Problem Description
Sales teams often struggle to prioritize leads effectively due to incomplete data, lack of standard scoring, and inefficient engagement planning. Automating lead scoring and outreach strategies through a coordinated multi-agent AI system improves conversion rates, saves time, and ensures consistent sales operations. Using Syncloop, agents can process, score, and segment leads while designing intelligent outreach strategies that evolve with feedback.
How It Works
The system comprises six Syncloop agents working through modular APIs to clean lead data, enrich it, assign scores, categorize them into strategic tiers, and plan outreach. Control structures like Await, IfElse, and Redo are used to allow for real-time adjustments, strategic delays (e.g., based on timezone), or retry logic. Outputs are routed to CRMs or campaign managers for execution.
Who can use this
- Sales managers
- Business development teams
- CRM product teams
- Marketing ops professionals
- Inside sales automation engineers
Benefits
- Real-time lead scoring with predictive analytics
- Strategy generation based on firmographics and intent signals
- Reduced human error in lead prioritization
- Time zone-aware or behavior-aware campaign launches
- Self-optimizing outreach plans based on past performance
Agents Required
| Agent Name | Specific Roles and Capabilties |
|---|---|
| CleanserAgent | Formats and de-duplicates lead data. Uses Transformers for consistency and standardization. |
| EnricherAgent | Uses external APIs (LinkedIn, ZoomInfo) to fill gaps in lead data (e.g., title, company size). |
| ScorerAgent | Applies rules or ML models to assign scores to leads based on engagement signals. |
| StrategySegmentAgent | Uses IfElse logic to group leads into strategic buckets: High Touch, Mid-Touch, Low Touch. |
| OutreachStrategyAgent | Crafts dynamic outreach plans including channels (email, phone), timing, and frequency. |
| FeedbackLoopAgent | Uses Await and Redo to receive CRM results, update strategies, and re-score as needed. |
Tool v/s Agent Name
| Tool | Agent Name |
|---|---|
| Transformer | CleanserAgent |
| REST Client | EnricherAgent |
| Python Model / Scoring | ScorerAgent |
| IfElse | StrategySegmentAgent |
| Redo, Await | OutreachStrategyAgent |
| Await, Transformer | FeedbackLoopAgent |
Syncloop API Usage
| API Endpoint | Method | Input Parameters | Output Format | Agent Name |
|---|---|---|---|---|
| /leads/cleanse | POST | raw_leads (array of JSON) | standardized_leads | CleanserAgent |
| /leads/enrich | POST | standardized_leads | enriched_leads | EnricherAgent |
| /leads/score | POST | enriched_leads | scored_leads (score: 0–100) | ScorerAgent |
| /leads/strategize | POST | scored_leads | strategized_leads (labeled: high, mid, low touch) | StrategySegmentAgent |
| /leads/outreachplan | POST | strategized_leads, campaign_settings | outreach_strategies | OutreachStrategyAgent |
| /crm/feedback | PUT | lead_id, response, engagement_outcome | feedback_processed | FeedbackLoopAgent |
Flow Summary
- CleanserAgent initiates the workflow by transforming and cleaning the uploaded leads via /leads/cleanse.
- The cleaned data is forwarded to EnricherAgent, which fetches missing details from external APIs through /leads/enrich.
- ScorerAgent receives enriched data and applies a predictive model via /leads/score, ranking each lead.
- Leads are categorized by StrategySegmentAgent using /leads/strategize into actionable buckets (e.g., High-Touch = exec outreach, Low-Touch = nurture campaign).
- Based on this, OutreachStrategyAgent creates outreach schedules via /leads/outreachplan, adjusting for past CRM interactions.
- FeedbackLoopAgent monitors /crm/feedback, using Await to process responses over time and Redo to re-trigger the pipeline when thresholds are unmet (e.g., low reply rates).
Optional Enhancements
- Integrate LLM-based email copywriting agent
- Add timezone-aware scheduling based on IP/geolocation
- Include predictive analytics dashboard with lead pipeline conversion probability
- Use GPT-based sentiment analysis for feedback interpretation
Ideal (Key Performance Indicator) KPIs to Measure Success
- Lead conversion rate (before vs. after automation)
- Average scoring accuracy (based on CRM outcome correlation)
- Email open & response rate per strategy type
- Reduction in manual lead analysis time (hours/week)
- Strategy adjustment frequency (showing responsiveness of AI loop)
- CRM-to-outreach synchronization success rate
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