NR Setter Qualification Scorecard

Lead data (raw + Apollo enrichment)

{
  "name": "...", "bio": "...", "website": "...", "category": "...",
  "follower_count": null, "review_count": 85, "rating": 4.9,
  "has_booking_link": true, "is_running_ads": false,
  "sources": ["google_maps"], "email": "...", "phone": "...",
  "ig_handle": "...", "linkedin_url": "...",
  "apollo_headcount": 12, "apollo_revenue": 2500000,
  "apollo_industry": "Accounting", "apollo_founded_year": 2018,
  "apollo_funding_total": null, "apollo_technologies": [],
  "apollo_org_name": "...", "apollo_enriched": true
}

Any field can be null or missing.

Scoring Rubric

Each criterion is scored independently, 0-20 points, for a total of 0-100. The five criteria are designed to answer one question: "Is this lead worth a setter's time?"

1. Social Proof / Reviews (0-20)

This measures whether the lead has public credibility signals. Leads with strong social proof are easier to close because prospects can verify their track record.

Use review_count and rating as primary signals.

2. Review / Proof Volume (0-20)

This overlaps with social proof but focuses on the raw volume of evidence — testimonials, case studies, client mentions. High volume means consistent delivery over time, which correlates with ability to pay for NR's services.

When review data is missing, estimate conservatively from social signals (follower engagement, testimonials in bio, case studies on website).

3. Real Audience Size — Cross-Platform (0-20)

Audience size indicates reach and demand capacity. But this criterion works differently for the two ICP segments because professional service firms don't build audiences the same way coaches do.

For coaches/consultants (Segment B):

For professional service firms (Segment A) without social presence:

Use alternative credibility signals instead — these firms don't need followers to be great prospects:

• Apollo headcount 50+ → 16-20
• Apollo headcount 20-49 → 12
• Apollo headcount 10-19 → 6-8
• Apollo revenue $5M+ → 16-20
• Apollo revenue $1-5M → 12
• Review count 50+ → 12-16
• Founded 10+ years ago → bonus 2-4 points

Use the highest applicable score, not a sum.

4. Offer Fit — High-Ticket, $3K+ (0-20)

NR's services are priced for businesses selling $3K+ offers. A lead selling $50 ebooks can't afford NR and won't see ROI. This criterion gates for economic fit.

Signals for clear fit:

• estimated_ticket is 3k_5k or higher
• Professional service firm with Apollo revenue > $500K and headcount > 3
• Booking link + running ads (paid consultation model)
• Bio/website mentions premium pricing, retainers, or enterprise clients

Signals for partial:

• estimated_ticket is unknown but industry norms suggest $3K+
• Coaching model with large audience but unclear pricing

5. Monthly Demand — Estimated Booked Calls (0-20)

This estimates whether the lead has enough inbound demand to justify NR's acquisition system. A lead with 5 calls a month doesn't need what NR offers. A lead drowning in 150+ calls/month is the ideal — they need systems to scale.

This is an estimate — triangulate from:

• is_running_ads = true → strong signal, likely 50+ calls/month
• has_booking_link = true → confirmed call-booking model
• Apollo headcount > 10 → team to handle volume
• Apollo revenue > $1M → revenue implies consistent inbound
• High review count → high customer throughput
• Large follower count + booking link → likely high call volume

When unsure between two tiers, pick the lower one. False positives waste setter time — a setter who shows up to a call with inflated expectations loses trust in the pipeline.

Score Tiers

If the classification vertical is disqualified, assign score 0 and tier dq automatically.

The tier assignment follows the score mechanically — the scorer doesn't override tiers based on gut feel. The rubric is the system of record.

Setter Notes

Write 1-2 sentences a setter can glance at before reaching out. These should be actionable — reference specific data points, not generic praise.

Good: "Tax advisory firm with 85+ Google reviews and 12-person team in Dallas. Lead with how NR helped Amanda Han CPA scale client acquisition."

Bad: "Looks like a promising lead with good potential."

Include:

• What the lead does (plain language, not the vertical code)
• The key signal that drove the score (why they're good or why they fell short)
• A suggested outreach angle if score >= 50

Account & Channel Assignment

After scoring, route the lead to the right outbound account and contact channel. Read references/account_assignment.md for the full mapping tables and the reasoning behind each rule.

