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F-2 · GTM Foundation · 100 XP · ~18 min
Most ICP documents are wrong. Not because the team didn’t think hard enough — but because they defined the ICP based on who they want to sell to, not who actually buys. This module teaches you the difference, and how to translate a real ICP into targeting criteria you can automate.

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Why Most ICPs Are Wrong

The classic ICP document lists things like: “B2B SaaS companies, 50–500 employees, Series A–C, US-based.” That describes half the companies on LinkedIn. It’s not a targeting definition — it’s a demographic sketch. A real ICP answers a different question: what combination of attributes predicts that this company will buy from us, get value, and stay? The answer always comes from your existing customers. Not from a whiteboard.

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Building an ICP from Existing Customer Data

Run this analysis on your best customers — define “best” as highest LTV, fastest time-to-value, or fewest support tickets:
AttributeWhere to Find ItWhat to Look For
Company sizeLinkedIn, ClearbitIs there a range that clusters?
Industry / verticalCRM, LinkedInWhich verticals appear most?
Funding stageCrunchbase, ApolloBootstrap vs VC-backed pattern?
Tech stackBuiltWith, ClearbitWhat tools do they all use?
Team structureLinkedIn job postingsSDR team? Marketing team size?
Growth signalsLinkedIn headcount trendWere they hiring when they bought?
Trigger eventCRM notes, deal historyWhat made them look for a solution now?
When you map 20–30 of your best customers against these columns, patterns emerge. Those patterns are your real ICP.

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The ICP Scoring Matrix

Once you’ve identified patterns, translate them into a scoring rubric: 
ICP Fit Score (0–10):

Firmographic fit:      0–3 points
  - Industry match:    +1
  - Size in range:     +1
  - Funding stage:     +1

Technographic fit:     0–3 points
  - Uses CRM:          +1
  - Has SEP (e.g. Outreach/Instantly): +1
  - Has data tool (Apollo/ZoomInfo):   +1

Signal fit:            0–4 points
  - Actively hiring SDRs: +2
  - Recent funding:       +1
  - Headcount growth >20%: +1

Score 8–10: Hot ICP — top priority
Score 5–7:  Warm ICP — work the signal
Score 0–4:  Outside ICP — deprioritize
This matrix becomes the backbone of your enrichment and scoring columns in Bitscale.

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Negative ICP: Who to Exclude

Equally important: who you should not spend time on. Common negative ICP signals:
  • Fewer than 10 employees (no budget, no infrastructure)
  • No sales team at all (wrong use case)
  • Competitor’s customer (already solved the problem)
  • Wrong geography (if you have regional constraints)
  • Stage too early or too late (pre-product or enterprise with 18-month sales cycle)
Build your negative ICP list explicitly. In Bitscale, you can filter these out before they ever reach your outbound queue.

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Encoding ICP as Bitscale Criteria

Once you have your ICP defined, it lives in your Bitscale grids as column filters and scoring formulas:
  • Industry column → filter to target verticals
  • Employee count column → filter to size range
  • Tech stack columns → flag CRM/SEP presence
  • Hiring signal column → detect SDR hiring
  • ICP score formula column → sum the above
You’ll build this in F-3 (Your First Grid). For now, make sure your ICP is specific enough to encode — if you can’t write it as a filter, it’s not specific enough.
Quick Check: Can you name three firmographic and two technographic criteria that predict a customer buys from you? If you can’t yet, that’s what the F-2 challenge will help you figure out.

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F-2 Challenge: Build Your ICP Scoring Matrix (+100 XP)

Take your 10 best current customers. Map them against the attributes above. Identify the top 3 patterns that appear across 7+ of them. Write up your ICP scoring matrix.

Submit F-2 Challenge →

Submit your ICP scoring matrix based on real customer data. +100 XP on approval.

Next: F-3 Your First Grid →

Turn your ICP definition into a live Bitscale grid that finds and scores prospects automatically.