Stop Treating All Customers
As If They're Worth the Same
Your top 20% of customers drive 80% of revenue—but which 20%? Upload your transaction data and get customer lifetime value segmentation, retention curves, and acquisition budget targets in minutes.
Why Average CLV Is Misleading
A single CLV number hides the customers who matter most—and the ones costing you money.
Customer Value Segmentation
Not all customers are equal. RFM analysis groups customers by recency, frequency, and monetary value so you can see exactly which segments drive profit and which drain resources.
Retention Curves That Predict Churn
See when customers drop off, by segment. Cohort retention analysis reveals whether your best customers are staying longer—or if you're churning your most valuable buyers.
Acquisition Budget You Can Defend
When you know a high-value customer is worth $500 over their lifetime, you can justify spending $100 to acquire them. CLV turns "marketing is expensive" into "marketing is an investment."
Export. Upload. Understand Your Customers.
From transaction data to CLV insights in 3 minutes
Export Your Transaction Data
Download your order history from Shopify, Stripe, WooCommerce, your CRM, or any system as CSV. Include customer ID, order date, and order value. More columns mean richer 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 CLV Insights
Choose an analysis or ask a question. Get an interactive report with customer segments, retention curves, revenue projections, and AI-written recommendations for your business.
Customer Value Analyses
Every analysis runs on your actual data and produces a shareable report
RFM Segmentation
Segment customers by Recency, Frequency, and Monetary value. Identify champions, loyal customers, at-risk accounts, and lost customers automatically.
Cohort Retention
Track how each acquisition cohort retains over time. See whether your latest customers stick around longer than older ones.
CLV Prediction
Probabilistic models estimate future customer value based on purchase history. Know which customers will be worth the most over the next 12 months.
Revenue Projection
Forecast total revenue from existing customers. See how much revenue your current customer base will generate without any new acquisition.
Churn Risk Scoring
Identify customers showing signs of churning before they leave. Flag at-risk accounts based on declining purchase frequency and recency gaps.
Purchase Pattern Analysis
Understand buying frequency, average order value trends, and inter-purchase timing across segments. Find the patterns that predict high-value behavior.
Segment Profiling
Deep-dive into each customer segment: what they buy, when they buy, how much they spend, and how long they stay. Tailor your marketing to each group.
CAC Benchmarking
Compare customer acquisition cost against lifetime value by segment and channel. Know exactly where acquisition spend generates positive ROI.
Repeat Rate Analysis
What percentage of customers come back? Track first-to-second purchase conversion, repeat rates by cohort, and time-to-repeat by segment.
See What You'll Get
Example output from a customer lifetime value analysis
Key Insights
Top 18% of customers generate 72% of total revenue
Champions and Loyal segments have 5x higher CLV than average. These customers purchase every 23 days on average vs. 67 days for the overall base.
At-risk segment shows recoverable revenue of $42K
312 previously active customers haven't purchased in 60+ days. Based on their historical CLV, a targeted re-engagement campaign could recover significant revenue.
Q4 acquisition cohort retains 22% better than Q3
Customers acquired during the holiday season show higher 90-day retention, suggesting seasonal buyers convert to repeat customers at a higher rate.
MCP Analytics vs Spreadsheet CLV
What you gain beyond a simple average
Customer Lifetime Value FAQ
What data do I need to calculate customer lifetime value?
At minimum, you need a CSV with customer ID, transaction date, and transaction amount. For richer analysis, include product category, acquisition channel, and customer demographics. MCP Analytics works with exports from Shopify, Stripe, WooCommerce, or any system that tracks orders.
How does MCP Analytics calculate CLV differently from a spreadsheet formula?
Spreadsheet CLV formulas use simple averages that treat all customers the same. MCP Analytics uses statistical models including RFM segmentation, cohort analysis, and probabilistic models that account for customer heterogeneity, purchase patterns, and churn probability. You get per-segment CLV, not a single misleading average.
Can I use CLV analysis for acquisition budget decisions?
Yes. The CLV report shows the expected value of each customer segment, so you can set acquisition cost targets per segment. If your high-value segment has a CLV of $500, you know you can afford to spend significantly more to acquire those customers than a segment with $50 CLV.
How many transactions do I need for accurate CLV analysis?
For basic CLV segmentation, 500+ transactions across 100+ customers is sufficient. For predictive modeling with cohort analysis, 1,000+ transactions over 6+ months provides more reliable projections. MCP Analytics will tell you if your dataset is too small for certain analyses.
Does this work for subscription businesses or just one-time purchases?
Both. For subscription businesses, upload your billing history and get MRR cohort analysis, churn curves, and projected LTV per cohort. For transaction-based businesses, get purchase frequency analysis, repeat rate curves, and predicted future spend per customer segment.
Customer Analytics Resources
Guides and deep-dives for understanding customer value
Guides
Related Analyses
Ready to Know What Your Customers Are Really Worth?
Upload your transaction data and get CLV segmentation in under 3 minutes. No credit card required.