Machine Learning
"We're Losing Customers and We Don't Know Why."
15%
Churn Reduction
85%
Prediction Accuracy
The Challenge
A subscription box service was struggling with high customer churn. They were unable to identify which users were at risk of canceling their subscriptions.
Our Solution
We developed a predictive churn model that analyzed user behavior patterns. The system identifies at-risk customers with 85% accuracy and automatically flags them for a proactive retention campaign.
The Outcome
Monthly churn was reduced by 15% in the first quarter. The targeted retention campaigns led to a measurable increase in overall customer lifetime value.
Tech Stack & Services
Machine Learning & AI ModelsData Engineering