Sales Coaching

An AI coach that studies everything you do in sales — your emails, your calls, your deals — finds the patterns in what works and what doesn't, and gives you specific, actionable coaching to get better.

This isn't generic sales advice ("always be closing"). It's coaching derived from YOUR data: your top-performing emails, your winning calls, your successful deal patterns, your most common objection fumbles, and your specific product's market.

What makes this different from sequence-performance:

The other composite analyzes the work. This one coaches the worker.

When to Auto-Load

Load this composite when:

• User says "how can I improve my sales", "coach me", "what am I doing wrong", "sales coaching", "help me sell better"
• User says "review my selling style", "analyze my calls", "what patterns do you see in my sales"
• User asks "why am I losing deals", "why aren't people responding", "what are my best emails doing differently"
• After a bad quarter/month and the user wants to diagnose personal performance
• As a periodic (monthly/quarterly) self-improvement exercise

Step 0: Configuration (One-Time Setup)

User Profile

Role options:

• "sdr" — Focus on prospecting, outreach, qualification, meeting booking
• "ae" — Focus on discovery, demos, negotiation, closing
• "founder" — Focus on everything (wearing multiple hats)
• "sales_leader" — Focus on team patterns, not just individual

Data Sources

Store config in the current working directory or wherever the user prefers.

Step 1: Collect Sales Data

Purpose: Pull all available sales data for analysis. The more data types available, the richer the coaching. But the system works with whatever is available.

Input Contract

data_sources: { ... }                 # From config
analysis_window: integer              # Days to look back
user_role: string                     # From config

Data Collection Matrix

Pull from each configured source:

Email Data

email_data: {
  campaigns: [
    {
      name: string
      date_range: { start: string, end: string }
      sequence: [
        {
          touch: integer
          subject: string
          body: string
          sent: integer
          opens: integer
          replies: integer
        }
      ]
      replies: [
        {
          sender: string
          sender_title: string
          reply_text: string
          category: string            # positive, objection, not interested, etc.
          touch_triggered: integer
        }
      ]
    }
  ]
  total_campaigns: integer
  total_emails_sent: integer
  total_replies: integer
  overall_reply_rate: percentage
}

Call Data

call_data: {
  calls: [
    {
      date: string
      prospect_name: string
      prospect_company: string
      call_type: string               # discovery, demo, follow-up, negotiation
      duration_minutes: integer
      transcript: string | null       # Full transcript if available
      recording_url: string | null    # Recording link if available
      outcome: string                 # "next step agreed", "no next step", "closed won", "closed lost"
      notes: string | null            # Post-call notes
    }
  ]
  total_calls: integer
  avg_duration: float
  outcome_distribution: { ... }
}

Pipeline Data

pipeline_data: {
  deals: [
    {
      name: string
      company: string
      stage: string
      created_date: string
      close_date: string | null
      outcome: "open" | "won" | "lost"
      loss_reason: string | null
      amount: number | null
      days_in_pipeline: integer
      source: string | null           # How the deal originated
      touches_before_close: integer | null
    }
  ]
  total_deals: integer
  win_rate: percentage
  avg_cycle_days: float
  avg_deal_size: number | null
}

Output Contract

collected_data: {
  email_data: { ... } | null
  call_data: { ... } | null
  pipeline_data: { ... } | null
  data_richness: "full" | "partial" | "minimal"
  data_summary: string                # "Analyzing X campaigns, Y calls, Z deals over N days"
}

Human Checkpoint

## Data Collected

| Source | Available | Volume |
|--------|-----------|--------|
| Email campaigns | Yes | X campaigns, Y emails sent, Z replies |
| Call transcripts | Yes/No | X calls, Y hours |
| Pipeline deals | Yes | X deals (W won, L lost, O open) |

Analysis window: [start] to [end]
Data richness: [full/partial/minimal]

Proceed with analysis? (Y/n)

Step 2: Analyze Email Performance Patterns

Purpose: Go beyond campaign-level metrics. Find patterns in what makes YOUR best emails work and YOUR worst emails fail. Pure LLM reasoning.

Process

A) Identify Top-Performing Emails

Across all campaigns, find:

• Highest reply-rate subject lines — What do they have in common?
• Highest reply-rate email bodies — What patterns exist?
• Emails that generated positive replies — What specifically triggered interest?
• Emails that generated meetings — The gold standard. What did these say?

For each top performer, extract:

B) Identify Worst-Perf