How to Build a Lead Scoring System Using Observable Public Signals
A repeatable framework for ranking prospects based on what you can observe before making contact. No guesswork, no gut feelings - just structured signal analysis.
What Lead Scoring Actually Means
Lead Scoring
Definition: A systematic method of assigning numerical values to prospects based on observable attributes, producing a ranked list that determines contact priority.
Observable Signal
Definition: Any publicly available piece of business information - website presence, review count, rating, listing completeness, social profiles - that can be checked without contacting the business.
Why Score Instead of Just Filtering
- Filtering is binary - in or out. Scoring is a spectrum that tells you who to contact first, second, and last.
- A business with no website but 200 reviews is different from one with no website and 3 reviews. Scoring captures that difference.
- Scoring forces you to define what matters before you start outreach, removing emotional bias from prospect selection.
- After running outreach, you can correlate scores with reply rates and refine the model over time.
Core Principle
A lead score is not a prediction of whether someone will buy. It is a priority rank based on observable opportunity signals. The business with the highest score gets contacted first - not because they will definitely respond, but because observable evidence suggests they are most likely to need what you offer.
The Six Core Signals
These are publicly observable data points available for most local businesses. Each signal can be checked without contacting the business, using their online listings, website, and public profiles.
Signal 1: Website Presence
Does the business have a working website? A business with no website that still has reviews and a Google listing is actively operating without basic digital infrastructure.
Signal 2: Review Profile
Reviews reveal business health. High review count with a strong rating signals an active, established business. Low count may signal a new business or one that does not encourage feedback.
Signal 3: Listing Completeness
An incomplete listing - missing phone number, no business hours, no photos - signals a business that has not invested time in its online presence. This is an observable gap.
Signal 4: Business Category
Some industries are inherently better fits for your service. The category tells you whether this business operates in a sector where your offer makes sense.
Signal 5: Verified Status
An unclaimed or unverified listing suggests the business owner has not taken ownership of their online presence. This is a strong opportunity signal for digital services.
Signal 6: Contact Data Quality
A lead you cannot contact is not a lead. The availability and quality of contact information directly affects whether you can act on the opportunity.
Building the Scoring Matrix
Scoring Formula
Each signal gets a weight (how important it is to your specific service) and a point value (what the observed data shows). Total score ranges from 0 to 100.
| Signal | Suggested Weight | High Points (Max) | Low Points (Min) |
|---|---|---|---|
Website Presence | 25% | No website = 25 pts | Modern website = 5 pts |
Review Profile | 20% | 50+ reviews, 4.0+ = 20 pts | 0 reviews = 2 pts |
Listing Completeness | 15% | Incomplete = 15 pts | Fully complete = 3 pts |
Business Category | 15% | Perfect fit = 15 pts | Poor fit = 0 pts |
Verified Status | 10% | Unclaimed = 10 pts | Claimed + active = 2 pts |
Contact Data Quality | 15% | Email + Phone = 15 pts | No contact = 0 pts |
Weights Are Service-Dependent
The weights above are a starting framework. If you sell web design, website presence should be weighted higher. If you sell reputation management, the review profile matters more. Adjust weights to match what you actually offer. The structure stays the same - only the weights change.
Scoring in Practice: Hypothetical Walkthrough
For illustration: The following examples use hypothetical businesses to demonstrate how the scoring framework works in practice. Your actual scores will depend on the real data in your lead list.
| Signal | Example A: Plumber | Example B: Dentist | Example C: Salon |
|---|---|---|---|
| Website | None → 25 pts | Outdated → 18 pts | Modern → 5 pts |
| Reviews | 87 reviews, 4.6★ → 20 pts | 12 reviews, 3.8★ → 10 pts | 3 reviews, 5.0★ → 4 pts |
| Listing | Missing hours → 12 pts | No photos → 10 pts | Complete → 3 pts |
| Category Fit | Perfect → 15 pts | Good → 12 pts | Moderate → 8 pts |
| Verified | Unclaimed → 10 pts | Claimed → 2 pts | Claimed → 2 pts |
| Contact Data | Email + Phone → 15 pts | Phone only → 8 pts | Email + Phone → 15 pts |
| Total Score | 97 / 100 | 60 / 100 | 37 / 100 |
What This Tells You
The plumber scores highest not because they are the biggest business, but because observable signals show the widest gap between business activity (high reviews) and digital investment (no website, unclaimed listing). That gap is the opportunity. Contact the plumber first, the dentist second, the salon last - or skip the salon entirely if your list is large enough.
