gtm-skills

Account Fit Rank

Score and tier a list of accounts on four axes (fit, intent, trigger, workforce) using firmographics, technographics, intent topics, events, and workforce trends, then apply a transparent weighted composite the calling model computes from the returned data.

Input

• Accounts (required): list of business IDs, company names, domains, or a mixed CSV.
• Use case (default prospecting): one of prospecting, abm, territory_planning, pipeline_acceleration. Shifts tier thresholds and recommended actions.
• ICP definition (required): industries, employee buckets, revenue buckets, country or region, optional tech-stack vendors, optional intent topics. Capture inline; no persisted ICP exists.
• Weight overrides (optional): {fit, intent, trigger, workforce} summing to 100. Default 45 / 25 / 25 / 5.
• Tier thresholds (optional): {A, B}. C is the remainder. Default A>=75, B 50-74.

Workflow

1. Lock the ICP and intent topics. Restate the ICP from the user. Discover canonical values for every free-text dimension (industry, technology, intent topic, city). Resolve intent topics one term at a time: fuzzy multi-term queries fail silently. If a tag does not resolve, drop it and flag that axis as configuration-gap, not signal-absent.

2. Resolve identifiers. Route inputs by shape: existing business IDs pass through; domains and names resolve via business match, with an optional country tiebreaker for names. Never silently pick a winner: surface top candidates for ambiguous rows and ask for confirmation. For high-collision names, require domain confirmation before scoring. Sanity-check the resolved firmographics: if a major-brand input returns 1-50 employees and Corporate-Managing-Offices category, the match likely routed to a shell entity. Retry with the alternate domain or the name string. Every input ends as auto-resolved, verified, ambiguous, or failed.

3. Pre-flight relationship context. Tag each resolved account against any user-supplied competitor / customer / partner lists before scoring so a “pursue this competitor” line is never produced silently.

4. Fetch firmographic, technographic, and signal data in small chunks end-to-end (resolve, enrich, score, write row, discard raw payloads). Per chunk: enrich with firmographics, technographics, recent LinkedIn posts, funding and acquisitions, workforce trends, strategic insights, and website changes; then fetch business events scoped to the last 90 days for funding rounds, leadership changes, product launches, and expansions. If the ICP includes intent, size intent-topic exposure separately; if no topics resolved in step 1, set intent weight to zero and redistribute. Drop raw payloads after extracting the per-axis inputs and the single winning signal for “why now”.

5. Score each axis (calling model computes from the fetched data):
   • Fit (0-100): banded firmographic match. Industry primary = 25, adjacent = 10; employee bucket in band = 20, one off = 12, two off = 4; revenue bucket same banding, max 20; geography country = 15, region = 8; company age in window = 10; vendor match if specified = 10.
   • Intent (0-100): 90 for 3+ resolved topics active, 70 for 2, 50 for 1, 0 for none. Record the strongest topic. If no topics resolved, axis = 0 with configuration-gap and the weight redistributes.
   • Trigger (0-100): event_score = type_weight * recency_factor. Type weights: M&A / funding / new CEO = 95; product launch, hiring surge, major website change = 75; partnership, new facility = 55; generic announcement = 25. Recency: 0-14d = 1.0, 14-30d = 0.7, 30-60d = 0.4, 60-90d = 0.2, older = 0. Account trigger = max event_score, capped at 100. Verify the event headline actually mentions the target: industry-wide articles can cross-attribute.
   • Workforce (0-100): headcount up 10%+ in 90d or target-department hiring surge = 80-100; modest growth = 40-70; flat or shrinking = 10-30; no data = null and the weight redistributes.

6. Composite, tier, and “why now”. Composite = round(weighted sum / 100). Cap any axis with no data at null and redistribute proportionally; surface the redistribution. Assign tier from thresholds (use-case overrides: abm A=80 / B=55, pipeline_acceleration A=65 / B=40). “Why now” is one sentence anchored on the strongest underlying signal, never the composite restated. For strong trigger with low fit, be explicit (“Do not pursue: fresh CEO change but the revenue bucket mismatch keeps this in C.”).

7. Iterate. Offer: adjust weights and recompute from cached axes; tighten thresholds; drop tier C; swap the ICP; drill into one account with deeper enrichment (challenges, competitive landscape, ratings); add accounts and rescore. Only “add accounts” or “swap ICP” require new calls.

Output Format

TL;DR

Account Fit Rank, N accounts. Use case, weights, thresholds. Resolution counts (resolved / ambiguous / failed; flag if confirmation required). Tier distribution. Top 3 accounts each with a one-line “why now”.

Resolution Summary

Table: Input, Resolved To, Business ID, Confidence, Status (auto-resolved, verified, ambiguous, failed). For each ambiguous row, list candidates with industry, headcount, revenue bucket, country and ask the user to pick.

Ranked Accounts

Sorted by composite descending. Use - in any axis column that was redistributed. Columns: #, Account, Tag, Tier, Composite, Fit, Intent, Trigger, Workforce, Why now, Business ID.

Weights and Axes Used

List percentages applied and any axis redistributed because data was unavailable.

Recommended Actions per Tier

Tier A: route to AE for 1:1 outreach within 24h, prioritize contact enrichment. Tier B: SDR sequence using the why-now as opener, retarget for ABM. Tier C: monitor, rescore weekly when fresh events land.

Iteration Options

Adjust weights, tighten thresholds, drop tier C, swap the ICP, drill into one account with deeper enrichment, or add accounts and rescore.

Caveats (when relevant)

Ambiguous-pending count, failed resolutions, intent configuration-gap, stale trigger cliff (60-90d), workforce nulls with weight redistribution.

Limitations

• Business match returns no confidence score; infer ambiguity from candidate-set shape and confirm with the user.
• Strategic-insights and challenges signals come from public filings: null for private companies and 12-18 months stale for public ones. Use events, funding, workforce, and LinkedIn posts for current state.
• No native scoring engine. The composite and tiering are computed by the calling model from the data returned.
• Headcount and revenue are bucketed; band-distance scoring is the right resolution.
• No CRM-engagement axis (deal stage, last activity, named champion); workforce is the substitute, and the gap is surfaced rather than invented.
• Industry taxonomies are mutually exclusive on filters; pick one per run.
• No native similar-companies tool, no metro taxonomy, no Inc / Fortune ranking. Geography is country or region only.
• Country-scoped sizing does not strictly enforce the country filter; read the per-country breakdown rather than the global total.

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