icp-refine

/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

See refinement-report-template.md for the full ICP refinement report template.

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