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**Duration:** 15–30 minutes per account (or run in batch) **Output:** ICP score + tier assignment saved to CRM or `outputs/scoring/`
--- name: skill-icp-scoring description: "**Duration:** 15\u201330 minutes per account (or run in batch) **Output:** ICP score + tier assignment saved to CRM or `outputs/scoring/`" --- # Skill: ICP Scoring **Duration:** 15–30 minutes per account (or run in batch) **Output:** ICP score + tier assignment saved to CRM or `outputs/scoring/` --- ## Quick Start Single account: ``` Read skills/icp-scoring/SKILL.md and score [company.com] against our ICP ``` Batch: ``` Read skills/icp-scoring/SKILL.md and context/icp-definition.md. Score these companies and output a table sorted by score, Tier 1 flagged: [paste list of company names or domains] ``` --- ## Purpose Score any account against your ICP and assign it to the right tier. Replaces gut feel with a repeatable model. When run at scale, it tells you which accounts to prioritize this week, which to monitor, and which to skip entirely. --- ## When to Run This Skill - New accounts entering the pipeline — score before assigning to a rep - Enrichment run complete — re-score the full account list - ICP definition updated — re-score to find newly qualified accounts - Preparing a campaign list — score to determine sequence tier - Quarterly pipeline review — re-score all open opportunities ## Re-scoring Cadence Scores go stale. Set a recurring schedule: | Segment | Frequency | Why | |---------|-----------|-----| | Full account list | Quarterly | ICP drift, new signal data | | Tier 1 accounts | Monthly | High-value; worth tracking closely | | Active pipeline | After each campaign | Campaign results reveal scoring gaps | | After ICP change | Immediately | Find newly qualified or disqualified accounts | After a quarterly re-score, pull the delta: which accounts moved tiers? Accounts that dropped from Tier 1 to Tier 2 need to be removed from AE pipelines. Accounts that moved up need to be activated. --- ## Inputs - Account name, domain, and available firmographic/technographic data - `context/icp-definition.md` — scoring criteria and tier definitions - `context/signal-library.md` — signal scores to add on top of ICP fit --- ## Scoring Model ### Part 1: ICP Fit Score (0–70 points) Measure how well the account matches your ideal customer profile. #### Firmographic Fit (0–30 points) | Criterion | Points | How to assess | |-----------|--------|---------------| | Employee count in range | 0–10 | [Your range from ICP definition] | | Industry match | 0–10 | Primary = 10, Secondary = 5, Other = 0 | | Funding stage match | 0–10 | Ideal stage = 10, Adjacent = 5, Outside = 0 | #### Technographic Fit (0–20 points) | Criterion | Points | How to assess | |-----------|--------|---------------| | Uses [key integration tool] | 0–10 | Confirms workflow match | | Uses [secondary tool] | 0–5 | Confirms sophistication level | | No [disqualifying tool] | 0–5 | Absence of competitive blocker | #### Organizational Fit (0–20 points) | Criterion | Points | How to assess | |-----------|--------|---------------| | Has [key role/function] | 0–10 | Confirms decision-maker exists | | [Role] hired in last 12 months | 0–5 | New leader = change appetite | | Hiring for [relevant role] | 0–5 | Active investment in function | --- ### Part 2: Signal Score (0–30 points) Add points for active signals from `context/signal-library.md`. Reference the point values defined there. **Note:** Signal scores decay over time. Apply the decay multipliers from `context/signal-library.md` — a signal at 91–180 days is worth 25% of its original value; at 180+ days it expires entirely. --- ### Total Score Interpretation | Total | Tier | Action | |-------|------|--------| | 80–100 | Tier 1 | Immediate outreach, full research (run Account Research skill), assign to AE | | 60–79 | Tier 2 | Signal-triggered sequence within 48 hours | | 40–59 | Tier 3 | Add to automated sequence | | 20–39 | Tier 4 | Monitor, re-score in 90 days | | 0–19 | Exclude | Remove from active list | --- ## Running at Scale (Batch Scoring) When scoring a large list (50+ accounts), structure the output as a table: ``` | Account | Domain | Firmographic | Technographic | Org | Signal | Total | Tier | Action | ``` Instruct Claude: ``` Read skills/icp-scoring/SKILL.md and context/icp-definition.md. Score the accounts in [file or pasted list]. Output a scored table sorted by total score descending. Flag any accounts scoring 80+ for immediate follow-up. ``` --- ## Scoring Output Format ```markdown # ICP Score: [Company Name] Date scored: [YYYY-MM-DD] Scored by: [Claude / Name] ## Score Breakdown | Category | Score | Max | Notes | |----------|-------|-----|-------| | Firmographic fit | X | 30 | [Key observations] | | Technographic fit | X | 20 | [Key observations] | | Organizational fit | X | 20 | [Key observations] | | Active signals | X | 30 | [Signals present] | | **Total** | **X** | **100** | | ## Tier Assignment: [Tier 1 / 2 / 3 / 4 / Exclude] ## What Qualifies Them - [Specific reason 1] - [Specific reason 2] ## What Disqualifies or Reduces Score - [Gap 1 — what would need to change for this to be a higher tier] ## Recommended Next Action [Specific: which skill to run next, which sequence to assign, or what to monitor] ## Re-score Trigger [Condition that should trigger re-scoring — e.g., "If they raise a new round" or "If they hire a VP of Ops"] ``` --- ## Calibration Guide Scoring models drift. Thresholds that made sense when you had 20 deals may not hold after 100. Run a calibration review quarterly, or immediately if scored tiers consistently don't match outcomes. ### When to calibrate - Quarterly (minimum) - After any ICP definition change - When win rate by tier diverges from expectations - When the team reports that "Tier 1 accounts don't feel like Tier 1" ### Step 1: Pull scored accounts from the last 90 days Export all accounts scored in the last 90 days with: - Score at time of scoring - Tier assigned - Actual outcome: meeting booked (yes/no), opportunity created (yes/no), closed-won (yes/no), closed-lost reason (if applicable) If you use CRM tracking, pull from there. If scores live in `outputs/scoring/`, compile manually. ### Step 2: Compare predicted tier to actual outcome Build a conversion table: | Tier | Accounts scored | Meetings booked | Meeting rate | Opps created | Opp rate | Closed-won | Win rate | |------|----------------|-----------------|-------------|-------------|----------|------------|----------| | Tier 1 (80-100) | | | | | | | | | Tier 2 (60-79) | | | | | | | | | Tier 3 (40-59) | | | | | | | | | Tier 4 (20-39) | | | | | | | | ### Step 3: Check for calibration problems **What good calibration looks like:** - Tier 1 accounts convert to meetings at 2x+ the rate of Tier 2 - Tier 2 converts at 2x+ the rate of Tier 3 - Each tier step down shows a meaningful drop in conversion - Fewer than 10% of closed-won deals came from Tier 3 or below **Common problems and fixes:** | Problem | What it means | Fix | |---------|--------------|-----| | Tier 1 and Tier 2 convert at similar rates | Threshold too low: too many accounts in Tier 1 | Raise the Tier 1 threshold (e.g., 80 to 85) or tighten firmographic criteria | | Tier 3 accounts converting better than Tier 2 | A signal or criterion is underweighted | Check which Tier 3 accounts converted. What did they have in common? Add that as a scoring factor | | Most closed-won deals scored below 60 | The model is missing something | Interview the AEs: what did those accounts have that the model didn't capture? Add it | | Tier 1 has high meeting rate but low close rate | Scoring predicts interest but not fit | Add disqualification criteria or weight technographic fit higher | ### Step 4: Adjust and re-score After identifying calibration gaps: 1. Adjust point values in the scoring model above 2. Document the change in the calibration log below (what changed and why) 3. Re-score the full active account list to find newly qualified or disqualified accounts 4. Update tier assignments in CRM ### Calibration Log | Date | Change | Reason | Impact | |------|--------|--------|--------| | | | | | *Example: "2024-06-15 | Raised Tier 1 threshold from 80 to 85 | Tier 1 and Tier 2 meeting rates were within 1% of each other | 22 accounts moved from Tier 1 to Tier 2, freed up AE capacity"*--- name: skill-icp-scoring description: "**Duration:** 15\u201330 minutes per account (or run in batch) **Output:** ICP score + tier assignment saved to CRM or `outputs/scoring/`" --- # Skill: ICP Scoring **Duration:** 15–30 minutes per account (or run in batch) **Output:** ICP score + tier assignment saved to CRM or `outputs/scoring/` --- ## Quick Start Single account: ``` Read skills/icp-scoring/SKILL.md and score [company.com] against our ICP ``` Batch: ``` Read skills/icp-scoring/SKILL.md and context/icp-definition.md. Score these companies and output a table sorted by score, Tier 1 flagged: [paste list of company names or domains] ``` --- ## Purpose Score any account against your ICP and assign it to the right tier. Replaces gut feel with a repeatable model. When run at scale, it tells you which accounts to prioritize this week, which to monitor, and which to skip entirely. --- ## When to Run This Skill - New accounts entering the pipeline — score before assigning to a rep - Enrichment run complete — re-score the full account list - ICP definition updated — re-score to find newly qualified accounts - Preparing a campaign list — score to determine sequence tier - Quarterly pipeline review — re-score all open opportunities ## Re-scoring Cadence Scores go stale. Set a recurring schedule: | Segment | Frequency | Why | |---------|-----------|-----| | Full account list | Quarterly | ICP drift, new signal data | | Tier 1 accounts | Monthly | High-value; worth tracking closely | | Active pipeline | After each campaign | Campaign results reveal scoring gaps | | After ICP change | Immediately | Find newly qualified or disqualified accounts | After a quarterly re-score, pull the delta: which accounts moved tiers? Accounts that dropped from Tier 1 to Tier 2 need to be removed from AE pipelines. Accounts that moved up need to be activated. --- ## Inputs - Account name, domain, and available firmographic/technographic data - `context/icp-definition.md` — scoring criteria and tier definitions - `context/signal-library.md` — signal scores to add on top of ICP fit --- ## Scoring Model ### Part 1: ICP Fit Score (0–70 points) Measure how well the account matches your ideal customer profile. #### Firmographic Fit (0–30 points) | Criterion | Points | How to assess | |-----------|--------|---------------| | Employee count in range | 0–10 | [Your range from ICP definition] | | Industry match | 0–10 | Primary = 10, Secondary = 5, Other = 0 | | Funding stage match | 0–10 | Ideal stage = 10, Adjacent = 5, Outside = 0 | #### Technographic Fit (0–20 points) | Criterion | Points | How to assess | |-----------|--------|---------------| | Uses [key integration tool] | 0–10 | Confirms workflow match | | Uses [secondary tool] | 0–5 | Confirms sophistication level | | No [disqualifying tool] | 0–5 | Absence of competitive blocker | #### Organizational Fit (0–20 points) | Criterion | Points | How to assess | |-----------|--------|---------------| | Has [key role/function] | 0–10 | Confirms decision-maker exists | | [Role] hired in last 12 months | 0–5 | New leader = change appetite | | Hiring for [relevant role] | 0–5 | Active investment in function | --- ### Part 2: Signal Score (0–30 points) Add points for active signals from `context/signal-library.md`. Reference the point values defined there. **Note:** Signal scores decay over time. Apply the decay multipliers from `context/signal-library.md` — a signal at 91–180 days is worth 25% of its original value; at 180+ days it expires entirely. --- ### Total Score Interpretation | Total | Tier | Action | |-------|------|--------| | 80–100 | Tier 1 | Immediate outreach, full research (run Account Research skill), assign to AE | | 60–79 | Tier 2 | Signal-triggered sequence within 48 hours | | 40–59 | Tier 3 | Add to automated sequence | | 20–39 | Tier 4 | Monitor, re-score in 90 days | | 0–19 | Exclude | Remove from active list | --- ## Running at Scale (Batch Scoring) When scoring a large list (50+ accounts), structure the output as a table: ``` | Account | Domain | Firmographic | Technographic | Org | Signal | Total | Tier | Action | ``` Instruct Claude: ``` Read skills/icp-scoring/SKILL.md and context/icp-definition.md. Score the accounts in [file or pasted list]. Output a scored table sorted by total score descending. Flag any accounts scoring 80+ for immediate follow-up. ``` --- ## Scoring Output Format ```markdown # ICP Score: [Company Name] Date scored: [YYYY-MM-DD] Scored by: [Claude / Name] ## Score Breakdown | Category | Score | Max | Notes | |----------|-------|-----|-------| | Firmographic fit | X | 30 | [Key observations] | | Technographic fit | X | 20 | [Key observations] | | Organizational fit | X | 20 | [Key observations] | | Active signals | X | 30 | [Signals present] | | **Total** | **X** | **100** | | ## Tier Assignment: [Tier 1 / 2 / 3 / 4 / Exclude] ## What Qualifies Them - [Specific reason 1] - [Specific reason 2] ## What Disqualifies or Reduces Score - [Gap 1 — what would need to change for this to be a higher tier] ## Recommended Next Action [Specific: which skill to run next, which sequence to assign, or what to monitor] ## Re-score Trigger [Condition that should trigger re-scoring — e.g., "If they raise a new round" or "If they hire a VP of Ops"] ``` --- ## Calibration Guide Scoring models drift. Thresholds that made sense when you had 20 deals may not hold after 100. Run a calibration review quarterly, or immediately if scored tiers consistently don't match outcomes. ### When to calibrate - Quarterly (minimum) - After any ICP definition change - When win rate by tier diverges from expectations - When the team reports that "Tier 1 accounts don't feel like Tier 1" ### Step 1: Pull scored accounts from the last 90 days Export all accounts scored in the last 90 days with: - Score at time of scoring - Tier assigned - Actual outcome: meeting booked (yes/no), opportunity created (yes/no), closed-won (yes/no), closed-lost reason (if applicable) If you use CRM tracking, pull from there. If scores live in `outputs/scoring/`, compile manually. ### Step 2: Compare predicted tier to actual outcome Build a conversion table: | Tier | Accounts scored | Meetings booked | Meeting rate | Opps created | Opp rate | Closed-won | Win rate | |------|----------------|-----------------|-------------|-------------|----------|------------|----------| | Tier 1 (80-100) | | | | | | | | | Tier 2 (60-79) | | | | | | | | | Tier 3 (40-59) | | | | | | | | | Tier 4 (20-39) | | | | | | | | ### Step 3: Check for calibration problems **What good calibration looks like:** - Tier 1 accounts convert to meetings at 2x+ the rate of Tier 2 - Tier 2 converts at 2x+ the rate of Tier 3 - Each tier step down shows a meaningful drop in conversion - Fewer than 10% of closed-won deals came from Tier 3 or below **Common problems and fixes:** | Problem | What it means | Fix | |---------|--------------|-----| | Tier 1 and Tier 2 convert at similar rates | Threshold too low: too many accounts in Tier 1 | Raise the Tier 1 threshold (e.g., 80 to 85) or tighten firmographic criteria | | Tier 3 accounts converting better than Tier 2 | A signal or criterion is underweighted | Check which Tier 3 accounts converted. What did they have in common? Add that as a scoring factor | | Most closed-won deals scored below 60 | The model is missing something | Interview the AEs: what did those accounts have that the model didn't capture? Add it | | Tier 1 has high meeting rate but low close rate | Scoring predicts interest but not fit | Add disqualification criteria or weight technographic fit higher | ### Step 4: Adjust and re-score After identifying calibration gaps: 1. Adjust point values in the scoring model above 2. Document the change in the calibration log below (what changed and why) 3. Re-score the full active account list to find newly qualified or disqualified accounts 4. Update tier assignments in CRM ### Calibration Log | Date | Change | Reason | Impact | |------|--------|--------|--------| | | | | | *Example: "2024-06-15 | Raised Tier 1 threshold from 80 to 85 | Tier 1 and Tier 2 meeting rates were within 1% of each other | 22 accounts moved from Tier 1 to Tier 2, freed up AE capacity"*
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