ChurnShield uses advanced RFM (Recency, Frequency, Monetary) scoring to identify at-risk customers before they leave. Get actionable churn risk scores in 3 minutes, no data science required.
ChurnShield analyzes three critical dimensions of customer behavior to calculate churn probability. Each dimension tells a part of the story.
When did they last purchase? Longer gaps between purchases signal declining interest and fading engagement. Customers who haven't bought recently are more likely to churn.
How often do they buy? Decreasing purchase frequency means fading loyalty. A customer who used to buy monthly but now buys quarterly is showing churn signals.
How much do they spend? Declining spend per visit predicts churn. Customers reducing their basket size are often mentally disengaging before they leave entirely.
Purpose-built for small and mid-size businesses who want churn intelligence without the complexity.
Catch at-risk customers weeks before they leave, not after they're already gone.
Each customer gets a 0-100% churn probability score. No ambiguity, just data.
No Python, no ML models to train, no technical expertise. Just upload a CSV.
Your data never leaves your computer. ChurnShield runs entirely locally as a desktop app.
Know exactly what discount keeps each customer without hurting your margins.
See total revenue at risk across your entire customer base at a glance.
From CSV to churn insights in three simple steps.
Export your transaction history. All you need is customer ID, date, and amount.
RFM patterns are analyzed and churn probability is calculated for every customer.
See who's at risk and take action with personalized discount recommendations.
Stop guessing which customers are about to leave. ChurnShield's churn prediction model gives you the data you need to act before it's too late.
Version 1.0.1 • Windows
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