Billing data is more than just a record of transactions; it’s a detailed story about how customers interact with your product. By connecting this data to Business Intelligence (BI) tools, you can move from simple reporting to deep, actionable insights that drive growth. This guide explains how to unlock that potential.

Billing analytics is the process of collecting, analyzing, and visualizing your subscription and payment data to understand customer behavior, product value, and revenue trends. Instead of just asking “How much money did we make?”, it helps you answer more powerful questions like:

• Which customer cohorts have the highest lifetime value (LTV)?
• What sequence of events precedes a customer churning or upgrading?
• Where are the friction points in our payment or upgrade process?
• Which features or plans are driving the most sustainable revenue?

By turning raw billing events into clear visualizations and dashboards, you empower your team to make data-informed decisions about product, marketing, and customer success.

Connecting your billing platform to a BI tool involves a few key steps. The goal is to create a reliable pipeline that moves data from where it’s generated to where you can analyze it.

1. Data Extraction: First, you need to get the data out of your billing system. Most modern platforms, including payment processors and services like Kinde, offer two primary methods:
   • APIs: Application Programming Interfaces allow you to programmatically request historical and real-time data. You can pull records on customers, subscriptions, invoices, and more.
   • Webhooks: This is the most effective method for real-time updates. Your billing platform sends an automated message (a webhook) to a specified URL endpoint whenever a specific event occurs, like subscription.created, invoice.payment_failed, or plan.changed.
2. Data Loading (ETL/ELT): The raw data, often in JSON format, is then sent to a central data repository. This is typically a cloud data warehouse like BigQuery, Snowflake, or Redshift. The process of Extracting, Transforming, and Loading (ETL) or Extracting, Loading, and Transforming (ELT) cleans and structures the data for analysis.
3. Data Visualization: Once in the data warehouse, you can connect a BI tool. Popular choices include:
   • Tableau
   • Power BI
   • Looker Studio (formerly Google Data Studio)
   • Metabase

These tools query the data warehouse and present the findings in interactive dashboards, charts, and reports, making complex data easy to understand.

Once your pipeline is set up, you can start uncovering powerful insights. This is where you can visualize churn journeys, identify valuable customer cohorts, and spot revenue leaks.

Here are some key analyses you can run:

• Cohort LTV Analysis: Group users by their sign-up month (a cohort) and track their cumulative revenue over time. This helps you see if product changes are leading to more valuable customers.
• Churn Diagnostics: Visualize the journey of users who cancel. Did they experience a failed payment first? Did they downgrade from a specific plan? This analysis reveals critical intervention points.
• Friction Point Identification: Create funnels for key actions, like the upgrade or checkout process. A significant drop-off at a specific step indicates friction that needs to be addressed.
• Revenue Tailwinds: Identify your most successful customer segments or acquisition channels. A BI tool can help you connect subscription data with marketing data to see which channels bring in users who stick around the longest.
• Revenue Leakage Detection: Dashboards can highlight failed payments, overdue invoices, and unrecovered revenue. By tracking these metrics actively, you can implement dunning strategies to recover what would otherwise be lost.

While powerful, building a billing analytics stack isn’t without its challenges. Here are a few common hurdles to be aware of.

To get the most out of your billing analytics, follow a few guiding principles. These best practices will help you build a system that delivers clear, reliable insights.

• Start with questions, not data: Before you build anything, define the specific business questions you want to answer. This focus ensures you only collect and analyze the data you truly need.
• Prioritize event-driven data: Use webhooks as your primary source of truth. They provide a real-time, immutable log of everything that happens, which is more reliable than polling an API for state changes.
• Enrich data at the source: Whenever possible, add context to your billing events. For example, when a user subscribes, include their acquisition channel or initial marketing campaign as metadata. This saves you from having to join tables later.
• Choose the right tools for your stage: You don’t need a massive, expensive data stack from day one. Start with simpler tools and evolve as your needs grow. The combination of webhooks, a simple data loader, and a tool like Looker Studio can take you very far.

Kinde is designed to be a core part of a modern data stack, providing the clean, real-time billing events you need to power your analytics.

Instead of having to build and maintain complex integrations with a payment processor like Stripe, Kinde provides a structured layer on top. It emits a rich set of billing-related webhooks for events like customer.plan_changed, customer.payment_failed, and customer.payment_succeeded.

This approach simplifies the “Extract” step of your data pipeline. You can point a Kinde webhook to your data endpoint and instantly start receiving well-formed, predictable event data. By handling the direct interaction with the payment layer, Kinde ensures the data you receive is clean and ready for your BI tools, letting you focus on generating insights, not on data plumbing.

To learn more about how to set up and use webhooks in Kinde, check out the documentation.

• About webhooks
• Add and manage webhooks