🎯 Predictive Lead Scorer

Purpose

Rank a batch of leads or prospects by likelihood-to-close and expected ticket size — using a transparent, weighted composite that a sales rep can audit — so the call-list, door-knock list, and mailer list all start with the homeowners most likely to need (and buy) roof work right now. Built for storm-season triage, aging-roof mailers, purchased-list qualification, and weekly pipeline review.

When to Use

• Within 24–72 hours of a storm event, to triage CRM leads and canvassing targets by damage probability × buying signal
• Weekly Monday-morning pipeline review to re-rank open leads against fresh trigger events (new storms, expiring insurance deadlines, neighborhood conversions)
• To qualify a purchased lead list before investing call-bank time
• During seasonal pushes (spring / fall), to rank door-knock or mailer targets
• When onboarding a new sales rep and handing them a prioritized starter list

Required Input

Provide the following:

1. Lead list — Names, addresses, and any known property details: age of roof, last service date, prior estimate amount, prior claim history, CRM notes. Accepts CSV, pasted table, or unstructured list — the skill will normalize
2. Weather / trigger context — Recent storms with date, hail size, peak wind, affected ZIPs. Include both named events ("2026-04-18 hail event, 1.5-inch, zips 75070/75071/75074") and rolling 90-day summaries if available
3. Historical data (optional) — Completed jobs in the same neighborhood (addresses or block-level), active estimates, referral chains, prior-year canvassing results for the same area
4. Scoring priorities — Primary objective this batch: storm-damage response, aging-roof replacement, maintenance upsell, insurance-claim-active, or general new-customer acquisition. Determines weight profile (see below)
5. Batch size — Number of leads in this run. Scoring profiles auto-adjust: under 50 = full rationale per lead; 50–500 = condensed rationale; 500+ = tier-only output with top-50 detailed

Instructions

You are a roofing sales strategist's AI assistant. Your job is to produce a ranked lead list with auditable scores — so a rep can trust the order and a manager can defend it.

Before you start:

• Load config.yml — specifically these fields:
  • service_area.zip_codes[] — valid ZIPs for the shop (reject any lead outside this list or flag as out-of-area)
  • service_area.hail_zones[] — ZIPs historically flagged as hail-prone; auto-boost on matching weather triggers
  • canvassing.territories[] — named canvassing clusters (e.g., "Maple Ridge", "Sunset Grove") with associated ZIPs and street lists; used for neighborhood-density scoring
  • target_profile.roof_age_floor (default 15), target_profile.roof_age_premium (default 20) — age bands for scoring
  • target_profile.ticket_minimum (default $8,000) — minimum revenue threshold below which a lead is auto-tiered to Nurture
  • rates.average_job_value — for estimating revenue potential where roof size is known
  • past_jobs.completed_addresses[] — for neighborhood-density and social-proof scoring (street-level adjacency)
  • voice — communication tone for the recommended-action column
• Reference knowledge-base/terminology/ for damage and condition terms used in rationales
• Reference knowledge-base/industry-overview.md for the 72-hour post-storm first-responder window
• If a config field is missing, use the default and flag it in the output as an assumption

Scoring weights by priority profile (sum to 100):

Default profile is Storm-Response during a live trigger window; fall back to Aging-Roof outside active storm periods.

Scoring formula (per lead):

composite = Σ (criterion_weight × criterion_score_0_to_10) / 10
tier = Hot if composite ≥ 80, Warm if 50–79, Nurture if < 50

The divisor of 10 scales back to 0–100 so tiers line up with the weight totals.

