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Prompt Engineering for Automated Lead Scoring

Goose Digital complete
lead-scoringprompt-engineeringmarketing-automation ClaudeMarketo

The Problem

Traditional lead scoring relies on rigid point-based rules. A form fill gets 10 points, a page visit gets 5. But these rules miss nuance — the intent behind actions, the quality of engagement, the context of behavior patterns.

Our Approach

We designed a prompt engineering framework that feeds behavioral data to Claude for contextual lead scoring. The system considers:

  • Engagement patterns over time
  • Content topics consumed
  • Firmographic data alignment
  • Behavioral velocity (acceleration in engagement)

Results

The AI-powered scoring model demonstrated significant improvements in lead quality identification compared to the legacy rule-based system, with particular gains in identifying high-intent leads earlier in the funnel.

Lessons Learned

  1. Context windows matter — Feeding too much history dilutes recent signals
  2. Prompt structure is critical — Structured output formats improve consistency
  3. Human oversight remains essential — AI scores should augment, not replace, sales judgment