Stripe MRR Report: Automated Churn & Revenue Tracking

Master Monthly Recurring Revenue analysis to understand subscription growth, customer retention, and revenue trends in your Stripe account.

Introduction to MRR Analysis

If you're running a subscription-based business with Stripe, one question keeps you up at night: "What is my MRR?" Monthly Recurring Revenue (MRR) is the lifeblood of subscription businesses, providing a clear picture of predictable income and business health.

Unlike one-time sales, MRR represents the normalized monthly value of all your active subscriptions. It's the single most important metric for understanding whether your business is growing, stagnating, or declining. This tutorial will walk you through analyzing your MRR in Stripe, answering critical questions like:

By the end of this guide, you'll have a complete understanding of your subscription metrics and be equipped to make data-driven decisions that accelerate growth.

Prerequisites and Data Requirements

Before diving into MRR analysis, ensure you have the following in place:

Required Access

Recommended Timeline

Technical Knowledge

What You'll Accomplish

This tutorial guides you through a complete MRR analysis workflow. You'll learn to:

  1. Calculate your current MRR from Stripe subscription data
  2. Track the number of active subscriptions over time
  3. Compute your monthly churn rate
  4. Identify which pricing plans contribute most to revenue
  5. Visualize trends and make strategic decisions

Each step includes practical examples with expected outputs so you can verify your analysis is correct.

Step 1: What Is My Current MRR?

MRR is the sum of all recurring revenue normalized to a monthly amount. This means annual subscriptions are divided by 12, quarterly by 3, and monthly subscriptions are counted as-is.

Using the Stripe Dashboard

  1. Log into your Stripe dashboard at dashboard.stripe.com
  2. Navigate to Billing > Subscriptions
  3. Click on Overview to see your MRR displayed prominently
  4. Note the current MRR value and the trend direction (up/down)

Calculating MRR Manually

For a deeper understanding or custom analysis, you can calculate MRR using the Stripe API:

import stripe
from datetime import datetime

stripe.api_key = 'your_secret_key'

# Fetch all active subscriptions
subscriptions = stripe.Subscription.list(
    status='active',
    limit=100
)

total_mrr = 0

for sub in subscriptions.auto_paging_iter():
    # Get the subscription amount
    amount = sub['items']['data'][0]['price']['unit_amount']
    interval = sub['items']['data'][0]['price']['recurring']['interval']

    # Normalize to monthly
    if interval == 'month':
        monthly_value = amount
    elif interval == 'year':
        monthly_value = amount / 12
    elif interval == 'quarter':
        monthly_value = amount / 3
    elif interval == 'week':
        monthly_value = amount * 4.33

    total_mrr += monthly_value

# Convert from cents to dollars
total_mrr = total_mrr / 100

print(f"Current MRR: ${total_mrr:,.2f}")

Expected Output

Current MRR: $24,567.50

Understanding the Result

Your MRR represents the predictable monthly revenue from subscriptions. For example, if your MRR is $24,567.50, you can expect approximately this amount each month, assuming no changes in subscriptions. Track this metric weekly or monthly to identify growth trends.

For automated MRR tracking and analysis, consider using specialized tools like the MCP Analytics MRR Analysis tool, which provides real-time dashboards and trend analysis without coding.

Step 2: How Many Active Subscriptions Do I Have?

Understanding your subscription count helps you assess customer base growth and calculate important metrics like average revenue per user (ARPU).

Via Stripe Dashboard

  1. Navigate to Billing > Subscriptions
  2. Apply filter: Status = Active
  3. The total count appears at the top of the subscription list
  4. Note subscriptions in "trialing" status separately as they represent future MRR

Programmatic Count

import stripe

stripe.api_key = 'your_secret_key'

# Count active subscriptions
active_subs = stripe.Subscription.list(status='active', limit=1)
active_count = active_subs['total_count']

# Count trialing subscriptions
trial_subs = stripe.Subscription.list(status='trialing', limit=1)
trial_count = trial_subs['total_count']

print(f"Active Subscriptions: {active_count}")
print(f"Trial Subscriptions: {trial_count}")
print(f"Total: {active_count + trial_count}")

Expected Output

Active Subscriptions: 487
Trial Subscriptions: 23
Total: 510

Key Insights

Calculate your Average Revenue Per User (ARPU): MRR ÷ Active Subscriptions. In our example: $24,567.50 ÷ 487 = $50.45 ARPU. This metric helps you understand the average value of each customer.

Step 3: What Is My Churn Rate?

Churn rate measures the percentage of customers who cancel their subscriptions during a given period. It's one of the most critical metrics for subscription businesses because high churn directly undermines growth.

