Your Best Customers Are
Leaving. Do You Know Which Ones?
By the time you notice churn in your revenue numbers, those customers are already gone. Upload your transaction data and get churn risk scores, retention curves, and a list of who to save—before it's too late.
Why Churn Catches You Off Guard
Acquiring a new customer costs 5–7x more than retaining one. Yet most businesses only measure churn after the fact.
Early Warning Signals
Declining purchase frequency, shrinking order values, growing gaps between visits. Statistical models detect these patterns weeks before a customer goes silent.
Segment-Level Churn Rates
Not all churn is equal. See which customer segments, acquisition channels, and product lines have the highest attrition—so you fix the right problems first.
Revenue at Risk
Churn isn't just a percentage—it's dollars. See exactly how much revenue your at-risk customers represent, so you can justify the retention investment.
Export. Upload. Save Customers.
From transaction data to churn predictions in 3 minutes
Export Your Customer Data
Download your transaction history or subscription records from Shopify, Stripe, your CRM, or any system as CSV. Include customer ID, dates, and transaction values for the richest analysis.
Upload Your CSV
Drop the file into MCP Analytics. The system auto-detects customer IDs, dates, and monetary values. No manual column mapping or data cleaning needed.
Get Churn Predictions
Choose an analysis or ask a question. Get an interactive report with churn risk scores, at-risk customer lists, retention curves, and AI-written recommendations for your retention strategy.
Churn & Retention Analyses
Every analysis runs on your actual data and produces a shareable report
Churn Risk Scoring
Each customer gets a churn probability based on their purchase patterns. Sort by risk to find who needs attention right now.
Retention Curves
See how quickly customers drop off after their first purchase. Compare retention across cohorts, channels, and product lines.
Cohort Analysis
Track each acquisition cohort over time. Is last month's cohort retaining better than three months ago? See the trend.
Survival Analysis
Statistical survival curves show the probability of a customer remaining active at each time point. Median customer lifetime, hazard rates, and survival by segment.
RFM Segmentation
Segment customers by Recency, Frequency, and Monetary value. Identify champions, at-risk, hibernating, and lost customers automatically.
Revenue at Risk
Calculate the dollar value of customers flagged as high churn risk. Quantify what you stand to lose without intervention.
Repeat Purchase Analysis
What percentage of customers come back? Track first-to-second purchase conversion and repeat rates by cohort and segment.
Inter-Purchase Timing
How long between purchases is normal? Detect customers whose gaps are growing—a leading indicator of churn.
Churn Driver Analysis
Which factors predict churn? Statistical models identify whether it's price, product mix, support interactions, or acquisition channel driving attrition.
See What You'll Get
Example output from a churn and retention analysis
Key Insights
412 customers show high churn risk (>70% probability)
These customers haven't purchased in 45+ days and their purchase frequency has declined by 60%. Collectively they represent $89K in projected annual revenue.
Paid search cohort churns 2.3x faster than organic
Customers acquired via paid search have a 47-day median lifetime vs. 108 days for organic. This suggests paid traffic is attracting deal-seekers rather than loyal customers.
Second purchase within 14 days predicts 3x higher retention
Customers who make a second purchase within two weeks of their first have a 78% chance of becoming long-term buyers. Consider a post-purchase incentive in this window.
MCP Analytics vs Manual Churn Tracking
What you gain beyond counting lost customers
Churn Analysis FAQ
What data do I need for churn analysis?
At minimum, you need a CSV with customer ID and transaction or activity dates. For richer analysis, include transaction amounts, product categories, subscription status, and support interactions. MCP Analytics works with exports from Shopify, Stripe, WooCommerce, Chargebee, or any CRM.
How does churn prediction work without machine learning expertise?
MCP Analytics runs the statistical models automatically. It analyzes purchase recency, frequency trends, and inter-purchase intervals to calculate a churn probability for each customer. Results are presented in plain English with specific recommendations, not raw model outputs.
Can I use this for subscription businesses?
Yes. Upload your subscription billing data and get MRR churn rates, cohort survival curves, and predicted churn timing. The analysis identifies which subscription tiers, acquisition channels, or customer segments have the highest churn risk.
How early can churn be predicted?
It depends on your data depth. With 6+ months of transaction history, MCP Analytics can flag at-risk customers 30–60 days before they would typically churn. Early warning signals include declining purchase frequency, decreasing order values, and growing gaps between transactions.
What's the difference between churn analysis and retention analysis?
Churn analysis focuses on predicting which individual customers will leave and why. Retention analysis looks at aggregate patterns: how cohorts retain over time, what your overall retention curve looks like, and whether retention is improving. MCP Analytics provides both in a single report.
Churn & Retention Resources
Guides and deep-dives for understanding and reducing churn
Ready to Stop Losing Customers You Could Have Saved?
Upload your transaction data and get churn predictions in under 3 minutes. No credit card required.