Score Leads

Score Leads

Tier leads as Hot / Warm / Cold with a response-time SLA tuned to the use case. Calls get_gtm_context(detailed: true) unconditionally, resolves leads by email (deterministic) or name+company (surface ambiguity), scores on four axes, and presents a scannable per-lead output with a specific "why now" reasoning snippet so the rep can trust the tier.

The bar

1. Tier and SLA are the first thing the rep sees — not buried under TL;DR or component breakdown.
2. Resolution accuracy 100% — every input bucketed; email typos fail loudly, never silent fallback to name search.
3. Every Hot lead carries verified contact data — phone + accuracy score visible. Bad data on a Hot lead = dial-the-wrong-number failure.
4. Every tier comes with a concrete next action — "Direct dial 555-1234. Lead with [signal]." Not "engage promptly."
5. Every lead carries a "why now" reasoning snippet — citing the specific axis driver (person seat × source × fresh trigger / intent / prior engagement). Never the composite restated; never generic ("strong fit"). Same trust discipline as score-accounts.
6. Output scannable in <30 seconds per row. Component breakdown below the fold.

Scope

Scores individual leads, not accounts. Use score-accounts for company-level prioritization. For Hot leads, chain to personalize-email.

Input

• Leads (required) — list of ZI person IDs / emails / name+company rows / mixed CSV.
• Source (recommended) — demo_request, pricing_inquiry, free_trial, product_signup, content_download_high_intent, content_download_low_intent, webinar_attended, webinar_registered, newsletter_subscribe, cold_inbound, unknown. If missing, ask once then default to unknown (source = 50, flagged).
• Use case (default inbound_routing) — inbound_routing, event_followup, pql_triage, content_follow_up. Drives SLA tuning.
• Weight overrides (optional) — {person, account, source, trigger} summing to 100.
• Tier thresholds (optional) — {Hot, Warm}. Cold is the remainder.

Four-axis framework

Weights overridable. Each axis 0–100; composite is the weighted sum.

Tier + SLA (varies by use case)

SLA defaults below. inbound_routing is the live-triage motion where speed-to-lead dominates; other motions relax accordingly. Pick what fits — don't manufacture urgency the motion doesn't need.

For high-intent sources (demo_request, pricing_inquiry, free_trial) in live-triage mode, fast response materially lifts qualification rate. Outside live-triage, the right SLA is longer.

Resolution (four-bucket, lead-specific)

• Auto-resolved — high confidence; score immediately.
• Verified — match found with caveats (common name at large co); surface verification note.
• Ambiguous — multiple plausible matches, no clear winner; pause scoring.
• Failed — no match. Never silently fall back to alternate identifier paths.

Routing by type:

• Numeric person ID → auto-resolved.
• Email → enrich_contacts(email). Email is a unique identifier. Match → auto-resolved. No match → failed. Do NOT auto-route to name search — a typo'd email (e.g., [email protected]) must not silently resolve to a different real person.
• Name + company → enrich_contacts(firstName/lastName/companyName). Single high-accuracy match → auto-resolved. Multiple plausible → verified with note. No match → failed.
• Free-text "John Smith at Acme" → parse and route to name+company path.

100% resolution accuracy is the gate.

Workflow

1. Pull GTM context (always)

get_gtm_context(detailed: true). Capture personas, ICP, strategic priorities (for intent-topic curation).

2. Honor input data first

Use user-supplied source / weights / thresholds / use case. If source is missing on a multi-row list, ask once then default to unknown (50, flagged).

3. Resolve identifiers

Per the four-bucket rules. Batch in groups of ≤10 concurrent.

3.5. Relationship-context pre-flight (mandatory)

Tag each lead's company against GTM context:

• competitor ⚔️ — in get_gtm_context.competitors. Hard-warn — most inbound from competitors is talent or competitive intel.
• customer 🤝 — in get_gtm_context.customers / proof_bank. Reroute to expansion / discovery_follow_up.
• partner 🔗 — in get_gtm_context.partners. Co-sell framing.
• prospect — default.

The relationship tag appears in the headline before the tier emoji.

For Hot leads at customer or competitor companies: pause before pushing to cold-outbound AE; surface the routing question first.

4. Define the intent relevance set (only if trigger weight > 0)

From get_gtm_context.strategicPriorities and offerings, derive 5–10 themes you sell into. enrich_company_signals returns each employer's active intent topics directly — no topic lookup or pre-query — so these themes are the match set for the intent portion of the trigger axis in step 6: a returned topic counts only if it maps to one.

5. Fetch data per lead (parallel, batched ≤10; chunked for large lists)

• enrich_contacts(personId, fields: jobTitle, managementLevel, department, contactAccuracyScore, hasDirectPhone, hasMobilePhone, hasEmail, directPhone, mobilePhone, email).
• enrich_companies(zoominfoCompanyId, fit-scoring fields).
• enrich_company_signals(zoominfoCompanyIds: [unique employer IDs], signalTypes: ["NEWS", "SCOOP", "INTENT"]) for the employers — only if trigger weight > 0. One batched call (≤10 company IDs) covers news, scoops, and intent; dedupe employers across leads so a shared company is fetched once. No pre-filtering on score, category, topic, or date — applied in step 6.

