Design a lead scoring model with firmographic and behavioral points
Produces a complete, implementable lead scoring model: firmographic fit points, behavioral intent points with decay and negative scoring, threshold bands tied to actions, and a monthly validation loop. It caps complexity at 12 criteria and adapts to what you can actually track, so the model ships instead of dying in a spreadsheet.
You are a RevOps architect who has designed lead scoring models for B2B companies from seed stage to public. You've seen every failure mode: models with 40 criteria nobody maintains, 'gut feel with numbers on it', behavioral scores that reward newsletter opens like they're demo requests, and thresholds set once and never validated. Your models are simple, opinionated, and built to be wrong in detectable ways. I want you to design my lead scoring model. Produce: 1. FIT SCORE (0-50): 4-6 firmographic criteria with point values and the exact attribute ranges that earn them. State why each criterion earns its weight. 2. INTENT SCORE (0-50): 4-6 behavioral criteria with point values, including negative scoring for disqualifying behavior and decay rules (how many days before a behavior stops counting). 3. THRESHOLDS: score bands for cold / nurture / MQL / fast-track, with the action each band triggers and who owns it. 4. THE VALIDATION LOOP: a simple monthly check — which 3 numbers to pull, and what pattern means the weights are wrong. 5. IMPLEMENTATION NOTES: how to express this in a CRM or a Clay table without custom engineering. Keep the total model under 12 criteria. If I give you a criterion that sounds predictive but usually isn't, push back and say why. Before you write anything, interview me. Ask me these questions ONE AT A TIME, waiting for my answer each time: 1. What do you sell, at what price point, and how long is a typical sales cycle? 2. Describe your ICP — or paste it if you have one written. 3. What lead behaviors can you actually track today (site visits, email replies, demo requests, content downloads, product signups)? 4. Roughly how many leads enter your funnel monthly, and what percent should sales realistically touch? 5. What does your team currently do when a 'hot lead' appears, honestly? Once you have my answers, produce the model. If my trackable behaviors are too thin for a real intent score, say so and design a fit-heavy model instead of faking it.
How to use it
- 1
Copy the prompt into Claude, ChatGPT, or any LLM.
- 2
Have your ICP written down first — run the 10-best-customers prompt in this category if you don't — and audit which behaviors your stack can actually track.
- 3
Be honest on question 4; threshold placement depends entirely on your lead volume versus sales capacity.
- 4
Implement the model in your CRM or Clay within a week of generating it — scoring models decay fast when they stay theoretical.
- 5
Run the validation loop monthly and adjust weights based on which scored leads actually converted.
Best practices
Weight fit over intent early on — a perfect-fit company that's never heard of you beats a terrible-fit lead who downloaded three ebooks.
Insist on negative scoring: students, competitors, and job seekers inflate every intent-only model.
Don't skip decay rules; a demo request from 90 days ago is not a buying signal, it's a memory.
Present the model to your reps and ask what it gets wrong — front-line skepticism now is cheaper than ignored scores later.
Example: what this looks like in practice
A RevOps manager at a 90-person HR software company answers the interview: $18K ACV, 45-day cycle, ICP is 100-500 employee companies with a dedicated HR team, trackable behaviors are pricing page visits, demo requests, webinar attendance, and email engagement. About 400 leads arrive monthly but sales can properly work 80. The model returns a fit score weighting headcount range (15 points) and HR team presence (12), an intent score where pricing page visits earn 10 with 14-day decay and competitor domains score minus 30, and an MQL threshold at 55 calibrated to surface roughly 80 leads. The monthly validation check compares MQL-to-opportunity rates across score bands. Two months in, they discover webinar attendance predicts nothing and cut it.
Best fit
This prompt is one gear in a bigger machine. We orchestrate 20+ tools into outbound systems our clients own — and guarantee the results.
Apply for a Pilot Spot → →Frequently asked questions
Start with two components: a fit score based on how closely the company matches your ICP, and an intent score based on behaviors that historically precede buying. Assign point values, set a threshold that matches your sales capacity, and add decay so old behaviors stop counting. This prompt builds all of it in one pass, capped at 12 criteria so it actually gets maintained.
More icp, targeting & lead scoring prompts
Define your ICP from your 10 best customers
Reverse-engineers a tight, one-page ICP from your actual closed-won history instead of aspirational guesswork. It forces narrow ranges, separates signal from coincidence, and flags when your customer base actually contains two different ICPs — the insight most founders miss until their outbound numbers tell them the hard way.
Build a negative ICP: who to actively avoid and why
Turns your worst-customer war stories into a formal negative ICP: hard disqualifiers, stacking yellow flags, and the false-positive profile that keeps fooling your team. Includes list-building exclusion filters and a polite decline template, so avoiding bad fits becomes operational instead of a lesson everyone relearns quarterly.
Build an inbound lead scoring rubric your team will actually use
Replaces inbound chaos with a four-bucket rubric — hot, work, nurture, discard — a rep can apply in 30 seconds. It ranks your conversion sources by true buying intent, defines the minimum enrichment before routing, and hands you implementable if-then routing logic with speed-to-lead targets per bucket.