Quick Start: Build Account Scoring for Outbound
Including 1 AI prompt
Welcome to a new 🔒 subscriber-only edition 🔒 of my weekly newsletter of Outbound Kitchen. Each week, I dive into reader questions about scaling outbound and making it your #1 growth engine. For more: Launching Outbound, and Scaling Outbound
An SDR leader recently asked me (after reading my guides on building ICPs):
"That's great, but how can I actually use this ICP? How do I build my scoring?"
Sound familiar?
You've done the work to build your ICP, but now what? Your team still treats all "qualified" accounts the same way, picks accounts randomly, and you have no systematic way to predict which accounts are worth the most effort.
Here's what's happening in your revenue organization right now:
❌ ICP exists but accounts aren't ranked or prioritized
❌ SDRs pick accounts based on gut feeling or company size
❌ AEs fight over the same "obvious" prospects
❌ Team wastes time on accounts that will never close
❌ Track buying signals on accounts that don't fit their ICP
Your ICP is stuck in a spreadsheet. Until those insights are scored, automated, and surfaced to your reps daily, they're just reference material.
What You'll Build: A simple 2-layer scoring system that tells your team exactly which accounts to call first. You'll see higher response rates, faster conversions, and clear team focus within the first month.
Prerequisites:
If you don't have an ICP yet, build that first with these guides:
Today is Part 1 (foundation).
Coming later this week in Part 2:
Consumption-based scoring model
Buying signal scoring
AI prompts to create those systems quicker
How to implement this in Clay
Foundation Check: Do You Have What You Need?
🛑 STOP HERE if you don't have this foundation.
Scoring without solid data = guaranteed failure.
✅ Before starting, ensure you have:
Internal Data (From Your CRM):
List of your best customers (highest ACV, lowest churn, fastest sales cycles)
List of your worst customers (high churn, low value, hard to close)
Clear patterns between these two groups
Basic External Data Access:
LinkedIn Sales Navigator or similar for team size data
One technographic tool (BuiltWith free tier works)
Crunchbase free tier for funding data
Team Buy-In:
At least one SDR willing to test the system
Sales leadership support for a 30-day pilot
Commitment to track results honestly
⚠️ Critical Reality Check: Data Quality Warning
At Chili Piper, we used a provider giving us incorrect Salesforce data 50% of the time. Result? Our entire scoring system was worthless and the team stopped trusting it.
The Rule: If you can't get reliable data on company size and technology stack, stop here. Bad data = failed scoring system. Always test your data sources with 10 known customers first.
The Simple 2-Layer System: Your Foundation
Forget complex models. Start with two questions that matter most:
🎯 Layer 1: Propensity Score (0-100) - Will they buy and succeed?
What it measures: How well a prospect matches your successful customer profile
Why it matters: High-fit accounts convert 3-5x faster and stick around longer
💰 Layer 2: Value Prediction - How much revenue potential?
What it measures: Predicted deal size in actual dollars (not points)
Why it matters: A $200K opportunity deserves different attention than a $20K opportunity and different resource allocation
Key insight: Your team needs to see "$180K predicted ACV" not "Value Score: 75/100"
That's it for Part 1.
No buying signals, no complex triggers. Just fit and value prediction.
Now let's unpack both scoring systems.
Scoring #1: Building Your Fit Score: 7 Intelligence Indicators
Go beyond basic demographics (industry, company size, etc).
Use signals that predict success and reveal pain points that most teams miss.
Important: This list is non-exhaustive.
The goal is to find indicators that works for you (differentiate YOUR best customers from your worst customers).
Indicator 1: Technology Adoption Speed
What it reveals: How quickly they adopt new solutions (early adopters convert faster)
Recent technology adoptions (last 12 months): +20 points
Technology stack from last 2-3 years: +15 points
Legacy technology only (5+ years old): +5 points
No visible technology investments: 0 pointsHow to find it: Company careers pages (job description), LinkedIn job postings, BuiltWith implementation dates
Example: if you sell a data app: company on the modern data stack, company recently added Sigma (BI tool) or dbt (data transformation) = early adopter = 20 points
Indicator 2: Expansion/Growth Signals
What it reveals: Growing companies buy more tools and have budget
Recent funding (last 18 months): +18 points
Rapid hiring (30%+ team growth): +15 points
New office locations or markets: +12 points
Product launches or expansions: +10 points
No growth signals: 0 pointsHow to find it: Crunchbase, LinkedIn company updates, job posting volume
Companies in growth mode have more budget and urgency, because as you scale processes start to break.
Data Point 3: Technology Compatibility (Critical - Can Be Negative)
Uses incompatible technology: -100 points (automatic disqualifier)
Uses compatible adjacent tools: +10 pointsHow to find it:
BuiltWith Chrome extension for apps on websites
Job descriptions , or Clay technographic data
Critical Learning from Chili Piper:
We gave companies using Pipedrive or Microsoft Dynamics -100 points because we only integrated with Salesforce and HubSpot.
Lesson: Don't waste your team's time on technically incompatible accounts.
