AI-Powered Churn Prediction for Trade Union
We built an AI-powered tool that predicts membership churn thereby enhancing member retention and financial stability. We harness historical data to predict churn and enable proactive measures.
noun /ˈbreɪk.θruː/
Solved for good
Improved Churn Prediction
Overcome limitations of simple statistical methods by utilizing AI to effectively predict membership churn.
Financial Stability for Unions
Minimize income losses due to membership churn and maintain financial stability.
Proactive Member Engagement
Empower unions to take proactive measures based on churn predictions, convincing members to stay.
A Union's Financial Pain Point
The Membership Churn Problem
Trade unions handle worker representation, negotiate better conditions, advocate for labor rights, and educate members. One major issue faced by such labor unions is membership churn, which negatively impacts their financial stability and causes uncertainty about member departures. This project began with the analysis of historical membership data and previous churn information to address these concerns.
Optimize Member Retention
AI-Powered Churn Prediction
Traditional statistical methods proved insufficient at predicting membership churn, resulting in financial drawbacks. Our AI-powered tool analyzed factors such as historical membership data and previous churns to more accurately identify employees likely to leave. By recognizing clusters with varied churn risks, the algorithm enables the prediction and empowers proactive measures to persuade members to remain with the union.
What can be solved?
Everything.
However here are a few common pain points that we often see, which can be solved through our programs and will lead to an AI breakthrough.
Get started with AI
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