Welcome! Today we're diving into lead scoring in Coldout—one of the most valuable workflows for prioritizing your sales pipeline. Lead scoring helps you automatically evaluate and rank prospects so your team focuses on the best opportunities first. Let's explore how to build intelligent scoring systems using Coldout's enrichment capabilities, Coldout research, and formulas.
Why lead scoring matters
Not all leads are created equal. Some prospects perfectly match your ideal customer profile—they have the right company size, budget, industry, and pain points. Others might be interesting but not ready, too small, or outside your target market.
Lead scoring assigns each prospect a score or classification based on how well they match your criteria. High-scoring leads get immediate attention. Lower scores receive different treatment—maybe nurture campaigns or future follow-up. This ensures your sales team invests time where it matters most.
Building your scoring system
Effective lead scoring in Coldout combines three capabilities: data enrichment for firmographic information, Coldout research for intent signals, and formulas to calculate scores.
Step 1: Enrich with firmographic data
Start by enriching your leads with company attributes. Use Coldout's enrichment waterfalls to gather employee count, industry, revenue, funding stage, location, and technologies used. These data points form the foundation of your scoring.
Set up enrichment columns for each attribute. Waterfalls ensure maximum coverage by trying multiple providers sequentially until they find the data you need.
Step 2: Research intent signals with Coldout
Layer on intelligence that indicates buying intent. Create Coldout columns to research hiring activity, recent news, technology stack, and pain points.
For hiring signals, prompt Coldout: "Visit Company Website and check if they have job listings for roles related to sales, marketing, or engineering. Return 'Actively Hiring' with the number of roles, or 'Not Hiring'."
For recent news, prompt: "Search for news about Company Name from the last 3 months. Look for funding announcements, product launches, or expansion plans. Summarize in one sentence or return 'No recent news'."
Growing teams and companies making major changes often need new tools, making these strong buying signals.
Step 3: Create scoring logic with formulas
Now combine your enriched data into actual scores using Coldout's formula capabilities.
Create a point-based scoring formula that adds points based on your criteria:
"Calculate a score starting at 0. Add 25 if Employee Count is between 50 and 500. Add 20 if Industry equals 'Technology' or 'SaaS'. Add 15 if Funding Stage contains 'Series'. Add 20 if Revenue is greater than 5000000. Add 10 if Hiring Activity contains 'Actively Hiring'. Return the total score."
Tell Coldout's AI formula generator this logic in plain English, and it creates the Coldoutscript code to execute the calculation for every row.
Weighted scoring approach
Different factors matter differently. Create a weighted formula that emphasizes your most important criteria:
"Calculate: (Employee Count divided by 10) plus (if Industry matches 'SaaS', 30, otherwise 0) plus (if Recent News doesn't equal 'No recent news', 25, otherwise 0) plus (if Tech Stack contains 'Salesforce', 20, otherwise 0). Return the total."
This weighs industry match and recent activity more heavily than raw company size.
Creating tiered classifications
Instead of numerical scores, you might prefer categorical tiers. Use formulas to create classification logic.
Create an "ICP Fit" column: "If Employee Count is between 100 and 1000 AND Industry equals 'Technology' AND Revenue is greater than 10000000, return 'High Fit'. Else if Employee Count is between 50 and 100 AND Industry equals 'Technology', return 'Medium Fit'. Else return 'Low Fit'."
Create a "Buying Intent" column: "If Hiring Activity contains 'Actively Hiring' AND Recent News doesn't equal 'No recent news', return 'High Intent'. Else if either condition is true, return 'Medium Intent'. Else return 'Low Intent'."
Create a final "Priority Tier" combining both: "If ICP Fit equals 'High Fit' AND Buying Intent equals 'High Intent', return 'A-Tier'. Else if either is high, return 'B-Tier'. Else if ICP Fit equals 'Medium Fit', return 'C-Tier'. Else return 'D-Tier'."
Now filter your table to show only A-Tier and B-Tier leads for immediate outreach.
Identifying disqualifying factors
Create formulas that flag leads you should avoid:
"If Employee Count is less than 20, return 'TOO SMALL'. Else if Industry equals 'Non-Profit', return 'WRONG INDUSTRY'. Else if Recent News contains 'layoff', return 'FINANCIAL RISK'. Else return 'QUALIFIED'."
Filter out disqualified leads before launching campaigns.
Practical scoring examples
For a SaaS product targeting mid-market: 30 points for 100-500 employees, 25 points for Technology industry, 20 points for using Salesforce or Hubspot, 15 points for Series A+ funding, 10 points for actively hiring.
For B2B services targeting enterprise: 35 points for 1000+ employees, 25 points for Fortune 2000 status, 20 points for recent expansion news, 15 points for multiple locations, 5 points for target geography.
Customize your scoring criteria to match your specific ideal customer profile.
Optimizing your system
Test your scoring on a small batch first. Manually review the top 20 and bottom 20 scored leads to verify the scoring matches reality. Adjust weights and criteria based on your findings.
After running campaigns, track conversion rates across different score ranges. If low-scoring leads convert surprisingly well, your criteria needs refinement.
Update your scoring quarterly. Your ideal customer evolves, so review which characteristics correlate with actual success and adjust accordingly.
Use conditional enrichment to save credits—only run expensive Coldout research on leads that pass basic qualification criteria.
Wrapping up
Lead scoring in Coldout combines enrichment waterfalls for firmographic data, Coldout for intent research, and formulas for scoring logic. Build point-based scores, create weighted calculations, establish tiered classifications, and develop comprehensive scoring dashboards.
The result? Your sales team focuses on leads most likely to convert, your outreach targets the right prospects at the right time, and your pipeline fills with higher-quality opportunities. Start simple, test thoroughly, and refine based on results. That's intelligent lead scoring.
