Challenge

Blaze relies on Stripe to power payments and subscription billing. As the company scaled, it quickly outgrew standard dashboard reporting and needed deeper visibility into revenue, churn, and retention.

Parsing this data fell largely to John Snyder, Blaze’s head of analytics—a team of one supporting product, marketing, finance, and the executive team. With limited bandwidth, Snyder needed to focus his time on analysis, but instead spent much of it on manual data preparation. This included exporting data, stitching data from multiple systems, and manually ingesting it into Blaze’s Snowflake data warehouse. These time-consuming workflows slowed time to insights and raised data reliability concerns.

With new products launching and a rapidly growing user base, Blaze needed a faster, more scalable way to explore its data and ensure decisions were grounded in accurate insights. The company required a solution that could manage its growing data needs without increasing the burden on its small analytics team.

Solution

Blaze chose to implement Stripe Sigma and Data Pipeline.

Blaze uses Stripe Sigma to quickly query Stripe Billing data and build custom reports using SQL directly in the Dashboard. Snyder often starts with Stripe Sigma’s prebuilt SQL templates—such as active subscriber growth, MRR growth over time, and subscriber churn rate—and customizes them by slicing data across different dimensions to understand what’s impacting performance. These templates give him a fast starting point for analysis, and because Stripe Sigma runs queries directly against Stripe’s source of truth, the team can trust the accuracy of every report.

To eliminate manual data movement, Blaze implemented Data Pipeline to automatically sync Stripe data to Snowflake on an ongoing basis. Within 24 hours, Snyder completed the setup, and all of Blaze’s Stripe data was accessible in its data warehouse—with no ongoing engineering work needed.

“Our recent Stripe data is always available for access, which is really important to us because we operate with a lot of urgency and like to move quickly as possible. Data Pipeline also gives me confidence in our data—everything syncs seamlessly and it’s secure,” said Snyder.

In addition to automating data delivery, Data Pipeline provides Blaze with analytics-ready tables and curated datasets exclusive to Data Pipeline—which speed up reporting and analysis. Snyder relies heavily on the subscription items change events dataset, which provides a clean, structured view of each subscription item’s MRR change.

Results

Stripe Sigma and Data Pipeline help identify ideal customer profiles and reduce cost of acquisition by 25%

Using Stripe Sigma and Data Pipeline together, Snyder can now identify Blaze’s highest-value customer cohorts. He starts this analysis in Stripe Sigma, using the subscriber churn rate over time and ARPU prebuilt SQL templates. After modifying these templates, he pastes the queries to Snowflake and enriches them with additional Stripe data delivered through Data Pipeline, alongside customer persona data from other systems.

By unifying these datasets, Snyder has calculated trial-to-paid conversion, retention, and LTV across different cohorts—revealing which customers deliver the most long-term value. These insights helped Blaze define its ideal customer profiles and optimize its marketing and product positioning, ultimately driving a 25% reduction in customer acquisition cost.

Curated subscription datasets generate deeper product and revenue insights

With Data Pipeline’s curated, analytics-ready datasets—such as the subscription items change events table—Snyder can now analyze subscription behavior and revenue changes far more quickly. The dataset enabled him to easily measure month-over-month subscription retention and diagnose the performance of Blaze’s new Autopilot product, where he identified a 30% improvement in retention. He also used this dataset to understand what was driving revenue shifts—whether growth came from new subscriptions, reduced churn, or both—and discovered that a meaningful share of new revenue was coming from customers purchasing multiple subscriptions.

Customer lifecycle analysis surfaces insights to improve Blaze’s new Autopilot product

By centralizing Stripe data with product usage, marketing, and CRM datasets in Snowflake, Snyder created a unified view of how users progress through Blaze’s new Autopilot product—from free trial sign-up, to conversion, retention, and churn. This allowed him to analyze the customer lifecycle and understand which behaviors correlated with stronger subscription outcomes.

One full day of monthly work is eliminated with automated financial reporting

With Data Pipeline ensuring Blaze’s Stripe data is continuously available in Snowflake and centralized with all other business data, Snyder built an automated monthly revenue report for the accounting team to calculate deferred revenue, along with other financial reports. What previously required a full day of manual work each month now runs automatically—freeing his time to focus on higher-impact work.

Faster, clearer visibility into factors impacting key SaaS metrics

Stripe Sigma gives Blaze a faster way to explore and understand what’s influencing MRR, churn, and retention. For example, Snyder used the “Subscription metrics per day (MRR roll-forward)” prebuilt SQL template to view daily MRR and subscriber changes, then modified the query to break the data down by product, cohort, and other dimensions. This helped pinpoint which products were driving new subscriptions and which were contributing to churn—informing Blaze’s growth and churn-reduction strategies.