Lead Scoring is an enterprise feature that requires collaboration with the SegmentStream data team for implementation and optimization.
- Focus on qualified leads that are most likely to convert
- Optimize marketing campaigns toward lead quality, not quantity
- Improve ROI by focusing budget on channels that generate valuable leads
- Export qualified leads to advertising platforms for better targeting
Getting started
Choose integration method
Determine if you can use a standard CRM integration or need custom data setup.
Data requirements
There are two approaches depending on your CRM setup:- Standard CRM integration
- Custom data integration
If you use a well-known CRM system, SegmentStream can automatically connect to your existing data. For example, HubSpot has a ready-to-use integration.Contact your SegmentStream manager to get details about connecting your specific CRM system. With standard CRM integration, SegmentStream automatically collects lead data and deal outcomes to train the scoring model.
How Lead Scoring works
- Data analysis — SegmentStream analyzes your historical lead and sales data to identify patterns.
- Model training — Machine learning models learn which lead characteristics predict successful conversions.
- Qualification — New leads are evaluated and only those meeting quality thresholds become conversions.
- Value prediction — The system estimates potential deal value for each qualified lead.
- Daily updates — Lead qualification and scoring are updated daily as new data becomes available.
What you get
- Qualified lead conversions — only high-quality leads that meet configurable thresholds are created as conversions in SegmentStream.
- Predicted deal values — estimated revenue potential for each qualified lead.
- Exportable data — qualified lead conversions can be exported to ad platforms such as Google Ads and Meta for enhanced conversions, lookalike audiences, and bid optimization.
- Reporting and analytics — use Lead Scoring conversions in SegmentStream reports to analyze qualified lead performance by traffic source and compare channel effectiveness.
Best practices
- Capture Google Client ID — ensure your forms capture the Google Analytics Client ID to link website behavior with lead data.
- Collect rich attributes — the more relevant information you collect about leads at the time of creation, the more accurate the scoring will be.
- Maintain data quality — keep lead and sales data clean and up-to-date.
- Historical data — provide at least 6 months of historical data for optimal model training.
Attributes must be available immediately when the lead is created to ensure they can be used for next-day conversion processing. Attributes added later in your sales process may not be available for new lead scoring.