)
Written by
Table of Contents
“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Building custom Python scoring models requires clean, reliable data feeds from your CRM. HubSpot’s manual CSV exports quickly become outdated, while direct API integration means wrestling with authentication tokens and rate limits.
Here’s how to create an automated pipeline that feeds your Python models with fresh HubSpot data without the technical headaches.
Set up automated HubSpot data exports using Coefficient
Coefficient eliminates the complexity of direct HubSpot API integration by handling all the authentication, rate limiting, and data synchronization automatically. You get scheduled imports that refresh your lead data in Google Sheets, creating a reliable foundation for your Python scoring models.
How to make it work
Step 1. Connect HubSpot to Google Sheets through Coefficient.
Install Coefficient from the Google Workspace Marketplace and authorize your HubSpot connection. This creates a managed API connection that handles all the technical complexity behind the scenes.
Step 2. Configure your lead data import with custom field selection.
Choose specific contact properties, deal data, and engagement metrics your Python models need. Select standard fields like email, company, and lifecycle stage, plus any custom properties you’ve created. Coefficient supports unlimited field selection without hitting API limits.
Step 3. Apply advanced filtering to focus on relevant lead segments.
Use up to 25 filters with AND/OR logic to target specific cohorts. Filter for leads created in the last 30 days, particular lead sources, or specific lifecycle stages. You can even point filter values to spreadsheet cells for dynamic adjustments.
Step 4. Set up automated refresh schedules.
Configure hourly, daily, or weekly imports to keep your data fresh without manual intervention. Your Python models always work with current data, and you never have to worry about stale CSV exports again.
Step 5. Include association data for richer model context.
Pull related deals, companies, and engagement history in a single import. This gives your predictive models the comprehensive context they need for accurate scoring, including deal progression and interaction patterns.
Start building better scoring models today
This automated pipeline transforms your workflow from manual data exports to reliable, scheduled feeds that keep your Python models running smoothly. Get started with Coefficient and focus on model development instead of data infrastructure.
