This prompt lets ChatGPT act like a sales strategist and define criteria to score leads by engagement, ICP fit, and responsiveness.
When implementing a structured lead scoring model for sales or marketing.
When aligning sales and marketing teams on lead quality criteria.
When prioritizing outreach to the most promising prospects.
When optimizing CRM and automation workflows for better conversion rates.
Help me score leads based on engagement levels, fit with my ICP, and responsiveness.
Provide a detailed list of criteria to prioritize, including scoring ranges or weights, and explain why each criterion is important for lead qualification.
To start, let’s assume I’m a senior marketer at HubSpot. The prompt in the context of HubSpot would look like:
Help me score leads based on engagement levels, fit with my ICP, and responsiveness. Provide a detailed list of criteria to prioritize, including scoring ranges or weights, and explain why each criterion is important for lead qualification.
As you can see, this example shows how the prompt combines behavior (engagement and responsiveness) and firmographic fit (ICP alignment) to prioritize high-quality leads that are more likely to convert.
And here's the output:
Measures how actively a lead interacts with your brand.
Website Activity (0–15 points)
Visiting high-intent pages like pricing, product, or integrations earns 8–15 points.
Visiting mid-intent content like case studies or comparison pages earns 4–8 points.
General blog views earn 1–3 points.
Why: High-intent pages reveal where they are in the buying journey.
Email Engagement (0–10 points)
Clicking inside emails earns 5–10 points.
Opening emails consistently earns 2–4 points.
Unsubscribing deducts 10 points.
Why: Email actions show content relevance and interest level.
Form Fills & Conversions (0–15 points)
Requesting a demo earns 15 points.
Downloading gated content earns 5–10 points.
Signing up for newsletters earns 3–5 points.
Why: These are explicit intent signals.
Event or Community Engagement (0–10 points)
Attending a webinar earns 5–10 points.
Registering but not attending adds only 2 points.
Why: Time investment is a clear indicator of curiosity and interest.
Evaluates how closely a lead matches your Ideal Customer Profile.
Company Size (0–10 points)
Perfect-size companies earn 10 points.
Acceptable but not ideal earn around 5.
Too small or too large earn 0.
Why: Size impacts budget, product fit, and likely ACV.
Industry Fit (0–10 points)
Core industries earn 10 points.
Adjacent industries earn around 5.
Poor-fit industries earn 0.
Why: Industry alignment affects adoption and lifetime value.
Role or Title (0–10 points)
Decision-makers (VP, C-suite) earn 10 points.
Influencers (manager level) earn around 5.
End users earn 0–3 points.
Why: Decision-makers accelerate deal cycles.
Tech Stack Alignment (0–5 points)
Using tools that integrate with your product earns 3–5 points.
Neutral tech stacks earn 0.
Why: Integration fit increases the probability of adoption.
Geographic or Market Fit (0–5 points)
Leads in target regions earn 3–5 points.
Non-target regions earn 0.
Why: Your GTM strategy may prioritize certain markets.
Reflects the likelihood the lead will engage with sales.
Reply Behavior (0–5 points)
Actual replies earn 5 points.
Opens without replies earn 2.
Bounces deduct 5.
Why: Reachability is essential for sales efficiency.
Speed of Response (0–5 points)
Responding within 24 hours earns 5 points.
Within 24–48 hours earns 3 points.
After 48 hours earns 1 point.
Why: Faster responses correlate with higher close rates.
Engagement: 50% of the total score
ICP Fit: 40% of the total score
Responsiveness: 10% of the total score
This weighting emphasizes current intent while still prioritizing long-term revenue potential through ICP alignment.
80–100 points: Hot lead / ready for immediate sales outreach
60–79 points: Warm lead / SDR follow-up or accelerated nurture
40–59 points: Early MQL / nurture with automation
0–39 points: Cold lead / low priority
Or to be more specific:
Assign numeric or weighted values to each criterion for objective scoring.
Include both explicit (firmographics, job title) and implicit (behavior, engagement) data.
Consider responsiveness windows (email opens, reply time) in scoring.
Regularly review and adjust criteria based on lead-to-customer conversion trends.
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