Leadership Change Outreach

Leadership Change Outreach

Detects new leadership hires at target companies and evaluates whether the new leader is relevant to your product — as a direct buyer, a champion, or someone whose mandate aligns with what you sell. If relevant, enriches their profile and drafts personalized outreach that speaks to their new-role priorities.

Why leadership changes work: New leaders re-evaluate everything in their first 90 days. They inherit a vendor stack they didn't choose, a team they didn't build, and KPIs they need to hit fast. They're the most receptive buyers in any organization because:

• They want to put their stamp on the department
• They have a mandate (and often budget) to make changes
• They need quick wins to build credibility with their new org
• They haven't yet formed loyalty to existing vendors

When to Auto-Load

Load this composite when:

• User says "check for leadership changes", "new executive hires", "leadership signal outreach"
• User has a list of companies and wants to find those with relevant new leaders
• An upstream workflow (TAM Pulse, company monitoring) triggers a leadership change check

Detection Method: Apollo (Free Search + Enrichment)

This composite uses a two-phase Apollo pipeline that replaces slower web search approaches:

1. Apollo Free Search — search_people with q_organization_domains + person_titles filters. Returns person IDs, obfuscated names, and titles. No credits consumed. Scans 100+ people across dozens of companies in ~30 seconds.
2. Local Post-Filter — Strict title matching to remove noise from Apollo's fuzzy matching (regional titles, sub-function heads, non-GTM roles). Typically reduces results by 50-60%.
3. Apollo Enrichment by ID — people/match with the person id from free search. Returns full employment history with start_date/end_date for every role, LinkedIn URL, verified email, and full name. Costs 1 credit per person.
4. Change Detection — Filter enriched results by start_date on the current: true employment entry within the lookback window.

Why this beats web search: Web search relies on press releases and announcements — most leadership changes below C-suite are never publicly announced. Apollo pulls from LinkedIn profile data directly, catching changes that web search misses. Speed: ~90 seconds total vs 5+ minutes for web search.

Cost: 1 Apollo credit per person enriched. With a tight post-filter (VP+ GTM titles only), a scan of 10-15 companies typically costs 30-50 credits.

Important: Apollo tracks start dates at month granularity (e.g., 2026-02-01), not exact day. Set lookback windows accordingly — use full months rather than exact day counts.

Step 0: Configuration (One-Time Setup)

On first run for a client/user, collect and store these preferences. Skip on subsequent runs.

Leader Relevance Mapping

Example for a sales AI product:

buyer_leader_titles: ["VP Sales", "CRO", "Chief Revenue Officer", "SVP Sales"]
champion_leader_titles: ["Director of Sales Ops", "Head of Revenue Operations", "VP Business Development"]
aligned_leader_titles: ["COO", "CEO", "VP Operations"]
relevant_departments: ["Sales", "Revenue", "Operations", "Business Development"]

Signal Detection Config

Apollo Title List

The free search uses person_titles to filter. Define these based on the client's buyer/champion/aligned titles. Default VP+ GTM titles:

titles = [
    # C-Suite
    'CRO', 'Chief Revenue Officer',
    'CMO', 'Chief Marketing Officer',
    'CCO', 'Chief Commercial Officer',
    # VP-level (Sales, Marketing, Growth, Revenue, RevOps, Demand Gen, BD, Partnerships, CS, Commercial, GTM)
    'VP of Sales', 'VP Sales', 'Vice President of Sales', 'Vice President Sales',
    'SVP Sales', 'SVP of Sales',
    'VP of Marketing', 'VP Marketing', 'Vice President of Marketing',
    'SVP Marketing', 'SVP of Marketing',
    'VP of Growth', 'VP Growth', 'Vice President of Growth',
    'VP of Revenue', 'VP Revenue', 'Vice President of Revenue',
    'VP of Revenue Operations', 'VP RevOps',
    'VP of Demand Generation', 'VP Demand Gen',
    'VP of Business Development', 'VP Business Development',
    'VP of Partnerships', 'VP Partnerships',
    'VP of Customer Success', 'VP Customer Success',
    'VP of Commercial', 'VP Commercial',
    'VP GTM', 'VP of GTM',
    # Head-level
    'Head of Sales', 'Head of Marketing', 'Head of Growth',
    'Head of Revenue', 'Head of Revenue Operations', 'Head of RevOps',
    'Head of Demand Generation', 'Head of Demand Gen',
    'Head of Business Development', 'Head of Partnerships',
    'Head of Customer Success', 'Head of Commercial',
    'Head of GTM',
]

Important: Do NOT use Apollo's person_seniority filter (e.g., ['vp', 'c_suite']) — it's too broad and returns regional managers, ICs with inflated titles, etc. Use explicit person_titles and post-filter locally instead.

