/octave:icp-refine – ICP Intelligence

Analyze deal outcomes, conversation patterns, and qualification scores to refine your ICP definitions. Compares what your library says your ideal customer looks like against what actually wins — then recommends updates.

Usage

/octave:icp-refine [--period <days>] [--segment <name>] [--focus wins|losses|both]

Examples

/octave:icp-refine                                        # Full ICP analysis (last 180 days)
/octave:icp-refine --period 90                            # Last quarter
/octave:icp-refine --segment "Enterprise"                 # Specific segment
/octave:icp-refine --focus wins                           # Only analyze what's working
/octave:icp-refine --focus losses                         # Only analyze what's not working

Instructions

When the user runs /octave:icp-refine:

Step 1: Set Parameters

If no options specified, use defaults and confirm:

I'll analyze your deal data to refine your ICP.

Period: Last 180 days (change with --period)
Segments: All (change with --segment)
Focus: Wins and losses

Starting analysis...

Step 2: Gather Current ICP Definition

# Get current segments (this IS the ICP definition)
list_all_entities({ entityType: "segment" })

# Get full segment details
get_entity({ oId: "<segment_oId>" })  // for each segment

# Get current personas
list_all_entities({ entityType: "persona" })
get_entity({ oId: "<persona_oId>" })  // for key personas

# Get products/services (what we're selling)
list_all_entities({ entityType: "product" })
list_all_entities({ entityType: "service" })

Step 3: Analyze Deal Outcomes

# Get won deals
list_events({
  startDate: "<period start>",
  filters: {
    eventTypes: ["DEAL_WON"]
  }
})

# Get lost deals
list_events({
  startDate: "<period start>",
  filters: {
    eventTypes: ["DEAL_LOST"]
  }
})

# Get findings from won deals
list_findings({
  query: "why we won success factors decision criteria champion",
  startDate: "<period start>",
  eventFilters: {
    outcomeFilters: ["WON"]
  }
})

# Get findings from lost deals
list_findings({
  query: "why we lost objections blockers competition pricing",
  startDate: "<period start>",
  eventFilters: {
    outcomeFilters: ["LOST"]
  }
})

# Get positive conversation signals
list_findings({
  query: "excited interested positive resonated value",
  startDate: "<period start>",
  eventFilters: {
    sentiments: ["POSITIVE"]
  }
})

# Get negative signals
list_findings({
  query: "concerned hesitant not a fit wrong timing",
  startDate: "<period start>",
  eventFilters: {
    sentiments: ["NEGATIVE"]
  }
})

Step 4: Analyze Patterns

For each won deal, extract:

• Company profile (industry, size, stage, tech stack)
• Persona(s) involved
• Pain points that resonated
• Value props that closed the deal
• Deal cycle length
• Deal size
• Competitors in the deal

For each lost deal, extract:

• Same attributes
• Why it was lost (competitor, timing, budget, fit, champion)

Step 5: Generate ICP Refinement Report

ICP REFINEMENT REPORT
======================

Period: [Start] to [End]
Deals Analyzed: [N] won, [N] lost
Win Rate: [X%]

===================================

CURRENT ICP DEFINITION (from library)
-------------------------------------

Segment: [Segment Name]
  Industry: [Defined industries]
  Company Size: [Defined range]
  Stage: [Defined stage]
  Characteristics: [Key attributes]

Personas: [List current personas with titles]

---

WHAT THE DATA SHOWS
--------------------

WINNING CUSTOMER PROFILE
-------------------------
Based on [N] won deals:

Industry:
  [Industry 1]: [N] wins ([X%]) ← [matches/exceeds/below ICP definition]
  [Industry 2]: [N] wins ([X%])
  [Industry 3]: [N] wins ([X%])
  Surprise: [Any industry winning that's not in current ICP]

Company Size:
  [Range 1]: [N] wins ([X%])
  [Range 2]: [N] wins ([X%])
  Sweet Spot: [Most common size range in wins]
  Current ICP says: [What's defined] ← [Match/Mismatch]

Deal Size:
  Average: [$X]
  Median: [$X]
  Range: [$X - $Y]

Cycle Length:
  Average: [X days]
  Fastest: [X days] (what made it fast)
  Slowest: [X days] (what slowed it down)

Common Characteristics of Wins:
✓ [Pattern 1 — e.g., "Had a technical champion"]
✓ [Pattern 2 — e.g., "Were actively replacing a competitor"]
✓ [Pattern 3 — e.g., "Had budget approved before evaluation"]
✓ [Pattern 4 — e.g., "Multiple stakeholders engaged early"]

---

LOSING PROFILE (anti-ICP)
---------------------------
Based on [N] lost deals:

Common Characteristics of Losses:
✗ [Pattern 1 — e.g., "No clear champion"]
✗ [Pattern 2 — e.g., "Evaluated on price alone"]
✗ [Pattern 3 — e.g., "Single-threaded with junior evaluator"]
✗ [Pattern 4 — e.g., "No defined timeline or budget"]

Lost to Competitors:
  [Competitor 1]: [N] losses — Common reason: [reason]
  [Competitor 2]: [N] losses — Common reason: [reason]

Lost to Status Quo: [N] — Why: [common reason]
Lost to Budget/Timing: [N] — Why: [common reason]

