale. Tasks that once required extensive time and resources can now be executed in real time, driven by data insights.

This growing reliance on AI is reflected in enterprise spending. According to Forrester, 44% of enterprises plan to increase their data science and AI budgets by 1%-4%, while 36% are aiming for even greater increases of 5%-10% in the coming year. The momentum behind AI-powered personalization shows no signs of slowing down.

Generic discounts are more than outdated—they're a liability to profitability and brand value. They fail to engage customers meaningfully and often waste resources on offers that miss the mark. The path forward lies in personalized promotions. AI enables businesses to analyze purchase history, browsing habits, and demographics to craft offers that hit the mark. While it’s not flawless, this level of scalable precision wasn’t achievable with traditional methods.

Take Starbucks. Its Deep Brew AI platform uses customer data to personalize rewards for specific cohorts, driving higher spend and engagement. By aligning incentives with individual preferences, Starbucks has turned its rewards program into a loyalty powerhouse. The result? A 13% year-over-year growth in its US 90-day active rewards member base in Q1, 2024, reaching 34.3 million—an increase of 4 million members.

Predictive AI takes the guesswork out of designing loyalty programs by surfacing actionable insights from customer data.

For example, AI can predict:

These insights can go onto inform everything from program structure to financial planning, like accurately forecasting loyalty point liabilities to maintain profitability.

Retention is cheaper than acquisition, and AI excels at keeping customers loyal. Predictive analytics can pinpoint at-risk customers based on declining engagement, reduced purchase frequency, or cart abandonment.

Once identified, brands can deploy retention tactics like exclusive offers or tailored discounts. For instance, an ecommerce retailer might use AI to detect frequent cart abandoners and send personalized incentives to close the sale.

AI eliminates the inefficiencies of manual processes, using real-time data to design and manage thousands of campaigns and incentives with speed, accuracy, and scale.

Take Carrefour, for example. The grocery giant faced the challenge of discounting 40,000 SKUs daily across 83 Belgian stores. With an AI-driven tracking tool, Carrefour now streamlines stock management, optimizes discounts, and reduces food waste. The system predicts demand, automates pricing, and saves employees up to 60 minutes daily—all while boosting profitability.

AI is a key solution in further bridging the gap between digital and physical channels, enabling personalized experiences wherever customers shop.

Puma’s partnership with Google Cloud is a standout example. By using predictive AI, Puma aligns offers and rewards with individual preferences, whether online or in-store. This approach has increased the brand’s average order value by 19%.

AI is a transformative tool—but it’s not infallible. It works best as a complement to human expertise, not a replacement. Even the most sophisticated AI models require constant oversight to align with business goals.

Factors like data quality, system integration, and market dynamics also play a role. Without addressing these, AI can’t deliver the results businesses expect. Here are five critical questions to ask:

The future of loyalty and promotions is AI-enabled—but success depends on how you wield it. Predictive and generative AI can unlock deeper personalization, improve efficiency, and strengthen customer relationships.

However, the real game-changer isn’t the technology—it’s the data. By prioritizing high-quality data practices and aligning AI with clear objectives, brands can turn AI from a buzzword into a competitive advantage.