Lead Scoring
Lead Scoring is a structured method used in the solar industry to evaluate and rank potential customers based on their likelihood to purchase a solar system. By assigning numerical values (scores) to each lead based on behavior, demographics, property characteristics, financial readiness, and engagement signals, solar companies can focus their sales efforts on the highest-quality opportunities first.
In modern solar CRM and proposal platforms—including automated sales workflows powered by SurgePV—lead scoring plays a crucial role in reducing wasted time, boosting close rates, and accelerating growth. It allows solar sales teams to prioritize homeowners or businesses who are genuinely ready to move forward, rather than chasing unqualified leads.
Key Takeaways
- Lead Scoring ranks solar prospects based on their likelihood to convert.
- Allows teams to prioritize high-quality leads and not waste effort on unqualified prospects.
- Modern systems adjust scores automatically using behavior, shading data, finances, and proposal engagement.
- Increases close rates, reduces cost per acquisition, and strengthens workflow efficiency.
- Works best when integrated with automated proposal and CRM systems.
What Is Lead Scoring?
Lead scoring is the process of ranking solar leads using a weighted scoring model. Each lead gets a score based on criteria such as:
- Roof suitability
- Electricity consumption
- Credit readiness
- Engagement with proposals
- Response behavior
- Interest level
- Timeframe to purchase
Sales and marketing teams use these scores to determine:
- Who should be contacted first
- Which leads should get automated follow-up
- When a lead is ready for proposal or site visit
- Which campaigns generate the highest-value prospects
Lead scoring becomes even more powerful when combined with automation tools like Lead Nurture Automation and proposal platforms such as the Solar Proposal & Sales Hub.
How Lead Scoring Works
Although every company builds its own scoring model, the process generally looks like this:
1. Define scoring criteria
Common scoring factors in solar include:
- Monthly energy bill
- Roof orientation & shading
- Homeownership status
- Credit score range
- Interest level (form interactions, calls, chats)
- Timeline: “ASAP,” “1–3 months,” “Researching”
- Engagement with a proposal sent through Solar Proposals
2. Assign positive or negative points
Example:
- +20 points → High utility bill
- +35 points → Excellent roof space
- +15 points → Lead opened proposal
- –10 points → Low credit readiness
- –15 points → Renters / temporary housing
3. Auto-update scores based on behavior
Modern automation tools adjust scores automatically when a lead:
- Opens a proposal
- Books a call
- Clicks a follow-up email
- Visits the proposal link multiple times
- Uploads a utility bill
- Uses finance tools like Solar Loan Calculator
4. Prioritize leads
Leads are ranked as:
- Hot Leads → Ready to close
- Warm Leads → Active interest, nurturing needed
- Cold Leads → Low engagement
- Disqualified → Not eligible or unsuitable
5. Route leads to the right workflow
Sales teams only call leads that meet a threshold score, while others enter automated follow-up.
Types / Variants of Lead Scoring
1. Demographic Scoring
Attributes like homeownership, location, credit range, and property type.
2. Behavioral Scoring
Engagement actions such as opening proposals, clicking links, or using solar calculators.
3. Technical Suitability Scoring
Solar-specific criteria including:
- Roof pitch (analyzed using the Roof Pitch Calculator)
- Orientation & shading (from Shadow Analysis)
- Utility rate compatibility
- Energy consumption
4. Financial Scoring
Based on savings potential, loan eligibility, and ROI projections using tools like the Solar ROI Calculator.
5. Predictive / AI Lead Scoring
Uses machine learning to predict which leads will close based on historical datasets.
How It’s Measured
Lead scoring is expressed as a numerical score, often from 0–100 or 0–1000.
Typical scoring components:
Customer Intent Score
Based on submitted forms, engagement, or sales interactions.
Property Suitability Score
Incorporates roof geometry, shading, and system feasibility.
Financial Score
Based on savings potential and payment method (loan, cash, PPA).
Engagement Score
Tracks proposal views, appointment bookings, and email interactions.
Practical Guidance for Solar Sales Teams
✔ 1. Build a scoring model that matches your business goals
Include technical, financial, and behavioral criteria—not just marketing engagement.
✔ 2. Automate follow-ups for mid-tier leads
Use workflows from the Solar Proposal & Sales Hub to nurture leads.
✔ 3. Connect scoring with proposal engagement
A lead who views their proposal 3+ times is hot and should jump to the top of the call list.
✔ 4. Use shading and roof data to refine scores
Integrate Shadow Analysis or site data for realistic feasibility scoring.
✔ 5. Re-score leads dynamically
Every new action—bill upload, call booking, calculator use—should update the score.
✔ 6. Train teams to respond faster to high-scoring leads
Solar leads convert best when contacted within minutes, not hours.
Real-World Examples
1. Residential Lead Scoring Boosts Close Rate
A homeowner with a $250 monthly utility bill submits a request, uploads their bill, and opens their proposal twice.
Their score jumps from 55 → 92, triggering immediate sales outreach and a same-day close.
2. Commercial Client Evaluation
A small business with high daytime load receives a high suitability score due to flat-roof potential and strong ROI.
The sales team prioritizes a site visit, closing a 110 kW deal.
3. Low-Quality Lead Automatically Disqualified
A renter with heavy shading and unclear interest receives a score of 18, automatically routed to long-term nurture.
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