Setting Priority Thresholds
Tier A: 70–100
Contact immediately. These leads show multiple strong opportunity signals.
- Personalize every message
- Reference specific observed gaps
- Follow up at least 3 times
Tier B: 40–69
Contact after Tier A is exhausted. Some signals present but not a strong multi-signal match.
- Semi-personalized templates
- 1–2 follow-ups maximum
- Test different angles
Tier C: 0–39
Skip or save for later. Few opportunity signals or poor contact data.
- Do not waste personalization effort
- Re-score in 3–6 months
- May indicate dead or declining business
Threshold Calibration Rule
If Tier A has more than 50% of total leads → raise the threshold by 5 points.
The goal is a Tier A bucket of roughly 20–30% of your total list. Too narrow means wasted leads. Too wide means diluted effort. Adjust thresholds after your first scoring pass.
Common Scoring Mistakes
Wrong Approach
- Scoring all signals equally regardless of your service
- Using gut feeling to override the score for individual leads
- Scoring once and never updating weights based on results
- Making the model too complex with 15+ signals
- Treating lead scoring as a one-time task instead of an iterative process
Correct Approach
- Weight signals based on what you sell and who buys it
- Trust the score - contact Tier A first, always
- Review weights after every 100 outreach attempts and adjust
- Keep the model simple - 6 signals is enough to start
- Track which score ranges produce replies and refine thresholds
Frequently Asked Questions
What is lead scoring based on observable signals?
It is a method of assigning numerical values to prospects using publicly available data - website presence, review count, rating, listing completeness, verification status, and contact data quality - to rank which businesses to contact first.
How many signals should a scoring model include?
Start with 6 or fewer. More signals add complexity without proportional accuracy gains. A simple model you actually use outperforms a complex model you abandon after one attempt.
How do I set the right weights for each signal?
Start with the weights that match your service. If you sell web design, weight website presence highest. If you sell reputation management, weight review profile highest. After 100 outreach attempts, compare reply rates across score ranges and adjust weights toward the signals that correlate with replies.
Does a high score guarantee a response?
No. A high score means observable evidence suggests this business has a gap that matches your service. It does not guarantee they will respond. It does guarantee you are spending your outreach time on the most promising prospects rather than random ones.
How often should I update my scoring model?
Review and adjust weights after every 100 outreach attempts. If leads scoring 80+ reply at the same rate as leads scoring 50, your weights need recalibration. The model improves with data from actual outreach results.
Can I score leads using a spreadsheet?
Yes. A spreadsheet with columns for each signal, a points column per signal, and a SUM formula for total score is all you need. No special software required. The framework is deliberately simple enough to run in any spreadsheet tool.
Key Takeaways
Score, Do Not Just Filter
Filtering is binary. Scoring creates a priority spectrum that tells you who to contact first, second, and last - maximizing your limited outreach time.
Six Signals Are Enough
Website presence, review profile, listing completeness, category fit, verified status, and contact data quality cover the observable signals that matter most for local business outreach.
Weights Depend on Your Service
The same scoring framework works for web design, SEO, reputation management, and marketing - only the signal weights change based on what you sell.
Three Tiers, Three Actions
Tier A (70–100): personalize and pursue. Tier B (40–69): template and test. Tier C (0–39): skip or revisit later. Match effort to score.
Iterate After 100 Contacts
A scoring model improves with real outreach data. After 100 contacts, compare reply rates across score ranges and recalibrate weights toward signals that actually predict responses.
Gaps Are the Opportunity
The highest-scoring leads are not the biggest businesses. They are the ones with the widest gap between business activity and digital investment. That gap is what you sell into.