Account Fit Rank

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.