Lead Enrichment & Routing Automation

The Challenge

A B2B company with 15 sales reps was struggling with lead management:

• Manual enrichment took 15-30 minutes per lead
• Inconsistent routing - leads went to whoever was available
• Delayed follow-up - average 2.5 hours from submission to first touch
• Lost opportunities - 23% of leads went cold before contact

The sales ops team needed automation that could handle 200+ monthly inbound leads.

Solution Architecture

Built an end-to-end automation pipeline connecting multiple tools:

Workflow Overview

graph LR
    A[Form Submit] --> B[Webhook]
    B --> C[Enrich Data]
    C --> D[AI Scoring]
    D --> E{Score >= 70?}
    E -->|Yes| F[Route to Senior Rep]
    E -->|No| G[Route to Junior Rep]
    F --> H[Update HubSpot]
    G --> H
    H --> I[Slack Alert]
    I --> J[SMS if Urgent]

Component Breakdown

1. Data Enrichment

• Parallel lookups to Clearbit and ZoomInfo
• Company size, industry, tech stack, funding
• Contact role, seniority, department
• Social profiles and engagement history

2. AI-Powered Lead Scoring

• GPT-4 analyzes enriched data
• Structured output with score (0-100) and reasoning
• Considers: company fit, buyer intent signals, timing, budget indicators
• Fallback to rule-based scoring if API fails

3. Intelligent Routing

• High-value leads (score >= 70) → senior enterprise reps
• Mid-tier leads (40-69) → mid-market reps
• Low-tier leads (< 40) → junior reps or nurture sequence
• Geographic and industry-based routing rules

4. Multi-Channel Notifications

• Slack message to assigned rep with enriched context
• Email summary with quick action buttons
• SMS for leads scored 85+ (urgent)
• Dashboard update for sales managers

Technical Implementation

Tools Stack

• n8n: Primary workflow orchestration
• Zapier: Backup workflows and monitoring
• HubSpot: CRM integration and data storage
• Slack: Team notifications
• Twilio: SMS for urgent leads

Key Features

Error Handling

• Retry logic for API failures (3 attempts with exponential backoff)
• Fallback enrichment sources if primary fails
• Dead letter queue for manual review of failed leads

Data Quality

• Validation of email, phone, company domain
• Duplicate detection against existing CRM records
• Data normalization (company names, job titles)

Monitoring

• Real-time dashboard of lead flow
• Alert if enrichment rate drops below 80%
• Weekly performance reports

Business Impact

Results after 4 months:

Speed Improvements

• 85% faster time to first touch (2.5 hrs → 20 min)
• 90% reduction in manual enrichment work
• 3x more leads handled by same team size

Quality Improvements

• 15% higher MQL→SQL conversion (better routing)
• 35% more meetings booked from inbound
• 12% increase in deal velocity

ROI

• $140K annual savings in sales ops time
• $320K additional revenue from faster, better follow-up
• Tool costs: $8K/year (n8n, APIs, integrations)
• Net benefit: $452K annually

Architecture Decisions

Why n8n + Zapier Hybrid?

• n8n: Complex logic, data transformations, AI integrations
• Zapier: Monitoring, alerting, simple backups
• Redundancy ensures 99.9% uptime for critical path

Why LLM for Scoring?

Traditional lead scoring models required manual rule updates. LLM-based scoring:

• Adapts to changing buyer signals
• Captures nuance in job titles, intent signals
• Provides explainability (reasoning for score)
• Improved accuracy by 28% vs rule-based

Data Privacy & Security

• PII handling complies with GDPR/CCPA
• Encryption at rest and in transit
• Audit logs for all data access
• Opt-out mechanism for prospects

Lessons Learned

✅ What Worked

• Parallel enrichment cut latency by 60%
• LLM scoring dramatically outperformed rules
• Fallbacks everywhere ensured reliability

❌ What Didn’t

• Over-complex routing rules - simplified to 3 tiers
• Too many Slack notifications - consolidated to one rich message
• Perfect data - embraced 80/20 rule, handle edge cases gracefully

Future Roadmap

• Add predictive lead scoring based on won/lost deals
• Implement automated meeting booking
• Build feedback loop - update scoring based on conversion data
• Expand to outbound lead discovery workflows

This system transformed lead management from a manual bottleneck into a competitive advantage, enabling the sales team to scale without adding headcount.