Understanding Churn Types

Calculating Monthly Customer Churn

The formula: (Customers Lost During Month ÷ Customers at Start of Month) × 100

import stripe
from datetime import datetime, timedelta

stripe.api_key = 'your_secret_key'

# Define time period (last month)
today = datetime.now()
month_start = (today.replace(day=1) - timedelta(days=1)).replace(day=1)
month_end = today.replace(day=1) - timedelta(days=1)

# Get customers at start of month
start_timestamp = int(month_start.timestamp())
customers_start = stripe.Subscription.list(
    status='active',
    created={'lte': start_timestamp},
    limit=1
)['total_count']

# Get canceled subscriptions during the month
canceled = stripe.Subscription.list(
    status='canceled',
    canceled_at={
        'gte': int(month_start.timestamp()),
        'lte': int(month_end.timestamp())
    },
    limit=1
)['total_count']

# Calculate churn rate
churn_rate = (canceled / customers_start) * 100

print(f"Customers at Start: {customers_start}")
print(f"Cancellations: {canceled}")
print(f"Monthly Churn Rate: {churn_rate:.2f}%")

Expected Output

Customers at Start: 503
Cancellations: 18
Monthly Churn Rate: 3.58%

Interpreting Churn Rate

Churn Rate Assessment Action Required
< 2% Excellent Maintain current retention strategies
2-5% Good Monitor trends, optimize onboarding
5-7% Concerning Investigate cancellation reasons, improve product value
> 7% Critical Immediate action needed, customer interviews, product-market fit review

In the example above, a 3.58% churn rate is within the acceptable range but should be monitored closely. Consider implementing exit surveys to understand why customers leave.

Understanding statistical significance in your churn analysis is crucial. Learn more about statistical significance in A/B testing to ensure your retention experiments yield reliable results.

Step 4: Which Plans Are Most Popular?

Analyzing plan distribution helps you understand which offerings resonate with customers and drive revenue. This insight informs pricing strategy and product development.

Via Stripe Dashboard

  1. Go to Products > Pricing
  2. Each price shows the number of active subscriptions
  3. Sort by "Subscriptions" to see most popular plans

Detailed Plan Analysis

import stripe
from collections import defaultdict

stripe.api_key = 'your_secret_key'

plan_stats = defaultdict(lambda: {'count': 0, 'mrr': 0})

# Fetch all active subscriptions
subscriptions = stripe.Subscription.list(status='active', limit=100)

for sub in subscriptions.auto_paging_iter():
    price = sub['items']['data'][0]['price']
    plan_name = price['nickname'] or price['id']
    amount = price['unit_amount']
    interval = price['recurring']['interval']

    # Normalize to monthly
    if interval == 'month':
        monthly_value = amount
    elif interval == 'year':
        monthly_value = amount / 12

    plan_stats[plan_name]['count'] += 1
    plan_stats[plan_name]['mrr'] += monthly_value / 100

# Sort by MRR
sorted_plans = sorted(plan_stats.items(),
                     key=lambda x: x[1]['mrr'],
                     reverse=True)

print(f"{'Plan Name':<30} {'Subscriptions':<15} {'MRR':<15}")
print("-" * 60)
for plan, stats in sorted_plans:
    print(f"{plan:<30} {stats['count']:<15} ${stats['mrr']:>12,.2f}")

Expected Output

Plan Name                      Subscriptions   MRR
------------------------------------------------------------
Professional Plan              187             $14,025.00
Starter Plan                   245             $ 7,350.00
Enterprise Plan                 32             $ 4,800.00
Basic Plan                     123             $ 1,845.00

Strategic Insights

From this analysis, you can identify:

For comprehensive subscription analysis with automated insights, explore the MRR Analysis service, which provides detailed breakdowns across all pricing dimensions.

Interpreting Your Results

Now that you've gathered your MRR data, active subscription counts, churn rate, and plan distribution, it's time to synthesize these metrics into actionable insights.

Calculating Key Performance Indicators

1. MRR Growth Rate

Formula: ((Current Month MRR - Previous Month MRR) ÷ Previous Month MRR) × 100

Example:
Current MRR: $24,567.50
Previous MRR: $23,100.00
Growth Rate: ((24,567.50 - 23,100.00) / 23,100.00) × 100 = 6.35%

2. Net MRR Churn

This accounts for both lost revenue (downgrades/cancellations) and gained revenue (upgrades/expansion):

Net MRR Churn = ((Churned MRR - Expansion MRR) ÷ Starting MRR) × 100

Example:
Starting MRR: $23,100.00
Churned MRR: $1,850.00
Expansion MRR: $3,317.50
Net MRR Churn: ((1,850 - 3,317.50) / 23,100) × 100 = -6.35% (negative is good!)

