Solar Lead Scoring Model for Indian EPCs: A Practical Framework

Your sales rep has 40 leads in the phone this month. He visits the first 15 who called, closes two, and misses the three serious buyers sitting at position 18, 26, and 33. That is not a lead problem. That is a sequencing problem, and a solar lead scoring model fixes it.

India added a record volume of rooftop solar under PM Surya Ghar Muft Bijli Yojana, with over 10 lakh installations reported on the PM Surya Ghar National Portal in 2025. Lead volume is not scarce anymore. Time is. A typical 12-person EPC doing ₹40 to 80 lakh GMV per month can run about 50 to 60 site visits a month with four field reps. If half those visits go to leads who never had the budget, the roof, or the intention, you have burned two rep-months of salary and travel on nothing.

This post gives you a working points model you can put on a whiteboard today. It is different from our post on lead scoring features inside solar CRMs, which covers software. This one is the actual math: which signals, how many points, what thresholds, and a worked example scored line by line.

What is solar lead scoring, in plain terms

Lead scoring is a system where each incoming lead gets a number based on how likely it is to buy. High number, visit this week. Low number, nurture over WhatsApp. Nothing more mystical than that.

In B2B software, scoring often mixes firmographic data (company size, industry) with behavioural data (opened email, visited pricing page). In Indian residential solar, most of that does not apply. Your lead is a homeowner in Surat or a factory owner in Pune. What predicts a sale is far simpler: does he own the roof, can he pay, does his bill justify a system, and does he want it this quarter.

That is why a solar-specific model uses only four to six signals. Anything more and your reps stop filling it in, which kills the whole system. A score that takes 90 seconds to fill beats a perfect score that takes 20 minutes, because the 90-second one actually gets used on every lead.

Why gut-feel qualification fails for solar EPCs

Most EPC owners we talk to qualify by instinct. The rep calls, chats for five minutes, and declares the lead "serious" or "timepass". Instinct works at 10 leads a month. It collapses at 40.

Here is why. First, instinct is not transferable. The owner's gut is good because he has closed 500 deals. The new sales boy has closed 12. Second, instinct has no memory. A lead judged cold in March gets a subsidy-driven push in May, and nobody calls him back because "woh timepass tha". Third, instinct cannot be audited. When conversion drops, you cannot ask instinct what changed.

The demand side is only getting louder. The Ministry of New and Renewable Energy (MNRE) reported record rooftop additions in 2024 to 2025, and Mercom India tracked continued residential growth through 2025, and JMK Research has mapped rising rooftop tenders and consumer awareness across states. The Press Information Bureau reported in 2025 that PM Surya Ghar was among the fastest-scaling rooftop programmes globally. More demand means more junk mixed into your pipeline. Scoring is how you find the gold without touching every stone.

Our opinionated take, backed by our own platform numbers: a points model run on a phone call beats a sophisticated model run in a spreadsheet. The spreadsheet model dies in week three. The phone-call model survives because it fits the way your team already works.

The 60-Point Solar Lead Score: the framework

The 60-Point Solar Lead Score is the model we recommend to Indian residential EPCs. Four signals, 60 points total. Budget carries 20 points, roof ownership 15, monthly electricity bill 15, and decision timeline 10. You score every lead on the first call, in under two minutes.

Why these four? Because each one kills deals independently. No budget, no sale. Tenant with no roof rights, no sale. ₹900 monthly bill, the payback math never closes. No timeline, the lead sits in your pipeline for nine months and rots. Anything that cannot kill the deal alone does not belong in the core score.

1. 1

   Budget (20 points)

   Ask what range he has in mind or whether he plans a loan. Ready cash or sanctioned loan is 20. "Will take a loan if EMI works" is 12. No idea, no plan is 4.

2. 2

   Roof ownership (15 points)

   Owns the house with clear terrace rights is 15. Joint family property needing one more signature is 8. Rented or disputed property is 0, and usually disqualifying.

3. 3

   Monthly bill size (15 points)

   Above ₹3,000 a month is 15, because a 3 kW system pays back cleanly. ₹1,500 to 3,000 is 9. Below ₹1,500 is 3, the payback story is weak and you will fight on price all the way.

4. 4

   Decision timeline (10 points)

   Wants installation within 30 days is 10. One to three months is 6. "Just checking" or "next year" is 1. Subsidy deadlines and loan offers often push genuine buyers into the 30-day band.

Two optional modifiers, plus or minus 5 each. Add 5 if the lead came as a referral from a past customer (referrals close at roughly double the rate in our platform data, 2026). Subtract 5 if the lead is only asking for the cheapest quote across five EPCs with no other questions, the classic IndiaMART price-shopper pattern.

