A national supermarket chain ran a loyalty program with over 8 million members, but its data was underutilized. While they had transaction histories, redemption rates, and demographic info, insights were locked behind manual SQL queries and static reports.

As a result, marketing teams struggled to identify high-value customers, predict churn, or deliver personalized offers at scale. Campaigns were often generic, leading to low engagement and poor redemption outcomes. Customers who expected real-time, tailored rewards instead received mass promotions, diminishing the perceived value of the loyalty program.

They needed an AI-powered way to transform loyalty data into actionable customer engagement strategies, shifting from descriptive reporting to predictive and prescriptive intelligence

The Challenge

The client’s loyalty and rewards programs were limited by static segmentation and low personalization, resulting in declining engagement and missed growth opportunities.

Key pain points included:

• Static & Broad Segmentation: Customers were grouped into generic categories, limiting targeted campaigns.

• Low Personalization: Offers and rewards were not tailored to individual preferences or behavior.

• Declining Loyalty Engagement: Reduced program participation over time affected repeat purchases.

• Poor Predictive Insights: Difficulty in forecasting churn and customer lifetime value hindered proactive retention strategies.

• Slow Campaign Adaptation: Marketing teams struggled to adjust campaigns quickly in response to trends or seasonal changes.

The client needed a data-driven, AI-powered loyalty management solution capable of delivering personalized experiences, predicting behavior, and improving engagement at scale.

Amantra Loyalty Intelligence Engine

Amantra deployed an LLM-powered analytics agent trained on the retailer’s loyalty program data to drive dynamic segmentation, personalization, and actionable insights.

Key Capabilities:

• Dynamic Customer Segmentation: Grouped customers in real time based on behavior, spend patterns, and preferences, replacing static, broad categories.

• Predictive Analytics: Forecasted customer lifetime value (LTV) and identified churn risk to enable proactive retention strategies.

• Personalized Promotions & Rewards: Recommended tailored offers and reward structures to maximize engagement and repeat purchases.

• Cross-Sell & Upsell Identification: Highlighted high-potential opportunities to increase basket size and revenue.

• Natural Language Campaign Insights: Generated easy-to-understand performance reports and recommendations for marketing teams.

This solution enabled highly personalized, data-driven loyalty programs, improving engagement, reducing churn, and increasing overall customer value.

Results

Testimonial

Why AI for Loyalty Data?