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“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 a Python lead scoring model requires clean, comprehensive contact data from HubSpot . But wrestling with API rate limits, authentication tokens, and pagination logic can eat up 20-40 hours of development time before you even start building your model.
Here’s how to get all the contact data you need for model development without writing a single line of API code.
Extract comprehensive contact data without API complexity using Coefficient
Coefficient eliminates the need to manage HubSpot’s API endpoints, rate limits, and authentication requirements. Instead of building custom scripts to handle pagination and error handling, you can import all your contact data with advanced filtering in under 30 minutes.
How to make it work
Step 1. Connect HubSpot to your spreadsheet.
Open Google Sheets or Excel and install Coefficient. From the sidebar, select “Import from HubSpot” and authenticate your account. Choose “Contacts” as your data source to access all contact records and properties.
Step 2. Select fields for your lead scoring model.
Pick the contact properties you need for model training: demographic data (company size, industry), engagement metrics (email opens, page views), lifecycle stage, and any custom properties. Coefficient shows all available fields in a visual interface, so you don’t need to know specific API field names.
Step 3. Apply advanced filtering for targeted datasets.
Use up to 25 filters across 5 filter groups to segment your data. Filter by date created, lifecycle stage, or engagement level to create specific training datasets. For example, filter for contacts created in the last 6 months with at least 3 email opens to focus on engaged prospects.
Step 4. Schedule automatic data refreshes.
Set up hourly, daily, or weekly imports to keep your training data current. This ensures your Python model always trains on fresh data without managing API calls in your scripts. Your data updates automatically while you focus on model development.
Step 5. Export to CSV for Python development.
Once your data is in the spreadsheet, export it to CSV format for your Python environment. You can also prototype scoring algorithms directly in the spreadsheet before moving to Python, using familiar formulas to test different weighting approaches.
Start building better lead scoring models today
Skip the API development headaches and get straight to building your Python lead scoring model. Coefficient reduces data extraction time from weeks to minutes while providing more reliable access to your HubSpot contact data. Try Coefficient free and start extracting your contact data today.