If you want the broader theory behind marketing qualified leads (MQL) and sales qualified leads (SQL), our glossary covers both. In practice for a 10 to 40 person EPC, a lead scoring above 25 is your working definition of an SQL.

A worked example, scored line by line

Theory is cheap. Here is a hypothetical example, fully invented for illustration, scored on the first call.

The lead: Mrs. Desai from Katargam, Surat, DGVCL (Dakshin Gujarat Vij Company Limited) area. She enquired through your website after her neighbour's 3 kW system went live. On the call you learn four things. Her monthly bill averages ₹3,400. She owns the bungalow outright. She has ₹2 lakh set aside and is open to a small top-up loan. She wants the system before Diwali, about 60 days away, to lock the ₹78,000 central financial assistance (CFA) under PM Surya Ghar.

Scoring her: budget 12 (partial cash, loan top-up needed), roof 15 (clear ownership), bill 15 (₹3,400 is above the ₹3,000 line), timeline 6 (60 days, not 30). Referral modifier +5, since the neighbour is your past customer. Total: 53 out of 60, plus the modifier makes 58. That is a hot lead. She gets a site visit within 48 hours and a subsidy-ready proposal the same evening.

Now compare with another hypothetical lead the same week. Mr. Patel, also Surat, bill ₹1,100, rents the first floor of his uncle's building, wants "the best price, sending to four companies". Score: budget 4, roof 0, bill 3, timeline 1, price-shopper modifier -5. Total 3. He does not get a site visit. He gets a WhatsApp message with your standard 3 kW pricing and a link to your EMI explainer, and your rep moves on.

Same week, same team, completely different allocation of the scarcest thing you own: rep-hours. For a deeper look at the questions behind each signal, read our guide on qualifying solar leads.

Set your thresholds: hot, warm, cold

A score without thresholds is just a number nobody acts on. We recommend three bands, each with a defined action and a defined owner. This is the part most EPCs skip, and it is why their scoring effort dies.

Notice the warm band action. Warm leads do not get a site visit first. They get a proposal. This is a deliberate tradeoff: a 60-second proposal costs you almost nothing, while a site visit costs ₹300 to 800 in fuel plus two hours of rep time. Sending a sharp proposal to a warm lead often converts him into a hot one, because the subsidy math in the PDF does the convincing. Our lead management workflow covers what happens after the visit, stage by stage.

Review the bands quarterly. If your close rate on hot leads drops below 25%, your thresholds are too loose. If warm leads keep converting without a visit, your hot threshold is too strict and you are wasting trips. Tracking this by rep and by source is covered in our lead management best practices post, and our guide on solar lead conversion rate gives you the benchmarks to compare against.

Signals you should ignore (and one misconception)

Here is the industry misconception we want to correct: lead source does not predict intent nearly as well as people think. Owners swear that IndiaMART leads are junk and website leads are gold. Across our platform data (2026), source explains far less of close-rate variance than bill size and roof ownership do. A ₹4,500-bill homeowner from a portal beats a ₹900-bill "premium" website lead almost every time. Score the buyer, not the channel.

Other signals to leave out of the core model:

Politeness bias is real. A chatty, flattering caller feels serious. A brusque one feels like a time-waster. Neither predicts payment. We have seen (hypothetical but typical) a gruff factory owner in Pune's MSEDCL (Maharashtra State Electricity Distribution Company Limited) belt sign a 25 kW order in one meeting, while a charming Bengaluru homeowner in the BESCOM (Bangalore Electricity Supply Company) area strung a rep along for eleven weeks. The four signals would have flagged both correctly.

Roll it out with your team this month

Adoption is the hard part, not arithmetic. Here is the rollout sequence that works for a 4 to 8 person sales team.

Week one: score your existing pipeline retroactively. Take every open lead, fill the four signals from your notes, and sort. You will immediately see the pattern, usually three to five hot leads nobody visited last week because newer leads kept arriving on top. That visible miss is what convinces the team, not your speech.

Week two: make scoring part of the first-call script. Four questions, 90 seconds. "Sir, bijli ka bill kitna aata hai? Chhat aapke naam hai? Budget ka kya socha hai? Kab tak lagwana chahenge?" Your reps already ask three of these. You are only writing the answers down as numbers.

Week three: tie dispatch to the score. No site visit gets scheduled without a score above 44, unless the owner overrides in writing. Expect grumbling. Hold the line for one month.