Same principle for CastorDoc: On-premise data solutions got -100 points because we were cloud-native only.
Indicator 4: Competitive Intelligence
What it reveals: Their current solution status and switching likelihood
Uses outdated competitor: +15 points (displacement opportunity)
Uses adjacent solution (not direct competitor): +12 points (expansion opportunity)
No current solution visible: +10 points (greenfield opportunity)
Uses strong direct competitor: +5 points (difficult displacement)
Recently implemented strong competitor: 0 points (no opportunity)How to find it: BuiltWith, G2 reviews they've left, job postings mentioning current tools
What it reveals: Their current solution status and switching likelihood
Indicator 5: Geographic/Market Sophistication
What it reveals: Market maturity affects buying sophistication and your success rates
Primary market (where you have most success): +12 points
Secondary market: +8 points
Expansion market: +4 points
Markets where you consistently struggle: 0 pointsHow to determine this: Analyze your closed deals by geography and identify patterns
Indicator 6: Company Structure & Operations (10 points max)
What it reveals: Operational complexity that affects your solution fit
Multiple office locations: +10 points (if your solution scales with complexity)
Remote/distributed team: +8 points (if you solve remote work problems)
Centralized operations: +6 points (if you need single decision maker)Customize this based on your solution: Does operational complexity help or hurt your value proposition?
Indicator 7: Company Size
What it reveals: Whether you can actually serve them successfully
Sweet spot size (based on your best customers): +15 points
Adjacent to sweet spot: +10 points
Over 1000 employees: +5 points (if you're not enterprise-ready, this might be 0 or -100)
Too small for your solution: 0 pointsCritical insight: More than 1000 employees can be a disqualifier if you're not enterprise-ready.
Total Fit Score: Add all indicators (0-100 scale, can go negative with disqualifiers)
Quick Validation Test
Score 20 of your best customers and 20 of your worst customers. If the scoring works:
Best customers should average 70+ points
Worst customers should average 40- points
If not, adjust your point values
Scoring #2: Building Your Value Score: Simple Revenue Prediction
For Seat-Based Pricing Companies
If your pricing is based on users/seats (most SaaS), here's how to predict revenue:
Step 1: Identify Your Usage Driver
Sales tools → GTM team size (Sales Reps)
Engineering tools → Developer count
HR tools → Total employee count
Marketing tools → Marketing team size
Step 2: Build Your Base Formula
Estimated Annual Value = (Target Team Size × Monthly Price × 12)Step 3: Find Team Size Data
LinkedIn Sales Navigator: Filter by department
Clay LinkedIn enrichment: Automated team size detection
Step 4: Calculate and Score
Example (B2B Sales Tool):
Target users: GTM team members
Pricing: $100/user/month
Data source: LinkedIn via Clay
Calculations:
150 GTM team members: 150 × $100 × 12 = $180K potential ACV
75 GTM team members: 75 × $100 × 12 = $90K potential ACV
25 GTM team members: 25 × $100 × 12 = $30K potential ACV
Step 5: Add Simple Growth Multipliers
Recent funding (Series A+): ×1.2 (20% boost)
Fast growth (50%+ YoY): ×1.15 (15% boost)
Multiple locations: ×1.1 (10% boost)Complete Example:
Base: 100 GTM team × $100 × 12 = $120K
Recent Series B funding: ×1.2
Fast growth signals: ×1.15
Final Value: $120K × 1.2 × 1.15 = $166K predicted ACV
Account Tiers: Your Action Framework
Combine Fit Score + Predicted Dollar Value into four actionable tiers:
🏆 Champions (Fit 70+ AND High Dollar Value)
What they are: Perfect fit + high revenue potential
Criteria: Fit Score 70+ AND Predicted Value $[your high threshold]+
Action: Immediate outreach, AE involvement, personalized approach
Resource allocation: Account-based SDRs + AEs
Expected outcome: Highest conversion rates + largest deals
🎯 Challengers (Fit 50-69 AND High Dollar Value)
What they are: High revenue but harder to convert
Criteria: Fit Score 50-69 AND Predicted Value $[your high threshold]+
Expected outcome: Lower conversion but high value when they close
⚡ Quick Wins (Fit 70+ AND Lower Dollar Value)
What they are: Easy to convert but smaller deals (can be your SMB/mid-market accounts)
Criteria: Fit Score 70+ AND Predicted Value $[low threshold]-$[high threshold]
Action: Efficient outreach, standardized approach, fast implementation
Resource allocation: Programmatic outbound
Expected outcome: High conversion rates, fast closes, smaller deals
❌ Avoid (Fit <50 OR Low Dollar Value)
What they are: Poor fit or low value
Criteria: Fit Score <50 OR Predicted Value <$[your low threshold]
Action: Do not actively contact
Resource allocation: 0% active prospecting
Expected outcome: Very low conversion, not worth effort
Define Your Dollar Thresholds:
Based on your average deal size and what justifies sales effort.
Example: High = $100K+, Low = $30K minimum.
AI Prompt: Build Your Custom Scoring System
Copy-paste this prompt to create your personalized scoring system:
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