3. Customer Lifetime Value (LTV)

Estimate how much revenue a customer generates over their lifetime:

LTV = ARPU ÷ Monthly Churn Rate

Example:
ARPU: $50.45
Churn Rate: 3.58% (0.0358)
LTV: $50.45 ÷ 0.0358 = $1,409.22

Health Check Framework

Use this framework to assess your subscription business health:

Metric Healthy Range Warning Signs
MRR Growth Rate > 10% monthly < 5% for 3+ months
Customer Churn < 5% monthly > 7% or increasing trend
Net MRR Churn < 0% (negative) > 2% positive
LTV:CAC Ratio > 3:1 < 2:1

Trend Analysis

Single-point measurements only tell part of the story. Track these metrics over time:

Advanced analytics techniques, similar to those used in AI-first data analysis pipelines, can help you uncover hidden patterns in your subscription data and predict future trends.

Automate Your MRR Analysis

Manual MRR analysis provides valuable insights, but tracking these metrics continuously requires significant effort. The MCP Analytics MRR Analysis Tool automates this entire workflow, providing:

Try MRR Analysis Now

Connect your Stripe account and get instant insights into your subscription metrics. No coding required.

Launch MRR Analysis Tool →

Common Issues and Solutions

Issue 1: MRR Doesn't Match Revenue

Problem: Your calculated MRR differs from actual revenue received.

Cause: MRR represents normalized recurring revenue, not actual cash flow. One-time charges, failed payments, and billing timing create discrepancies.

Solution:

Issue 2: Churn Rate Seems Too High

Problem: Your churn rate exceeds 7-10% monthly.

Cause: Poor product-market fit, inadequate onboarding, pricing issues, or competitive pressure.

Solution:

Issue 3: Subscription Count Increasing, But MRR Flat

Problem: You're acquiring customers but revenue isn't growing proportionally.

Cause: New customers choosing lower-tier plans while high-value customers churn (negative revenue mix).

Solution:

Issue 4: API Rate Limits When Fetching Subscriptions

Problem: Stripe API returns rate limit errors when processing many subscriptions.

Cause: Exceeding Stripe's API rate limits (typically 100 requests per second).

Solution:

import stripe
import time

stripe.api_key = 'your_secret_key'

def fetch_all_subscriptions():
    subscriptions = []
    has_more = True
    starting_after = None

    while has_more:
        try:
            result = stripe.Subscription.list(
                limit=100,
                starting_after=starting_after
            )
            subscriptions.extend(result['data'])
            has_more = result['has_more']
            if has_more:
                starting_after = result['data'][-1]['id']
                time.sleep(0.1)  # Rate limiting
        except stripe.error.RateLimitError:
            time.sleep(1)  # Wait before retrying
            continue

    return subscriptions

Issue 5: Difficulty Tracking MRR Changes Over Time

Problem: Understanding what drove MRR changes requires manual investigation.

Cause: Stripe doesn't provide built-in MRR movement categorization.

Solution: Implement MRR movement tracking:

Use Stripe webhooks to track subscription lifecycle events and categorize MRR changes in real-time.

Next Steps with Stripe Analytics

You've now mastered the fundamentals of MRR analysis in Stripe. Here are advanced topics to explore next:

1. Cohort Retention Analysis

Track how customer cohorts (grouped by signup month) retain over time. This reveals whether your product is improving at retaining customers and helps predict long-term value.

2. Revenue Forecasting

Use historical MRR growth, churn rates, and seasonality to project future revenue. This is critical for fundraising, hiring plans, and strategic planning.

3. Advanced Segmentation

Segment your MRR analysis by:

4. Churn Prediction

Build machine learning models to identify at-risk customers before they cancel. Techniques like Accelerated Failure Time (AFT) models can help predict subscription lifetime based on customer behavior.

5. Pricing Optimization

Experiment with pricing changes using controlled tests. Understand how to apply ensemble learning methods like AdaBoost to optimize pricing based on customer characteristics and behavior patterns.

6. Payment Recovery

Analyze failed payment patterns and implement dunning strategies to recover revenue from involuntary churn (expired cards, insufficient funds).

Recommended Resources

Conclusion

Understanding your MRR is fundamental to operating a successful subscription business. By systematically analyzing your current MRR, active subscriptions, churn rate, and plan distribution, you gain the insights needed to make informed decisions about growth, retention, and product strategy.

The metrics you've learned to calculate—MRR growth rate, customer churn, ARPU, and LTV—form the foundation of subscription business analytics. Track these consistently, identify trends early, and take action when metrics signal opportunity or risk.

Remember that MRR analysis is not a one-time exercise but an ongoing discipline. Set up weekly or monthly reviews, automate data collection where possible, and share insights across your organization. The businesses that win in subscription markets are those that deeply understand their metrics and act on them quickly.

Ready to take your analysis to the next level? Try the automated MRR Analysis tool to track these metrics in real-time with no code required.

Explore more: Stripe Analytics — all tools, tutorials, and guides →