Automate Your HubSpot and Stripe KPI Reporting

Most teams start with a Google Sheets connector like Supermetrics. It works for basic queries but hits API rate limits or query timeouts with large datasets. Pulling 18 months of Stripe invoice data often fails mid-query, leaving an incomplete sheet. The refresh schedules are unreliable, forcing manual refreshes that defeat the purpose of automation.

The alternative is manually exporting CSVs from HubSpot and Stripe, which is slow and error-prone. A single wrong filter on a HubSpot contact export or a misaligned date range in Stripe invalidates the entire report. Joining these two datasets in Sheets using VLOOKUP is fragile and breaks if a column name changes.

A 30-person software firm needed a weekly Cost Per Acquisition report. They used Supermetrics to pull ad spend from HubSpot and a manual CSV export for new subscriptions from Stripe. The HubSpot query took 20 minutes and often failed. The marketing lead then spent an hour matching HubSpot contacts to Stripe customers by email, a process that created frequent mismatches and left their CPA numbers a week old and untrustworthy.

The first step in any engagement would be a discovery phase to map your required KPIs such as MQLs, SQLs, new trials, and MRR to specific HubSpot and Stripe API endpoints. Syntora would then design a data pipeline architecture tailored to your data volume and reporting frequency.

The core logic for data extraction and transformation would be developed as a Python application. Leveraging our expertise in building production services, this application would typically deploy on AWS Lambda, running on a schedule to fetch new data incrementally. For efficient API interaction, we utilize httpx for async API calls. A crucial transformation step would involve joining HubSpot contact records to Stripe customer data, creating a unified customer view that eliminates manual consolidation.

The transformed data would be loaded into a Supabase Postgres database, providing a permanent, structured home for your KPIs. This approach replaces fragile spreadsheet-based reporting with a robust, queryable data source. We would then connect this database to your chosen BI tool, such as Metabase or Google Looker Studio, to build the exact dashboards needed for your analytical insights.

For system reliability, Syntora integrates structured logging with structlog, shipping logs to AWS CloudWatch for monitoring. Our production services also incorporate tenacity for automatic API call retries, ensuring resilience against transient issues. Should a job encounter persistent failures after multiple attempts, a CloudWatch Alarm would trigger an immediate notification to a designated Slack channel, linking directly to the error logs for rapid diagnosis. This proactive monitoring is standard in the robust systems we engineer.