Building Custom Analytics Dashboards
Create custom dashboards to visualize key metrics like MRR, ARPU, churn rate, and customer lifetime value using Stripe data.
Building Custom Analytics Dashboards is a free Stripe Payments & SaaS Billing Systems lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Stripe Payments & SaaS Billing Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Unlock Data's Potential
Welcome to building custom analytics dashboards! While Stripe provides basic reports, creating your own dashboard lets you dive deeper into your business's health.
You can combine Stripe data with other sources and visualize key metrics tailored to your needs for better decision-making.
Core SaaS Metrics Defined
Understanding your business performance starts with key metrics often used in SaaS:
- MRR (Monthly Recurring Revenue): Predictable revenue from subscriptions each month.
- ARPU (Average Revenue Per User): Average revenue generated by each active customer.
- Churn Rate: Percentage of customers who cancel or don't renew their subscriptions.
- LTV (Customer Lifetime Value): Total revenue expected from a customer over their relationship with your business.
Getting Data from Stripe API
To build custom dashboards, you'll need raw data from Stripe. The Stripe API is your primary source!
Key API endpoints you'll often use include /v1/customers, /v1/subscriptions, and /v1/invoices. These provide the details needed for calculating your metrics.
Fetching Subscriptions (Python)
Let's see how to fetch a list of subscriptions using the Stripe Python library. This forms the basis for many calculations.
Remember to replace YOUR_SECRET_KEY with your actual Stripe secret key for testing.
import stripe
stripe.api_key = "sk_test_YOUR_SECRET_KEY"
def get_subscriptions():
try:
# Fetch up to 3 subscriptions
subscriptions = stripe.Subscription.list(limit=3)
for sub in subscriptions.data:
print(f"ID: {sub.id}, Status: {sub.status}")
except stripe.error.StripeError as e:
print(f"Error: {e}")
if __name__ == "__main__":
get_subscriptions()Calculating Monthly Recurring Revenue
MRR is crucial for forecasting your business's financial health. To calculate it, you sum up the recurring revenue from all active subscriptions.
For each subscription, you'll look at its associated price.unit_amount and quantity. Make sure to convert currency units (e.g., cents to dollars) and adjust for different billing periods if needed.
Simple MRR Calculation (Python)
Here's a simplified Python example to calculate MRR from a list of subscriptions. It sums up the recurring amount for active subscriptions.
This example assumes a single currency and monthly billing for simplicity.
import stripe
stripe.api_key = "sk_test_YOUR_SECRET_KEY"
def calculate_mrr():
total_mrr = 0
try:
# Fetch active subscriptions
subscriptions = stripe.Subscription.list(status='active', limit=100)
for sub in subscriptions.data:
for item in sub.items.data:
# Assuming monthly interval and USD cents
if (item.price and item.price.recurring and
item.price.recurring.interval == 'month'):
amount = item.price.unit_amount * item.quantity / 100
total_mrr += amount
print(f"Total MRR: ${total_mrr:.2f}")
except stripe.error.StripeError as e:
print(f"Error: {e}")
if __name__ == "__main__":
calculate_mrr()Calculating Average Revenue Per User
ARPU helps you understand the average value of your customers. It's calculated by dividing your total MRR by the number of active customers.
ARPU = Total MRR / Number of Active Customers
Monitoring ARPU can highlight changes in your customer base or the effectiveness of your pricing strategies.
Visualizing Your Metrics
Once you have your calculated metrics, the next step is visualization! Tools like Matplotlib, Seaborn (for Python), or dedicated Business Intelligence (BI) platforms can turn raw numbers into insightful charts.
Think about using line charts for trends (e.g., MRR over time), bar charts for comparisons, and pie charts for composition.
Dashboard Best Practices
To make your custom dashboards truly effective:
- Keep it focused: Each dashboard should tell a clear story or answer specific business questions.
- Simplify: Avoid clutter. Use clear labels, intuitive layouts, and minimal text.
- Be actionable: Can users make informed decisions based on what they see?
- Update regularly: Ensure your data is fresh and reflects the current state of your business.
Quiz: Dashboard Components
Which of the following are key SaaS metrics you'd typically track on a custom dashboard, and which Stripe API endpoint is essential for fetching subscription data?
Recap: Dashboards for Insight
Great job! You've learned how custom analytics dashboards can transform your understanding of Stripe data. We covered core SaaS metrics like MRR and ARPU, how to fetch data using the Stripe API, and best practices for visualization.
Continue exploring Stripe's rich API and various visualization tools to uncover even more insights for your business!
Frequently asked questions
Is the “Building Custom Analytics Dashboards” lesson free?
Yes — the full text of “Building Custom Analytics Dashboards” is free to read here on the web, and the Stripe Payments & SaaS Billing Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Stripe Payments & SaaS Billing Systems course, upgrade to CoddyKit PRO.
What will I learn in “Building Custom Analytics Dashboards”?
Create custom dashboards to visualize key metrics like MRR, ARPU, churn rate, and customer lifetime value using Stripe data. You practise Stripe Payments & SaaS Billing Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Stripe Payments & SaaS Billing Systems?
No prior experience is required. Stripe Payments & SaaS Billing Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Building Custom Analytics Dashboards” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Stripe Payments & SaaS Billing Systems lesson?
Yes. Every Stripe Payments & SaaS Billing Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Advanced Financial Reporting with Stripe Data
- Building Custom Analytics Dashboards
- Churn Prediction and Revenue Optimization
- Cohort Analysis and Customer Lifetime Value (LTV)