ChatGPT Prompts for Building a Lead Scoring Model

A well-crafted lead scoring model helps sales teams prioritize high-value prospects, shorten sales cycles, and improve conversion rates. With ChatGPT-powered prompts, you can build a data-driven lead scoring framework tailored to your business needs.

1. Define Your Ideal Customer Profile (ICP)

ChatGPT Prompt:

Generate a list of key attributes (firmographic, demographic, and behavioral) that define our ideal customer profile based on [Industry, Company Size, Job Title, Annual Revenue, etc.

Example Output:

• Industry: B2B SaaS, Professional Services, Media, Advertising
• Company Size: 500 - 10K employees
• Job Title: CRO, VP of RevOps, VP of Sales Engineering, VP of Sales Enablement
• Annual Revenue: $50M+
• Tech Stack: Uses CRM (Salesforce, HubSpot), Engagement tools (Outreach, Salesloft)

2. Assign Points Based on Demographics & Firmographics

ChatGPT Prompt:

Create a weighted scoring system based on demographic and firmographic data, assigning higher scores to leads that match our ICP (e.g., job title, company size, industry, revenue, location).

Example Output:

3. Score Engagement & Intent Signals

ChatGPT Prompt:

Develop a lead scoring model that assigns points based on engagement (email opens, content downloads, demo requests) and intent signals (website visits, time spent on pricing page, competitor comparisons).

‍Example Output:

4. Incorporate Negative Scoring

ChatGPT Prompt:

List behaviors or characteristics that should lower a lead’s score (e.g., competitors, junior-level contacts, bounced emails, unsubscribes).

‍Example Output:

5. Differentiate MQLs vs. SQLs with Thresholds

ChatGPT Prompt:

Set clear thresholds for when a lead qualifies as an MQL (Marketing Qualified Lead) versus an SQL (Sales Qualified Lead), considering engagement level and demographic fit.

Example Output:

• MQL (Marketing Qualified Lead) = Score ≥ 50
  
  • Engaged with content but not enough high-intent actions
• SQL (Sales Qualified Lead) = Score ≥ 80
  
  • Has taken high-intent actions like requesting a demo or ROI calculation

6. Use Predictive AI for Lead Scoring Optimization

ChatGPT Prompt:

Generate a machine learning-based lead scoring model that dynamically adjusts scores based on historical conversion data, prioritizing leads with patterns similar to past closed deals.

Example Output:

• Model identifies that leads who attend webinars + visit the pricing page are 2.5x more likely to convert → Boost score by +15
• Leads from companies using Salesforce + Outreach historically convert at a 30% higher rate → Boost score by +10
• Leads from regions with low historical conversions get penalized -10 points

7. Create an Automated Lead Scoring System

ChatGPT Prompt:

Suggest ways to automate our lead scoring model using CRM (HubSpot, Salesforce) and marketing automation tools (Marketo, Pardot).

Example Output:

• Salesforce Workflow: Auto-update lead scores based on engagement (e.g., adding +20 points when a demo is requested)
• HubSpot Trigger: Send an alert to SDRs when a lead crosses 80 points (SQL threshold)
• Marketo Automation: Adjust email nurturing cadence based on lead score (high-scoring leads get fast-tracked to sales)

Final Thoughts: How to Build a High-Impact Lead Scoring Model

• Align with ICP – Focus on the right leads
• Use engagement signals – Prioritize buyers showing intent
• Include negative scoring – Filter out low-quality leads
• Automate scoring – Save time & boost efficiency
• Leverage AI – Continuously improve accuracy

With these ChatGPT-powered prompts, you can create a data-driven, scalable lead scoring model that helps your sales team close more deals, faster.