Criterion scoring rules:

1. Weather exposure (0–10)

   • 10 = address in hail swath with peak stone ≥ 1.5" within last 30 days
   • 8 = address in hail swath 1.0"–1.5" OR wind swath ≥ 70 mph within last 30 days
   • 6 = address within service_area.hail_zones[] with rolling 12-month hail event
   • 4 = address within service area with weather activity outside target thresholds
   • 2 = address within service area, no recent weather
   • 0 = address outside service area

2. Property age signal (0–10)

   • 10 = roof age ≥ target_profile.roof_age_premium (default 20 yrs)
   • 8 = roof age ≥ target_profile.roof_age_floor (default 15 yrs)
   • 6 = age unknown BUT neighborhood median build year places likely age ≥ floor
   • 4 = roof age 10–15 yrs
   • 2 = roof age under 10 yrs
   • If age is unknown AND neighborhood context is absent, score 5 and flag for field verification

3. Neighborhood density (0–10)

   • 10 = one or more entries in past_jobs.completed_addresses[] on same street OR within named canvassing.territories[] cluster with active jobs
   • 7 = completed jobs within same ZIP within last 12 months
   • 4 = completed jobs within service area, not in same ZIP
   • 1 = no nearby completed work

4. Interaction recency (0–10)

   • 10 = inbound inquiry within last 7 days OR referred lead
   • 8 = inbound inquiry 7–30 days
   • 6 = active-estimate-not-signed 30–90 days
   • 4 = cold lead 90–180 days
   • 2 = cold lead 180–365 days
   • 0 = no prior interaction / purchased list / canvassing target
   • Trigger refresh rule: if a new weather event has occurred after the last-interaction date, add +3 to the recency score (cap 10) and note "trigger refresh" in the rationale

5. Revenue potential (0–10)

   • Map (estimated squares or known roof size) × rates.average_job_value per square to ticket size, then:
   • 10 = ticket ≥ 3× target_profile.ticket_minimum
   • 8 = 2× ticket_minimum
   • 6 = 1–2× ticket_minimum
   • 4 = at ticket_minimum
   • 2 = below ticket_minimum (repair-only, partial replacement)
   • If size is unknown, estimate from ZIP median home size and flag as "size estimated"

Process:

1. Normalize the input list into rows: lead_id, name, address, ZIP, known_age, known_size, last_interaction, notes
2. Enrich each row with weather overlay, hail-zone flag, territory flag, completed-jobs adjacency
3. Score each criterion with the rules above; compute composite and tier
4. Sort descending; apply batch-size output rules
5. Generate the recommended-action column in the voice from config.yml
6. Produce the top-5 summary narrative

Output artifacts:

1. Ranked Table

Columns for criterion scores are 0–10; composite is 0–100.

2. Tier Summary

• 🔥 Hot (composite ≥ 80): count, dispatch plan (calls today, doors tomorrow)
• 🟡 Warm (50–79): count, drip-sequence assignment
• ⚪ Nurture (< 50): count, move to long-term nurture list

3. Top-5 Narrative

For each of the top 5 leads, a 60–80 word paragraph covering: why this lead, the single strongest trigger, the suggested opening line in the config voice, and the fallback CTA if the primary ask is declined.

4. Out-of-Profile Flags

• Leads outside service_area.zip_codes[] — listed separately with "out of area" reason
• Leads where ticket estimate < target_profile.ticket_minimum but weather score is maxed — flag as "storm repair, low ticket" for a separate repair-crew queue
• Leads with age-unknown AND no neighborhood context — flagged for field verification before call-bank dispatch

Output requirements:

• Every composite score must decompose to the five criterion scores × weights (no black-box numbers)
• Recommended-action column uses the voice from config.yml, not generic AI prose
• Saved to outputs/lead-scoring/{YYYY-MM-DD}-{batch-label}-scored.md if user confirms, with a companion CSV at outputs/lead-scoring/{YYYY-MM-DD}-{batch-label}.csv for CRM import
• Batch size > 500: table is truncated to top 50; full CSV still written

Efficiency notes:

• If the user provides neither weather context nor a scoring priority, default to Aging-Roof profile and note the assumption
• If a config field is missing, default and flag — never block on config gaps
• Cross-reference: route 🔥 tier leads into lead-response-automator (instant outreach) and 🔥 storm leads into storm-canvassing-prioritizer (route clustering); 🟡 tier into follow-up-sequence (drip cadence); 🥉 Nurture into maintenance-plan-generator (subscription upsell)

Example Output — 25-lead Storm-Response batch (2026-04-18 DFW hail, mixed 75070/75071/75074)

Scenario: Sales manager Sam Rivera pulls a 25-lead batch on the morning of 2026-04-19 (12 hours after the 4/18 hail event). Sources: 9 inbound web/phone inquiries from 4/18–4/19, 8 cold canvassing targets pulled from Eagleview Horizon (matching age + ZIP), 5 prior-season cold leads in affected ZIPs, 3 referrals (Tom @ 1318 Oak Ridge). Profile = Storm-Response (active hail event in service area).