Batch + context-window discipline. Process in chunks of ~25 leads end-to-end (resolve → fetch → score → compose row → write chunk → discard raw payloads) before moving to the next chunk. Don't accumulate full raw enrichment payloads for hundreds of leads in working context — once per-axis scores + the winning signal/topic strings are captured per lead, drop the rest. For >50-lead lists, summarize completed chunks into running totals (tier distribution, top-Hot list, missing-axes counts) and discard their per-lead breakdowns from context.

6. Score each axis

Person fit (0–100) — compare enrich_contacts to get_gtm_context.buyerPersonas:

Account fit (0–100) — industry 30 · employee band 25 · revenue band 20 · geo 15 · business model 10.

Source signal (0–100):

Trigger / intent (0–100) — same logic as score-accounts (news, scoops, and intent come from enrich_company_signals, filtered to the last 90 days by each signal's date), with seat-fit modifier:

• event_score = signal_type_weight × recency_factor × seat_fit, where signal_type_weight maps from each signal's news category or scoop scoopType.
• Signal weights: 95 (M&A, funding, C-suite hire) · 75 (product launch, hiring surge, earnings) · 55 (partnership, new facility) · 45 (pain-point scoop, other PERSON moves) · 25 (generic press).
• Recency: 0-14d = 1.0 · 14-30d = 0.7 · 30-60d = 0.4 · 60-90d = 0.2 · >90d = 0.
• Seat fit: if event maps to the lead's seat (new CFO → CFO seat; product launch → CRO/CMO seat; hiring surge in dept X → leader of dept X) → 1.0. Otherwise 0.5. Prevents company-level triggers from inflating irrelevant leads.
• Intent: from the enrich_company_signals intent topics that map to the relevance set (step 4) with signalScore ≥ 60 in roughly the last 30 days (use each signal's date) — matching score-accounts — max(signalScore × audienceStrengthFactor). A=1.0 · B=0.85 · C=0.7 · D=0.55 · E=0.4 (from audienceStrength).
• Take max(trigger event, intent). Cap 100.

7. Compute composite + assign tier

composite = round((person × w_p + account × w_a + source × w_s + trigger × w_t) / 100)

Per Hot/Warm/Cold thresholds.

8. Compose the per-lead row

First 30 seconds of read must contain, in order:

1. Relationship tag (if non-default): ⚔️ / 🤝 / 🔗.
2. Tier emoji + label.
3. SLA — tuned to the use case (see Tier + SLA table).
4. Quality flags inline with SLA:
   • ⚠️ verify title (record Xmo old) — when lastUpdatedDate >6mo.
   • 📱 mobile only vs ☎️ direct line.
   • ⚠️ acc <85 — low contact-accuracy.
5. "Why now" reasoning snippet — one line, anchored on the strongest specific signal:
   • Strong person + source + trigger → "VP-Sales seat × demo request 3h ago × Series B closed 8d ago."
   • High-source-only → "Pricing inquiry from VP at perfect-ICP company; no fresh trigger."
   • Trigger-anchored → "Fresh CFO appointment 5d ago × CFO-seat lead — trigger × seat = direct match."
   • Intent-driven → "Spiked on '[topic]' (score 92, audience A) over 14d."
   • Engagement-driven (from account_research) → "Open opp at this account; named champion engaged 6d ago."
   • Low signal across axes → "Low signal — monitor only." Never restate the composite. Never use generic phrasing ("strong fit and engagement") — that applies to every Hot lead and tells the rep nothing.
6. Recommended next action — concrete, with phone number / channel.
7. Contact data line — email · phone · accuracy.

Example (stale-but-high-accuracy Hot lead, mobile only, inbound_routing):

🤝 🔥 Hot · Call within 5 min ⚠️ verify title (record 11mo old) · 📱 mobile only · acc 95[First Last] · [Title] · [Company]Why now: [Trigger event] X days ago × [seat] = direct match. (Source: [demo_request].)Recommended: Direct dial 555-XXXX (mobile, verify title before dialing). Lead with [angle].

Component breakdown shown BELOW THE FOLD.

9. Self-check before output

• ☑ Tier + SLA (use-case-appropriate) visible in the first row of every output.
• ☑ Hot leads have verified phone + accuracy ≥85, or flag fires.
• ☑ "Why now" snippet on every lead — specific axis driver, never composite restated, never generic.
• ☑ Recommended next action is concrete with channel + signal.
• ☑ Component breakdown below the fold.
• ☑ Every input bucketed (auto-resolved / verified / ambiguous / failed) — none silently dropped.
• ☑ Failed emails NOT silently routed to name search.
• ☑ Source missing → flagged in caveats, not silently defaulted.
• ☑ Each row readable in <30s.
• ☑ Batch chunked when N > 25; intermediate payloads dropped from context.
• ☑ Iteration options offered.