Week four: review the data. Compare close rates by band, cost per visit, and cost per closed deal. If you are spending ₹400 to 800 per visit in fuel and time, moving 10 junk visits a month to proposal-first handling saves ₹4,000 to 8,000 and two rep-days. Our post on cost per solar lead in India has the full acquisition-cost math, and how to track solar leads covers the logging discipline this depends on.

How QuickEstimate fits

Scoring only works if the score lives where the lead lives. In notebooks and Excel, scores go stale within a week and nobody re-sorts the list every morning. QuickEstimate keeps the score attached to the lead record, so the moment a warm lead reads your proposal twice on WhatsApp, you can bump him to hot and dispatch a visit, all from the phone.

• Lead Capture, pull enquiries from your website, Facebook Lead Ads, and IndiaMART into one scored list, no lead lost in a rep's personal WhatsApp.
• Pipeline Management, see every lead by stage and score, and spot which rep is sitting on hot leads without visiting.
• Sales Reports, track close rate by score band and lead source, so you can tighten thresholds with real numbers each quarter.

Priya's version of this: instead of a Monday meeting where five reps narrate their weeks from memory, she opens one dashboard, filters score above 45, and asks two questions. Who has not been visited? Why not? Ten minutes, done.

What to do this week

Do not build the perfect model. Build the 60-point one and run it for 30 days.

1. Today: Write the four signals and the threshold table on one sheet. Share it on your sales WhatsApp group.
2. Tomorrow: Retro-score your 20 newest open leads. Find the hot ones nobody visited. Visit them this week.
3. This week: Add the four scoring questions to your first-call script and make "no score, no visit" the rule for new leads.
4. In 30 days: Compare close rates by band against your baseline. Adjust thresholds once, then leave the model alone for a quarter.

If you want the scoring, routing, and follow-up reminders handled inside one app instead of a sheet, book a QuickEstimate demo and we will set up the 60-Point Solar Lead Score with your team live.

Frequently asked questions

What is a solar lead scoring model?

A solar lead scoring model is a points system that ranks every incoming solar enquiry by how likely it is to convert. Each buying signal, such as budget readiness, roof ownership, monthly electricity bill size, and decision timeline, earns points. The total tells your team who gets a site visit this week and who goes into a WhatsApp nurture track instead.

How many points should each signal carry in solar lead scoring?

In our 60-Point Solar Lead Score, budget carries 20 points, roof ownership 15, monthly bill size 15, and decision timeline 10. Budget gets the most because payment failure is the most common deal-killer in Indian residential solar. You can adjust weights after one quarter of close-rate data, but keep the total signals at four to six so reps actually fill them in.

What score makes a solar lead hot?

We recommend treating 45 out of 60 as the hot threshold. Hot leads get a site visit within 48 hours and a proposal the same day. Scores of 25 to 44 are warm and get a proposal or video call first. Below 25, the lead goes into automated nurture and gets re-scored after 30 days. Review the thresholds quarterly against your actual close rates.

Does lead source matter in scoring?

Far less than most EPC owners believe. Our platform data (2026) shows bill size and roof ownership predict close rate much better than whether the lead came from IndiaMART, Facebook, or your own website. Use source as a reporting dimension to manage your marketing spend, not as a scoring signal. Score the buyer, not the channel.

Can a small EPC run lead scoring without a CRM?

Yes, for a while. A printed sheet and a shared spreadsheet works up to roughly 30 leads a month with two reps. Past that, scores go stale, the sheet forks into three versions, and nobody re-sorts daily. That is the point where a mobile CRM with the score attached to the lead record pays for itself in recovered hot leads alone.

How is lead scoring different from lead qualification?

Qualification is a yes-or-no gate: does this lead meet minimum criteria to enter the pipeline. Scoring is a ranking: among all qualified leads, who comes first. You need both. Qualification removes the impossible leads, like tenants with no roof rights. Scoring sequences the possible ones, so your limited site-visit capacity goes to the highest-probability deals first.

What is a good close rate for hot-scored solar leads?

For Indian residential EPCs, hot-band leads should close at 25% or higher if your thresholds are calibrated. Unranked pipelines typically close at 8 to 12%, according to ranges we see across QuickEstimate platform data (2026). If your hot band closes below 20%, tighten the budget or bill-size criteria, because visits are leaking to leads who were never going to buy.

Should commercial solar leads use the same scoring model?

No, adjust it. Commercial and industrial leads need extra signals: sanctioned load in kW, roof or land area, and whether the signatory is the business owner or a facilities manager. The 60-point structure still works, but replace the monthly-bill signal with sanctioned load and add a decision-authority signal worth 10 points. Timeline matters less because commercial cycles run 60 to 120 days.