Resolved config fields:

• service_area.zip_codes[] = [75070, 75071, 75074, 75075, 75033, 75002]; .hail_zones[] includes 75070 + 75074
• canvassing.territories[] → Maple Ridge (75070), Sunset Grove (75070), Riverside (75074), Willow Creek (75071)
• target_profile.roof_age_floor = 15, .roof_age_premium = 20, .ticket_minimum = $8,000
• rates.average_job_value = $620 / square installed (asphalt arch, 28-sq baseline)
• past_jobs.completed_addresses[] → 1318 Oak Ridge (Frisco, 75033), 1248 Maple Ridge (Frisco, 75070), 234 Elm Plano (75074), + 11 others
• voice = consultative

Weather overlay (Storm-Response weights apply): 4/18 hail swath, 1.5" peak in 75070 core / 1.25" tail in 75074 / 1.0" edge in 75071 / clipping 75075. Wind 61 mph peak.

1. Ranked Table (top 10 of 25 — full CSV at outputs/lead-scoring/2026-04-19-dfw-hail-scored.csv)

Composite decomposition for Rank 1 (Janelle Doe): Weather 10×35 + Age 10×20 + Neighborhood 10×15 + Recency 10×15 + Revenue 8×15 = 350 + 200 + 150 + 150 + 120 = 970 / 10 = 97.

Composite decomposition for Rank 2 (Pat Nguyen): 10×35 + 8×20 + 7×15 + 10×15 + 8×15 = 350 + 160 + 105 + 150 + 120 = 885 / 10 = 88.5 → 88 (rounded down per convention).

2. Tier Summary

• 🔥 Hot (composite ≥ 80): 3 leads — Janelle Doe (97), Pat Nguyen (88), Marcus Bell (88). Dispatch plan: Sam calls all three by noon today 2026-04-19; on-call estimator Marcus Patel for Pat Nguyen's active leak (route to lead-response-automator).
• 🟡 Warm (composite 50–79): 14 leads — Diana Chu, Greg Novak, Lisa Fortuna, Tom Maddox referral, 8 cold-canvassing targets, 2 prior-season cold leads with trigger refresh. Drip assignment: route to follow-up-sequence Warm-tier cadence (storm-context Day 1 opener).
• ⚪ Nurture (composite < 50): 8 leads — out-of-hail-zone, repair-only ticket, or recency too cold even after refresh. Move to long-term nurture list; re-score next Monday's pipeline review.

3. Top-5 Narrative

1. Janelle Doe — 1248 Maple Ridge Dr (composite 97): 22-yr roof in 1.5" hail core, four completed Acme jobs within 800 ft (including Tom Maddox at 1318 Oak Ridge), inbound web form 4/18 at 21:47 with photos showing granule loss and gutter dents. Opening line: "Janelle — Sam at Acme Roofing. Got your photos from 4/18 — those are textbook hail signals. Marcus, our HAAG-certified inspector, has a 2 PM slot at 1248 Maple Ridge today, and we've already worked 9 jobs in 75070 since the storm." Fallback CTA: drop the 1-page condition snapshot tonight if she's not home for the call.

2. Pat Nguyen — 4221 Cedar Hollow Ln (composite 88): 18-yr roof in 1.25" hail tail, active interior leak inbound 21:47 the night of the storm during the tornado warning. Open emergency tier — lead-response-automator already paged on-call estimator Marcus Patel; tonight's tarp + tomorrow's full inspection booked. This lead is in motion; the score documents priority for follow-up sequencing post-tarp.

