Automate Your HubSpot and Stripe KPI Reporting
Automate KPI reporting by building a Python service that pulls data from HubSpot and Stripe APIs. This service consolidates metrics into a database for automated dashboards and scheduled reports.
We built a reporting pipeline for a 25-person SaaS company that spent 4 hours every Monday pulling data into Google Sheets. The new system runs in 90 seconds, populates a Metabase dashboard automatically, and sends a summary to Slack. The build took 2 weeks.
The scope depends on the number of custom fields in HubSpot and the volume of Stripe transactions. A simple MQL-to-Cash report is a direct build. A cohort analysis with 24 months of Stripe data and custom HubSpot objects requires more complex data modeling.
What Problem Does This Solve?
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.
How Does It Work?
We connect to the HubSpot and Stripe APIs using their official Python libraries. We map your required KPIs like MQLs, SQLs, new trials, and MRR to specific API endpoints. The first step is a full historical data pull, processing 24 months of contact data and Stripe charges, which we stage in an S3 bucket for transformation.
The core logic lives in a Python application deployed on AWS Lambda. It runs on a schedule, typically every 6 hours, to fetch new data incrementally. We use httpx for async API calls to pull fresh records from HubSpot and Stripe efficiently. A key transformation step joins HubSpot contact records to Stripe customer data, creating a unified customer view. This join, which took 60 minutes in a spreadsheet, completes in under 5 seconds.
We load the transformed data into a Supabase Postgres database. This provides a permanent, structured home for your KPIs, not a fragile spreadsheet. We then connect this database to a BI tool like Metabase or Google Looker Studio, building the exact dashboards you need. The total data pipeline, from API pull to dashboard refresh, completes in under 3 minutes for over 100,000 records.
We use structlog for structured logging and ship logs to AWS CloudWatch. If a Stripe API call fails, tenacity handles retries automatically. If the job fails after three attempts, a CloudWatch Alarm triggers a notification to a designated Slack channel. You know immediately if the data is stale, with a link to the exact error log.
What Are the Key Benefits?
Reports in 90 Seconds, Not 4 Hours
The entire data pipeline runs automatically every morning. Your team gets fresh KPIs with their coffee instead of spending Monday building reports.
Fixed Build Cost, Near-Zero Hosting
Pay once for the system build. The AWS Lambda and Supabase free tiers cover most workloads, keeping monthly hosting costs under $20.
You Own the GitHub Repo and Data
We deliver the complete Python source code and infrastructure files. Your data lives in your own Supabase instance, not a third-party analytics platform.
Alerts When Data Breaks, Not When Execs Ask
Automated CloudWatch monitoring alerts your Slack channel if an API key expires or a data pull fails. You fix issues before anyone sees a broken dashboard.
Connects to Any BI Tool
The Postgres database from Supabase has standard connectors for Metabase, Looker Studio, Tableau, or even Google Sheets. You are not locked into one vendor.
What Does the Process Look Like?
Scoping and Access (Week 1)
You provide read-only API keys for HubSpot and Stripe and a list of required KPIs. We deliver a data mapping document confirming the exact fields to be pulled.
Pipeline Development (Weeks 2-3)
We build the core data extraction and transformation logic in Python. You receive access to a staging Supabase database to review the processed data.
Dashboard Integration (Week 4)
We connect the database to your BI tool and build the initial dashboards. You receive a draft version of the report for feedback and validation.
Deployment and Handoff (Week 5)
We deploy the system to production on AWS Lambda. You receive the full source code, a runbook for maintenance, and 4 weeks of post-launch monitoring.
Frequently Asked Questions
- How much does a custom reporting pipeline cost?
- Pricing depends on the number of data sources and the complexity of business logic. A basic report joining HubSpot contacts to Stripe customers is a standard 2-week build. Adding custom objects or historical data cleanup adds scope. We provide a fixed-price quote after the initial discovery call.
- What happens if the HubSpot API is down?
- The system is built with tenacity for automatic retries. It will attempt to reconnect 5 times with exponential backoff over 15 minutes. If it still cannot connect, the job fails gracefully, logs the error to CloudWatch, and sends a Slack alert. The dashboard shows the last successful refresh time, so you always know how fresh the data is.
- How is this different from a BI tool like Databox?
- Databox is a dashboarding layer. It pulls surface-level data but struggles with deep transformations, like joining HubSpot and Stripe data on a custom logic basis. We build the layer underneath, the data warehouse itself. This provides a clean, reliable data source that any BI tool can then connect to, giving you much more control and accuracy.
- Is there a limit to how much data we can process?
- The AWS Lambda architecture is designed to handle spikes in data volume without configuration changes. We have processed pipelines with over 5 million Stripe transactions. The primary constraint is API rate limiting from the source systems, which we manage by batching requests and using efficient, incremental queries.
- Can we add new reports or metrics later?
- Yes. You receive the full Python source code. Adding a new metric is often as simple as adding a new field to a HubSpot API query and a new column to the database table. The runbook we provide includes instructions for common modifications. We can also handle these changes on a small, scoped retainer.
- How are our API keys and data stored securely?
- API keys are never hardcoded. They are stored in AWS Secrets Manager and injected into the Lambda function at runtime. All data transfer uses TLS encryption. The Supabase database is firewalled to only allow connections from the Lambda service IP address. You control access to all underlying cloud accounts.
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