Predictive Lead Scoring B2B/Enterprise

Problem & Business Impact

B2B/Enterprise sales teams waste time on low-potential leads due to lack of intelligent prioritization. Manual or basic scoring doesn't capture complex buying intent signals.

Measured impact:

• +35% lead conversion rate
• -25% sales cycle (better prioritization)
• +18% average deal size
• Commercial ROI: 7:1

Data & Key Features

Data sources

• Firmographics: company size, industry, revenue, growth
• Digital engagement: site visits, downloads, webinars
• Intent signals: searches, pricing views, demos
• Interaction history: emails, calls, meetings
• Technographic: current tech stack, identified needs
• Competitive intel: current provider, contract end

Engineered features

• Lead score (0-100)
• Conversion probability
• Estimated deal size
• Optimal contact timing
• Decision-maker identified (yes/no)
• Available budget (score)

Models & Methods

1. Gradient Boosting: Overall score

   • Accuracy: 86%
   • AUC: 0.92

2. Lead Scoring ML: Prioritization

   • Hot (score >80): immediate contact
   • Warm (50-80): nurturing
   • Cold (<50): marketing automation

KPIs & Performance Targets

| Metric | Target | Current | |--------|--------|---------| | Lead conversion | +35% | +38% | | Sales cycle | -25% | -27% | | Deal size | +18% | +20% | | Score accuracy | >85% | 86% |