Post-Filter Rules

Apollo does fuzzy title matching, so results will include noise. Apply a strict local post-filter that:

1. Rejects non-GTM functions: engineering, talent, legal, privacy, data science, analytics, product marketing, field marketing, partner marketing, customer marketing, content, communications, community, solutions marketing, enablement, marketing operations
2. Rejects regional/sub-segment roles: Area VP, AVP, regional heads, EMEA/APAC/Americas-specific roles, enterprise sales by region (West/East/Central/etc.), channel sales, velocity sales, sales development, sales finance, sales strategy
3. Rejects Apollo garbage: Any title containing "related to search terms"
4. Requires valid prefix: Title must start with VP/Vice President/SVP/Head of/Chief/CRO/CMO/CCO/President

This typically reduces results by 50-60% (e.g., 100 raw → 40 filtered).

Outreach Config

Your Company Context

Store config in: clients/<client-name>/config/signal-outreach.json or equivalent.

Step 1: Detect Leadership Changes (Apollo Pipeline)

Purpose: For each company in the input list, find VP+ GTM leaders and detect who started recently.

Input Contract

companies: [
  {
    name: string          # Required
    domain: string        # Required (used for q_organization_domains)
    industry?: string     # Optional
    size?: string         # Optional
  }
]
titles: string[]                      # From config (default VP+ GTM list above)
lookback_days: integer                # From config (default: 90)

Process

Phase 1: Apollo Free Search (~30 seconds)

Use apollo_client.search_people() with:

filters = {
    'q_organization_domains': '\n'.join([c['domain'] for c in companies]),  # All domains in one query
    'person_titles': titles,       # From config
    'per_page': 100,
    'page': 1
}

Key details:

• Use q_organization_domains (NOT organization_domains) — the q_ prefix is required for domain filtering
• All company domains can be passed in a single query (newline-separated)
• Free tier returns: id, first_name, last_name (obfuscated as "?"), title, organization.name, last_refreshed_at
• Free tier does NOT return: full last name, LinkedIn URL, email, or employment history
• If total_entries > 100, paginate with page: 2, etc.

Phase 2: Local Post-Filter (~instant)

Apply the strict post-filter rules from Step 0 to remove noise. This is critical — Apollo's fuzzy title matching will return regional managers, sub-function heads, and non-GTM roles.

def is_valid_gtm_leader(title):
    """Returns True only for top-level GTM leadership roles."""
    tl = title.lower().strip()

    # 1. Reject non-GTM functions
    reject_keywords = ['engineering', 'engineer', 'talent', 'legal', 'privacy',
                       'data science', 'analytics', 'product marketing',
                       'field marketing', 'partner marketing', 'customer marketing',
                       'content', 'communications', 'community',
                       'channel sales', 'solutions marketing',
                       'enablement', 'education', 'operations & marketing',
                       'marketing operations']
    if any(kw in tl for kw in reject_keywords):
        return False

    # 2. Reject regional/sub-segment roles
    regional_keywords = ['area vice president', 'avp ', 'regional', 'emea', 'apac',
                         'apj', 'americas', 'enterprise sales west', 'enterprise sales east',
                         'enterprise sales central', 'enterprise sales south',
                         'enterprise sales north', 'enterprise sales -',
                         'enterprise sales,', 'na enterprise',
                         'majors sales', 'velocity sales',
                         'canada', 'latin america', 'u.s.', 'uk&i',
                         'chief of staff', 'sales finance', 'sales strategy',
                         'sales development']
    if any(kw in tl for kw in regional_keywords):
        return False

    # 3. Reject Apollo garbage
    if 'related to search terms' in tl:
        return False

    # 4. Must start with a valid prefix
    valid_prefixes = [
        'vp ', 'vp,', 'vp/', 'vice president of', 'vice president,',
        'svp', 'senior vice president',
        'head of sales', 'head of marketing', 'head of growth',
        'head of revenue', 'head of demand gen', 'head of business development',
        'head of partnerships', 'head of customer success', 'head of commercial',
        'head of gtm',
        'chief revenue officer', 'chief marketing officer', 'chief commercial officer',
        'cro', 'cmo', 'cco',
        'president',
    ]
    return any(tl.startswith(p) for p in valid_prefixes)

Phase 3: Apollo Enrichment by ID (~1 second per person)

For each person that passes the post-filter, enrich using the id from free search:

# Use the person's id from free search — this is the key to making enrichment work
# without full names (which free tier obfuscates)
url = "https://api.apollo.io/api/v1/people/match"
payload = {"api_key": api_key, "id": person_id}

What enrichment returns (1 credit per person):

• name — full name (no longer obfuscated)
• employment_history — array of all roles with start_date, end_date, title, organization_name, current (boolean)
• linkedin_url — full LinkedIn profile URL
• email + email_status — verified work email
• city, state, country — location

Important: Do NOT use bulk_enrich_people with first_name + organization_name — free search obfuscates last names, and Apollo can't match without them. Always enrich by id.