---

PERSONA EFFECTIVENESS
---------------------

| Persona | Deals Involved | Win Rate | Avg Deal Size | Notes |
|---------|---------------|----------|-------------|-------|
| [Persona 1] | [N] | [X%] | [$X] | [Observation] |
| [Persona 2] | [N] | [X%] | [$X] | [Observation] |
| [Persona 3] | [N] | [X%] | [$X] | [Observation] |

Observations:
• [Persona X] has highest win rate — consider prioritizing
• [Persona Y] has low win rate — investigate why
• [Missing persona] appeared in [N] deals but isn't defined in library

---

VALUE PROP EFFECTIVENESS
-------------------------

What's resonating (from won deals):
1. "[Value prop]" — Mentioned in [N] wins, [X%] positive reactions
2. "[Value prop]" — Mentioned in [N] wins
3. "[Value prop]" — Mentioned in [N] wins

What's not landing (from lost deals):
1. "[Value prop]" — Failed to resonate in [N] losses
   Why: [Insight from conversation data]
2. "[Value prop]" — [Analysis]

---

GAPS: DEFINED ICP vs. REALITY
-------------------------------

UNDERWEIGHTED (winning but not in ICP):
⚠ [Industry/size/characteristic] — [N] wins but not defined as target
  Recommendation: [Add to ICP / investigate further]

OVERWEIGHTED (in ICP but not winning):
⚠ [Industry/size/characteristic] — [N] losses, only [N] wins
  Recommendation: [Narrow ICP / adjust approach / investigate]

MISSING SIGNALS (new qualification criteria):
⚠ [Signal] — Appears in [X%] of wins, absent in [X%] of losses
  Recommendation: Add as qualification criterion

DISQUALIFICATION SIGNALS (new anti-patterns):
🚫 [Signal] — Present in [X%] of losses
  Recommendation: Add as disqualification criterion

---

RECOMMENDED UPDATES
--------------------

SEGMENT UPDATES:
1. [Update 1] — "[Specific change to segment definition]"
   Evidence: [Data supporting this change]
   Impact: [Expected improvement]

2. [Update 2] — "[Specific change]"
   Evidence: [Data]

PERSONA UPDATES:
1. [Update 1] — "[Specific change to persona definition]"
   Evidence: [Data]

2. [New persona suggestion] — "[Why we should add this persona]"
   Evidence: [Appeared in N deals, X% win rate]

PLAYBOOK UPDATES:
1. [Update 1] — "[Add/modify value props based on what resonates]"
2. [Update 2] — "[Update objection handling based on loss patterns]"

QUALIFICATION CRITERIA UPDATES:
Add:
+ [New criterion] — "[Why: seen in X% of wins]"
+ [New criterion] — "[Why]"

Remove or de-emphasize:
- [Criterion] — "[Why: not predictive of wins]"

---

Apply these updates?

1. Apply segment updates
2. Apply persona updates
3. Apply playbook updates
4. Apply all updates
5. Review specific recommendation first
6. Export report only

Step 6: Apply Updates (if requested)

# Update segment
update_entity({
  entityType: "segment",
  oId: "<segment_oId>",
  instructions: "<specific updates based on findings>"
})

# Update persona
update_entity({
  entityType: "persona",
  oId: "<persona_oId>",
  instructions: "<specific updates>"
})

# Update playbook value props
update_value_props({
  playbookOId: "<playbook_oId>",
  updates: [{ oId: "<vp_oId>", details: "<updated details>" }],
  reasoning: "Updated based on ICP refinement analysis: [evidence]"
})

# Create new persona if recommended
create_entity({
  entityType: "persona",
  name: "<new persona name>",
  instructions: "<details from deal analysis>"
})

Step 7: Offer Follow-Up Actions

What would you like to do next?

1. Deep dive on a specific finding
2. Analyze a specific segment or persona
3. Compare current quarter vs. previous
4. Update a specific library entity
5. Generate updated enablement materials
6. Export the full report
7. Done

MCP Tools Used

Library Context

• list_all_entities – Segments, personas, products
• get_entity – Full entity details for ICP definition

Deal Analytics

• list_events – Won/lost deals
• list_findings – Conversation insights, objections, signals
• get_event_detail – Deep dive into specific deals

Library Updates

• update_entity – Update segments, personas
• update_value_props – Update playbook value props
• create_entity – New personas or segments

Intelligence

• search_knowledge_base – Cross-reference patterns

Error Handling

No Deal Data:

No deal outcomes found in the last [N] days.

ICP refinement requires win/loss data. Options:

1. Extend the time period (try –period 365)
2. Review conversation data instead (calls/emails without deal outcomes)
3. Do a manual ICP review using your library definitions

Insufficient Data:

Found only [N] deals. Statistical patterns may not be reliable.

I’ll highlight patterns but flag low-confidence findings. Consider extending the period or combining with qualitative analysis.

No Segments Defined:

No segments found in your library.

I can still analyze deal patterns, but there’s nothing to compare against. Consider creating segments first: /octave:library create segment Or I’ll suggest segment definitions based on the deal data.

Related Skills

• /octave:wins-losses – Deeper win/loss analysis (complements ICP refinement)
• /octave:insights – Field intelligence trends
• /octave:prospector – Use refined ICP to find new prospects
• /octave:audit – Check library health after updates
• /octave:library – Manually update entities