Problem Description
Inconsistent and outdated lead scoring models result in lost opportunities and wasted sales resources. Many organizations struggle to differentiate between high-intent and low-quality leads. Automating lead scoring through Syncloop's multi-agent system provides real-time scoring based on behavioral, firmographic, and engagement data—ensuring sales reps prioritize the right leads at the right time.
How It Works
This system uses Syncloop agents to gather, analyze, and score leads based on criteria such as website activity, CRM engagement, company size, job role, and technology usage. Each agent focuses on a different dimension of the lead, with the final scoring agent aggregating and weighting scores. Control structures ensure fallback scoring, continuous learning, and re-scoring based on new data.
Who can use this
- SDR/BDR teams
- Revenue operations managers
- CRM administrators
- Marketing automation teams
- Inside sales teams
Benefits
- Scalable, AI-driven lead scoring
- Real-time updates based on new signals
- Reduces manual scoring and subjective bias
- Increases pipeline velocity and lead quality
- Syncs seamlessly with CRM and outreach tools
Agents Required
Agent Name |
Specific Roles and Capabilties |
EngagementDataAgent |
Collects engagement data from emails, web sessions, and CRM touches |
FirmographicAgent |
Pulls firmographic data like company size, industry, and revenue |
TechnographicAgent |
Detects technology stack using 3rd-party APIs (e.g., Salesforce, HubSpot) |
BehavioralScorerAgent |
Scores leads based on interaction depth and recency |
AggregateScorerAgent |
Applies a weighted model combining all sub-scores into a final score |
FeedbackUpdaterAgent |
Updates scoring logic based on closed-won/lost outcomes via Redo or IfElse |
Tool v/s Agent Name
Tool |
Agent Name |
REST Client |
EngagementDataAgent |
REST Client |
FirmographicAgent |
REST Client |
TechnographicAgent |
Python Logic |
BehavioralScorerAgent |
Transformer |
AggregateScorerAgent |
Redo / Await |
FeedbackUpdaterAgent |
Syncloop API Usage
API Endpoint |
Method |
Input Parameters |
Output Format |
Agent Name |
/leads/engagement |
POST |
lead_id, email_logs, web_data, crm_logs |
engagement_score |
EngagementDataAgent |
/leads/firmographics |
POST |
lead_id, company_name, domain |
firmographic_profile |
FirmographicAgent |
/leads/technographics |
POST |
domain, linked_tech_profile |
technographic_tags |
TechnographicAgent |
/leads/behavior_score |
POST |
engagement_score, frequency, recency |
behavioral_score |
BehavioralScorerAgent |
/leads/final_score |
POST |
All previous scores |
final_score (0–100) |
AggregateScorerAgent |
/leads/feedback |
PUT |
lead_id, outcome, deal_stage |
updated_weights or flagged_lead |
FeedbackUpdaterAgent |
Flow Summary
- EngagementDataAgent collects CRM, email, and web session data to determine how active a lead is.
- FirmographicAgent enriches each lead with static business attributes like industry and size.
- TechnographicAgent checks which tools or platforms the lead's company uses, adding context to interest levels.
- BehavioralScorerAgent scores based on recency and frequency of interactions.
- All scores are combined by AggregateScorerAgent, producing a lead score on a 0–100 scale.
- FeedbackUpdaterAgent listens to deal outcomes (win/loss) and adjusts the scoring model or retriggers scoring using Redo logic when thresholds shift.
Optional Enhancements
- Integrate predictive model scoring from a hosted ML service
- Add a visual lead heatmap for sales reps
- Include NLP-based job title analysis for buyer intent
- Connect to ad platform APIs to score based on ad click behavior
Ideal (Key Performance Indicator) KPIs to Measure Success
- Average lead-to-deal conversion rate by score tier
- Model precision (percentage of high-score leads that convert)
- Lead scoring accuracy (compared to manual assessments)
- Sales productivity increase (meetings set per lead)
- Response rate for leads above score threshold
- Feedback loop engagement (% of outcomes used for model tuning)