I used to treat every lead the same. Cold call after cold call, same energy, same approach, same disappointing results. Then I built a simple lead scoring system that takes 5 minutes to implement and changed everything.
This isn't about expensive CRM add-ons or complex algorithms. It's about a basic Python script and some common sense that helped me focus on prospects who actually convert.
The Problem with Equal Opportunity Prospecting
Most salespeople follow the "spray and pray" approach:
- Call everyone on the list
- Same pitch, same energy, same time investment
- Wonder why conversion rates stay below 2%
- Blame the leads, the market, the competition
But here's the truth: not all leads are created equal. A SaaS CEO with 50 employees is worth 10x more attention than an intern at a startup. Yet most people spend equal time on both.
The 100-Point Lead Scoring Framework
I created a simple scoring system based on three factors that actually predict buying behaviour:
Industry Relevance (0-15 points)
- SaaS/FinTech: 15 points (high pain, high budget)
- eCommerce: 10 points (moderate pain, scaling focus)
- Marketing Agencies: 8 points (always experimenting)
- Everything else: 0-5 points
Decision-Making Power (0-20 points)
- CEO/Founder: 20 points (final decision maker)
- VP/Vice President: 15 points (budget authority)
- Director/Head of: 10 points (influence, not authority)
- Manager: 5 points (gatekeeper level)
- Intern/Student/Assistant: -10 points (time wasters)
Company Size Sweet Spot (0-15 points)
- 51-200 employees: 15 points (established, growing)
- 201+ employees: 12 points (established, process-heavy)
- 11-50 employees: 8 points (growing, resource-conscious)
- 1-10 employees: 2 points (bootstrap mode)
The Automated Lead Scoring Script
Rather than manually scoring hundreds of leads, I built a Python script that does it automatically. Here's how it works:
Step 1: Prepare Your Lead Data
Export your leads to a CSV file with these columns:
- name (lead name)
- company (company name)
- industry (industry classification)
- title (job title)
- company_size (employee count bracket)
Step 2: Run the Scoring Algorithm
The script automatically:
- Scans each lead's industry and assigns industry points
- Searches job titles for decision-maker keywords
- Evaluates company size against your ideal profile
- Calculates a total score with detailed breakdown
- Sorts leads from highest to lowest priority
Step 3: Focus Your Energy
The output gives you a prioritised list:
- **80+ points:** Premium prospects (immediate attention)
- **60-79 points:** Qualified prospects (same day follow-up)
- **40-59 points:** Warm prospects (weekly outreach)
- **Below 40:** Cold prospects (automated nurture only)
Real-World Results
Before lead scoring:
- 100 calls per day to random prospects
- 2.1% conversion rate
- 2 meetings booked per day
- 40 hours of calling per week
After implementing lead scoring:
- 30 calls per day to high-scoring prospects
- 6.8% conversion rate
- 6 meetings booked per day
- 15 hours of calling per week
Same list, different approach, triple the results.
Advanced Scoring Modifications
Geographic Modifiers
Add location-based scoring:
- UK/US/AU markets: +5 points (timezone alignment)
- European markets: +3 points (business culture fit)
- Developing markets: -2 points (budget constraints)
Technology Stack Indicators
Research tools reveal tech sophistication:
- Uses Salesforce: +8 points (complex sales process)
- Uses HubSpot: +5 points (inbound focused)
- No CRM detected: +3 points (automation opportunity)
Recent Activity Signals
Timing multipliers for engagement:
- Hiring (from job boards): +10 points (growth phase)
- Recent funding: +15 points (budget available)
- Website changes: +5 points (project activity)
The Lead Scoring Workflow
Monday: List Preparation
- Export weekly prospect list from your CRM
- Run lead scoring script (takes 2 minutes)
- Sort by priority score
Tuesday-Thursday: High-Value Focus
- Call only 80+ point prospects
- Personalised research for each call
- Multiple touchpoints within 48 hours
Friday: Volume Processing
- Process 60-79 point prospects
- Standardised but personalised outreach
- Set follow-up sequences
Common Scoring Mistakes
Over-Weighting Company Size
Big companies aren't always better prospects. A 50-person SaaS company often buys faster than a 500-person enterprise with procurement committees.
Ignoring Negative Indicators
Some signals should reduce scores:
- Recently switched solutions
- Budget freeze announcements
- Known non-decision makers
Set-and-Forget Scoring
Review and adjust your scoring criteria monthly based on actual conversion data. What you think predicts success might not match reality.
"The goal isn't to score leads perfectly—it's to spend your time on prospects who are 3x more likely to buy."
Building Your Lead Scoring System
Week 1: Analyse your last 100 closed deals for common patterns
Week 2: Build your scoring criteria based on actual data
Week 3: Implement the scoring script with your lead database
Week 4: Test the system and measure conversion improvements
Free Lead Scoring Kit
I've put together a complete lead scoring kit including:
- The exact Python script I use for automated scoring
- Sample CSV template with example data
- Scoring criteria worksheet
- Excel version for non-coders
This isn't about perfect prediction—it's about working smarter, not harder. When you focus your energy on prospects who actually convert, everything changes.
Stop calling everyone. Start scoring everyone.