The short version:

• Each vertical maps to a primary and secondary setter
• Channel priority: LinkedIn for professional service verticals (if they have a profile), email for 100K+ audience leads, IG for everyone else
• Flag partnership-track leads if they're in agency verticals

Output format

Return only valid JSON — no markdown wrapping, no explanation:

{
  "setter_score": 76,
  "score_breakdown": {
    "social_proof": 16,
    "review_volume": 16,
    "audience_size": 12,
    "offer_fit": 20,
    "demand_indicator": 12
  },
  "score_tier": "standard",
  "notes": "CPA firm with 65+ Google reviews and 12-person team in Dallas. Has booking link but not running ads. Lead with tax strategy pain points.",
  "assigned_account": "Britt",
  "secondary_account": "Wade",
  "primary_channel": "linkedin",
  "is_partnership_track": false
}

Examples

Example 1: Strong Segment A lead Classification: {"vertical": "tax_strategy", "offer_type": "service", "estimated_ticket": "10k_25k", "audience_tier": "unknown", "demand_signals": ["high_reviews", "has_team", "booking_calls"]} Lead data: review_count=92, rating=4.9, has_booking_link=true, apollo_headcount=8, apollo_revenue=1800000, apollo_founded_year=2015, linkedin_url="https://linkedin.com/company/apex-tax"

Output:

{"setter_score": 82, "score_breakdown": {"social_proof": 16, "review_volume": 16, "audience_size": 12, "offer_fit": 20, "demand_indicator": 18}, "score_tier": "priority_a", "notes": "Tax advisory firm, 92 Google reviews at 4.9 stars, 8-person team, $1.8M revenue. Has booking link and strong fundamentals. Lead with scale story — similar firms 2x'd inbound with NR.", "assigned_account": "Britt", "secondary_account": "Wade", "primary_channel": "linkedin", "is_partnership_track": false}

Example 2: Mid-tier coach Classification: {"vertical": "str_coaching", "offer_type": "coaching", "estimated_ticket": "3k_5k", "audience_tier": "mid", "demand_signals": ["running_ads", "booking_calls"]} Lead data: follower_count=78000, is_running_ads=true, has_booking_link=true, review_count=null, apollo_headcount=3, email="[email protected]"

Output:

{"setter_score": 62, "score_breakdown": {"social_proof": 0, "review_volume": 0, "audience_size": 12, "offer_fit": 20, "demand_indicator": 16}, "score_tier": "nurture", "notes": "STR coaching program with 78K followers, running ads and booking calls. No review data which drags down score. Worth nurturing — if they can show testimonials, score jumps to standard.", "assigned_account": "Wade", "secondary_account": "Ben", "primary_channel": "ig", "is_partnership_track": false}

Example 3: Disqualified lead Classification: {"vertical": "disqualified", "offer_type": "course", "estimated_ticket": "under_3k", "audience_tier": "mid", "demand_signals": ["running_ads"], "disqualify_reason": "crypto keyword in bio"}

Output:

{"setter_score": 0, "score_breakdown": {"social_proof": 0, "review_volume": 0, "audience_size": 0, "offer_fit": 0, "demand_indicator": 0}, "score_tier": "dq", "notes": "Disqualified — crypto/trading vertical. Not an NR ICP fit regardless of audience size.", "assigned_account": "Scott", "secondary_account": "Ben", "primary_channel": "ig", "is_partnership_track": false}

Guardrails

• The 5 breakdown scores must sum to the total setter_score. This is a mechanical check — if they don't add up, the output is wrong.
• Zero supporting data for a criterion means 0 points for that criterion. Inflating scores on thin data is the most common failure mode — it sends weak leads to setters who then lose confidence in the pipeline.
• Disqualified leads from the classifier get score 0 across the board, tier dq. Still write notes (explaining the DQ) and assign account/channel (for logging, even though they won't be pushed).
• When classification data is missing entirely, return score 0, tier dq, and note that classification is required first.

Failure handling

• If the lead is missing critical fields (no name, no website, no bio, no Apollo data), score based on whatever is available and note the data gaps in setter notes. A partial score is more useful than refusing to score.
• If you're unsure between two point values on a criterion, go lower. The cost of a false positive (wasted setter call) is higher than the cost of a false negative (lead stays in nurture a bit longer).