Customer churn prediction

We worked with a telecommunications company to develop a predictive model that could identify customers who were likely to churn. By analyzing historical data on customer behavior and engagement, we were able to develop a model that could accurately predict which customers were at risk of leaving. The company was able to use this information to develop targeted retention strategies and reduce churn by 25%.

Fraud detection

We worked with a financial institution to develop an AI-powered fraud detection system. The system used machine learning algorithms to analyze transactional data and identify patterns of fraudulent activity. This helped the company to detect and prevent fraud in real-time, saving millions of dollars in losses.

Inventory optimization

We worked with a retail company to develop an AI-powered inventory optimization system. The system used predictive analytics to forecast demand for different products and optimize inventory levels accordingly. This helped the company to reduce stockouts and overstocking, improving both customer satisfaction and profitability.

Personalized marketing

We developed a customer segmentation and recommendation system for an e-commerce company that allowed them to personalize their marketing efforts based on customer behavior. This helped the company to increase engagement and sales, improving the ROI of their marketing efforts.