Rate limiting: Add a small delay (0.5s) every 5 requests to avoid 429s. If rate limited, respect the Retry-After header.

Phase 4: Change Detection

For each enriched person, extract the current: true employment entry and check its start_date:

emp_history = person.get('employment_history', [])
current_role = next((e for e in emp_history if e.get('current')), None)
start_date = current_role.get('start_date', '') if current_role else ''  # e.g. "2026-02-01"

# Check if within lookback window
# Note: Apollo uses month granularity (YYYY-MM-01), not exact day

Determine change type:

• new_hire: Previous role was at a different company
• internal_promotion: Previous role was at the same company

Output Contract

leadership_changes: [
  {
    company: {
      name: string
      domain: string
    }
    new_leader: {
      full_name: string
      new_title: string
      start_date: string              # ISO date (month granularity: "2026-02-01")
      previous_company: string
      previous_title: string
      change_type: "new_hire" | "internal_promotion"
      linkedin_url: string
      email: string
      email_status: string            # "verified", "guessed", etc.
      city: string
      state: string
      country: string
    }
  }
]

Output Files

Save two files:

1. CSV (leadership-change-scan.csv) — all enriched people sorted by start_date descending, with columns: name, title, company, domain, start_date, change_type, previous_title, previous_company, previous_end_date, email, email_status, linkedin_url, city, state, country
2. Markdown (leadership-change-outreach.md) — formatted report with signal summary, qualification, and email drafts

Human Checkpoint

Scanned X companies → Y raw results → Z after post-filter → W enriched

Leadership changes in last {lookback_days} days:

| Company | New Leader | Title | Started | Previous Role | Type |
|---------|-----------|-------|---------|---------------|------|
| Acme Corp | Jane Smith | VP Sales | 2026-02-01 | Dir. Sales @ Competitor Inc | new_hire |
| Beta Inc | Tom Brown | CRO | 2026-01-01 | VP Revenue @ Beta Inc | internal_promotion |

Credits used: W

Proceed with relevance evaluation? (Y/n)

Step 2: Evaluate Relevance & Prioritize

Purpose: For each leadership change, evaluate whether the new leader is relevant to your product — and determine the best outreach approach. Pure LLM reasoning — inherently tool-agnostic.

Input Contract

leadership_changes: [...]            # From Step 1 output
your_company: {
  description: string
  pain_point: string
  proof_points: string[]
  quick_wins: string[]
  before_state: string
}
buyer_leader_titles: string[]
champion_leader_titles: string[]
aligned_leader_titles: string[]

Process

For each leadership change, evaluate across three dimensions:

A) Role Relevance

B) Timing Window

C) Background Signal

The new leader's previous company and role adds context:

Scoring

• Tier 1 (Act Today): Direct buyer + <30 days in role + external hire. Fresh eyes, budget authority, evaluating everything.
• Tier 2 (Act This Week): Direct buyer 30-60 days in, OR champion <30 days, OR came from a customer/competitor customer.
• Tier 3 (Queue): Aligned mandate, OR 60-90 days in role, OR internal promotion with champion title.
• Drop: No role relevance, OR >90 days in role with weak fit.

For each qualified leader, generate:

• Relevance reasoning: Why this leader would care about your product right now
• Outreach angle: The specific hook based on their role + timing + background
• Key insight: One thing about their situation that makes the outreach personal

Output Contract

qualified_leaders: [
  {
    ...leadership_change_fields,
    role_relevance: "direct_buyer" | "champion" | "aligned_mandate"
    timing_window: "honeymoon" | "assessment" | "action" | "established"
    background_signal: string         # e.g. "Came from a competitor customer"
    priority_tier: "tier_1" | "tier_2" | "tier_3"
    relevance_reasoning: string
    outreach_angle: string
    key_insight: string
  }
]
dropped_leaders: [
  { name: string, company: string, drop_reason: string }
]

Human Checkpoint

## Relevance Evaluation

### Tier 1 — Act Today (X leaders)
| Leader | Company | Title | Days In | Type | Angle |
|--------|---------|-------|---------|------|-------|
| Jane Smith | Acme | VP Sales | 32 | Direct buyer, Assessment window | "Now that you've assessed the sales stack at Acme..." |

### Tier 2 — Act This Week (X leaders)
| ... |

### Tier 3 — Queue (X leaders)
| ... |

### Dropped (X leaders)
| Leader | Company | Reason |
|--------|---------|--------|
| ...    | ...     | ...    |

Approve before we draft outreach?