Inspiration
Businesses often struggle to understand why customers leave until it’s too late. Traditional loyalty programs are static and treat all customers the same. We wanted to build an AI-powered solution that helps businesses predict churn early and personalize engagement to strengthen customer relationships and increase retention.
What it does
Loyalytics analyzes customer behavior data — such as purchase frequency, total spend, last interaction, and satisfaction levels — to predict how likely each customer is to churn. It then recommends personalized retention actions, such as offering discounts, sending loyalty messages, or suggesting new products. A simple, intuitive dashboard shows churn risk scores, customer insights, and data-driven recommendations to help businesses take action in real time.
How we built it
Collected and structured sample customer data (transactions, interactions, and feedback). Trained a machine learning model (XGBoost) to predict churn probability. Developed a recommendation engine that maps risk scores to personalized actions. Created a web-based dashboard to visualize predictions and insights. Integrated data visualization tools to display customer trends and retention metrics.
Challenges we ran into
Balancing model accuracy with limited sample data.
Accomplishments that we're proud of
Built a fully functional AI retention prototype that turns raw customer data into actionable insights. Demonstrated measurable business value — helping companies anticipate churn before it happens.
What's next for Loyalytics
Integrate with CRM systems (like HubSpot or Salesforce) for real-time use. Add automated retention workflows via SMS, WhatsApp, or email APIs.
Log in or sign up for Devpost to join the conversation.