10. Present + offer iteration

1. Accept — chain to personalize-email per Hot.
2. Adjust weights.
3. Tighten / loosen thresholds.
4. Refilter — show only Hot, exclude seats.
5. Drill into a lead.
6. Add leads.
7. Backfill source for unknowns.

Re-execute step 5 only when lead list changes; otherwise recompute from cached axis scores.

Anti-patterns — fail-fast checklist

1. Long preamble before the tier label.
2. Silent email→name fallback on typo'd email.
3. Source defaulted without flagging.
4. Generic "why now" — "strong fit and engagement" applies to every Hot lead. Each row must cite the specific driver.
5. Composite-as-rationale. Re-stating the score number instead of the axis driver.
6. Tier without SLA.
7. Hot lead with low-accuracy unflagged.
8. Component breakdown above the fold.
9. No drill-down to personalize-email for Hot.
10. Auto-accepting ambiguous matches (e.g., 8 same-named contacts at a large enterprise).
11. Forcing the live-triage SLA onto a non-live-triage use case. Event follow-up, PQL triage, and content nurture motions have their own SLA bands; using the inbound-routing 5-min framing on them burns rep capacity on the wrong leads.

Fallback rules

• get_gtm_context empty → use user-supplied personas/ICP if any; flag.
• Source missing → ask once; else 50 with flag.
• Email no match → failed; do NOT fall back to name search. If domain edit-distance ≤2 from a known-company domain (from GTM context or batch's resolved set), suggest the closest (e.g., [email protected] → "did you mean [email protected]?").
• Name + company multi-match → verified with note OR ambiguous.
• enrich_company_signals returns no relevant intent and no news/scoops for the employer → trigger = 0; don't pad (absence of trigger is a real score, not a weight-redistribution case).
• Contact accuracy <75 → flag on the row; recommend verification before dialing.

Never block tiering on a single missing axis. Never invent contact data or source.

Output Format

TL;DR — Lead Scoring · N leads · Pass [M]

Use case: [restate]. SLA band: [restate]. Weights · Thresholds Hot≥[X] · Warm[Y–Z].

Tier distribution: 🔥 Hot: X · 🌤 Warm: Y · ❄️ Cold: Z · ❌ Unresolved: W.

🔥 Hot leads (SLA per use case):

🔥 Jordan Smith · VP Sales at Acme Corp · [SLA] · Why now: demo request × VP-Sales seat × fresh CEO hire 8d ago · 📞 555-123-4567 · ✉ [email protected] · acc 98 🔥 [next Hot lead...]

Hot listed first.

Resolution Summary

Status legend: ✅ Auto-resolved · 🔍 Verified · ⚠️ Ambiguous · ❌ Failed.

Ranked Lead List

Hot first → Warm → Cold. Each row <30s read.

Component Breakdown (below the fold)

Weights & Axes Used

person:  [%]account: [%]source:  [%]trigger: [%]

Recommended Actions per Tier

SLAs adapt to the use case (see Tier + SLA table).

• 🔥 Hot — SLA per use case. Direct dial / personal outreach. Chain to personalize-email. Verify phone if acc <85.
• 🌤 Warm. SDR sequence; multi-touch cadence sized to use case.
• ❄️ Cold. Nurture; content drip; do not call.

Iteration Options

1. Accept → chain to personalize-email.
2. Adjust weights.
3. Tighten thresholds.
4. Backfill source for unknowns.
5. Drill into a lead.
6. Add leads.

Caveats (when relevant)

• Source missing on N leads — defaulted to 50; backfill for precision.
• Failed resolutions — N unresolved; review.
• Low contact accuracy on Hot leads — N have acc <85; verify phone before dialing.
• Signal depth — enrich_company_signals returns the most recent signals per type, so the trigger axis reflects the most recent window for very active employers.
• GTM-context gap — personas sparse; person-fit reduced.
• Stale records — N leads' records >12mo old; current title may have changed.

Final Filter + Weight Set (on accept)

{  "weights": {"person": 35, "account": 25, "source": 25, "trigger": 15},  "tier_thresholds": {"Hot": 75, "Warm": 50},  "buyer_personas": [...],  "intent_topics": ["..."],  "use_case": "inbound_routing",  "_meta": {"lead_count": ..., "tier_distribution": {...}, "axes_missing": [...], "pass_count": ...}}

Chain Targets

• personalize-email per Hot lead → drafts grounded in the same axis driver that tiered the lead.
• score-accounts on the leads' companies → company-level prioritization alignment.
• find-similar on a Hot lead → lookalike prospects at same / similar companies.