Lead Scoring is an advanced conversion type in SegmentStream that uses machine learning to assign quality scores to your leads based on their characteristics and likelihood to convert. This feature helps you identify high-value prospects and optimize your marketing spend toward leads that are most likely to generate revenue.
Overview
Lead Scoring analyzes your lead data to identify qualified leads that meet your business criteria and estimates their potential deal value. This allows you to:
- Focus on qualified leads that are most likely to convert
- Optimize marketing campaigns toward lead quality, not just quantity
- Improve ROI by focusing budget on channels that generate valuable leads
- Export qualified leads to advertising platforms for better targeting
Getting Started
To implement Lead Scoring for your business:
- Assess Your Data: Review whether you have the required lead and sales data
- Choose Integration Method: Determine if you can use a standard CRM integration or need custom data setup
- Contact SegmentStream: Reach out to discuss your specific requirements and begin the setup process
Lead Scoring is an enterprise feature that requires collaboration with SegmentStream's data team for proper implementation and optimization.
Data Requirements
To implement Lead Scoring, you need to provide SegmentStream with information about your leads and their outcomes. There are two main approaches depending on your CRM setup:
Option 1: Standard CRM Integration
If you use a well-known CRM system, SegmentStream can automatically connect to your existing data. For example, we support HubSpot with a ready-to-use integration - Connection Guide.
Contact us to get more details about connecting your specific CRM system.
With standard CRM integration, SegmentStream automatically collects lead data and deal outcomes to train the scoring model.
Option 2: Custom Data Integration
If you use a custom CRM or one not in our catalog, you'll need to provide lead and sales data in the following format:
Leads Data Table
Your leads table should contain the following information:
Required Fields:
created_at- When the lead was created
updated_at- When the lead was last updated
lead_id- Unique identifier for each lead
google_client_id- Google Analytics Client ID when the lead was created
user_id- Internal user identifier that links to sales data
Custom Properties (Recommended): Include as many relevant lead characteristics as possible to improve model accuracy, for example:
user_email- Lead's email address
first_name- Lead's first name
last_name- Lead's last name
company_name- Company name
company_size- Number of employees
industry- Industry sector
job_title- Lead's job title
seniority_level- Decision-making level (C-Suite, Manager, Individual Contributor)
lead_source- How the lead was acquired (Organic Search, Paid Ads, Referral, etc.)
country- Geographic location
annual_revenue- Company's annual revenue
budget- Stated budget for your solution
pain_points- Specific challenges mentioned
product_interest- Which products/services they're interested in
Example Lead Record:
Column | Value | Required | Comment |
created_at | 2024-01-15 10:30:00 | ✅ | |
updated_at | 2024-01-15 10:30:00 | ✅ | |
lead_id | lead_12345 | ✅ | |
google_client_id | 1234567890.1234567890 | ✅ | NULL if not collected |
user_id | user_abc123 | ✅ | |
user_email | |||
first_name | John | ||
last_name | Doe | ||
company_name | Acme Corporation | ||
company_size | 250 | ||
industry | Technology | ||
job_title | VP of Marketing | ||
seniority_level | C-Suite | ||
lead_source | Google Ads | ||
country | United States | ||
annual_revenue | 10000000 |
Sales/Deals Data Table
Your sales table should track successful conversions from leads:
Required Fields:
created_at- When the deal was closed
updated_at- When the deal was last updated
order_id- Unique identifier for each sale/deal
value- Revenue amount of the deal
user_id- Links back to the lead data (same user_id)
Important for LTV Prediction: To accurately predict customer lifetime value, include ALL purchases attributed to each user_id in this table, not just the first purchase. This helps the model understand the complete revenue potential of similar leads.
Example Sales Record:
Column | Value | Required |
created_at | 2024-03-20 14:15:00 | ✅ |
updated_at | 2024-03-20 14:15:00 | ✅ |
order_id | order_67890 | ✅ |
value | 25000.00 | ✅ |
user_id | user_abc123 | ✅ |
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. We can work with you to determine the optimal threshold that meets your business needs.
Predicted Deal Values
Estimated revenue potential for each qualified lead, allowing you to focus marketing spend on high-value opportunities.
Exportable Data
Qualified lead conversions can be exported to advertising platforms like Google Ads and Meta Ads for:
- Enhanced Conversions: Send high-quality conversion signals back to ad platforms
- Lookalike Audiences: Create audiences based on your qualified leads
- Bid Optimization: Optimize toward lead value rather than just lead volume
Reporting and Analytics
Use Lead Scoring conversions in SegmentStream reports to:
- Analyze qualified lead performance by traffic source
- Compare channel effectiveness based on lead quality and predicted value
- Optimize marketing attribution for lead generation campaigns
Implementation Process
- Data Assessment: SegmentStream's data team evaluates your lead and sales data
- Setup: We setup Lead Scoring conversion for you once data is validated
- Model Training: Initial machine learning models are trained on your historical data
- Testing Phase: Lead scores are generated and validated against known outcomes
- Go Live: Daily lead scoring begins with continuous model improvement
Best Practices
Data Collection
- 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 lead creation, the more accurate the scoring will be.
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 (e.g., after sales calls) may not be available for new lead scoring
- 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
Support
For questions about Lead Scoring implementation or to begin the setup process, please contact your SegmentStream account manager or reach out to our support team.