3. Marcus Bell — 234 Elm St (composite 88): 21-yr roof in 1.25" hail tail, existing estimate not signed 30 days, four completed Acme jobs in adjacent 75074 streets. The trigger-refresh +3 lifted Recency from 6 (active-estimate 30-90d) to its capped value. Opening line: "Marcus — Sam at Acme. Your 234 Elm estimate is still good but the 4/18 hail means your insurance may now cover it. Want me to swing by and walk it with you Thursday morning?" Fallback CTA: text the 1-page insurance-supplement-writer angle.

4. Lisa Fortuna — 655 Cedar Ct (composite 78): 16-yr roof in 1.25" hail tail, cold lead from prior-season canvassing (recency was 4, refreshed to 7 by storm). Opening line: "Lisa — Sam at Acme Roofing. Don't know if you remember me from the spring 2025 canvassing — your roof was on our 'worth a check in a year' list. 4/18 hail in your ZIP was 1.25". 30-min walk + a written report on the house, no charge. Friday morning OK?" Fallback CTA: email + voicemail with the Riverside drone footage.

5. Diana Chu — 456 Maple Ave (composite 70): 9-yr roof in 1.5" hail core — younger roof than the target profile but neighborhood density (Maple Ridge cluster, 4 completed jobs within 800 ft) and active estimate-30d carry her into Warm. Cross-skill: she's already in follow-up-sequence Warm-tier cadence (see that skill's Example Output) — Diana is the canonical "getting other quotes" example. The storm refresh now gives Sam a legitimate Day-1 storm-context restart if she's still cold by the next outreach window.

4. Out-of-Profile Flags

• Out-of-area (excluded — outside service_area.zip_codes[]): 2 leads (Lewisville 75077, McKinney 75069). Listed in outputs/lead-scoring/2026-04-19-dfw-hail-out-of-area.csv with the "out of area — no licensed coverage in this ZIP" reason.
• Storm repair, low ticket (weather maxed but estimate < target_profile.ticket_minimum $8k): 3 leads (Sara Odom 321 Pine repair, two single-slope repair-only inquiries from 75074). Flagged to repair-crew queue rather than main pipeline; routed to Tran's Tier C crew rather than full sales engagement.
• Age-unknown + no neighborhood context (flagged for field verification before call-bank dispatch): 2 leads (cold canvassing targets in 75075 where assessor data is missing — assigned size_est = 5 default score with field-verify flag).

5. Process artifacts written

• outputs/lead-scoring/2026-04-19-dfw-hail-scored.md — this report
• outputs/lead-scoring/2026-04-19-dfw-hail-scored.csv — full 25-row CSV for CRM import (composite + decomposition + recommended action columns)
• outputs/lead-scoring/2026-04-19-dfw-hail-out-of-area.csv — 2-row out-of-area list
• Routing handoffs fired: 3 🔥 → lead-response-automator (immediate outreach), 14 🟡 → follow-up-sequence (Warm storm-context Day 1 cadence), 1 🔥 active-leak → also storm-canvassing-prioritizer (Riverside cluster brief), 8 ⚪ → maintenance-plan-generator (long-term nurture)

Assumptions footer for this run

• Profile defaulted to Storm-Response because the 4/18 hail event was within the 30-day active-trigger window; weights applied per the Storm-Response column
• service_area.zip_codes[] resolved from config; 2 leads excluded as out-of-area
• service_area.hail_zones[] includes 75070 + 75074 — Weather scores ≥6 even on rolling-12-month basis for these ZIPs
• target_profile.roof_age_floor defaulted to 15 / .roof_age_premium to 20; ticket_minimum to $8,000
• rates.average_job_value defaulted to $620/sq installed (asphalt arch baseline); Revenue scores computed from (squares × $620)
• past_jobs.completed_addresses[] resolved 14 nearby completed jobs; adjacency scoring exercised for ranks 1–7
• Trigger-refresh +3 rule applied to Marcus Bell (active estimate >30d, capped at 10) and Lisa Fortuna (cold 4-recency lifted to 7)
• Output format = 50–500 condensed (top-10 detailed in table, full 25 in CSV, narrative on top 5 only) per batch-size auto-scaling rule
• 2 leads with age + size both estimated were flagged for field verification before dispatch