Account Expansion Mapper

Map untapped departments and roles within existing accounts to surface expansion opportunities, then find contacts in whitespace areas with personalization that references your existing relationship.

Instructions

When a user wants to find expansion opportunities within an existing account, follow these steps to map the org, identify whitespace departments, and generate personalized outreach fields for new contacts.

Steps

1. Gather account details from the user. Ask for:

• Account(s) to expand: company name(s) or domain(s)
• Departments already sold into (e.g., "We sold to Engineering and IT")
• Departments that could benefit from the product (e.g., "Marketing and RevOps could also use it")
• Target seniority levels for new contacts (e.g., "Director and above")

If the user provides only a company name, ask which departments they have already engaged before proceeding.

1. Enrich the company by calling mcp__claude_ai_Amplemarket__enrich_company with:

• domain if the user provided a domain or you can infer it
• linkedin_url if the user provided a LinkedIn company URL

This returns firmographics: industry, size, type, location, description, tech stack, headcount, and more. Use company size to calibrate how many departments to expect.

1. Check existing Amplemarket engagement data by calling mcp__claude_ai_Amplemarket__list_accounts with the company name or domain to find the account. If found, call mcp__claude_ai_Amplemarket__get_account with the account id to retrieve:

• Engagement stats (emails sent, opened, replied)
• Known contacts and their departments
• CRM data and opportunity status
• Account owner and tags
• AI-generated insights

Cross-reference the account's known contacts against the departments the user mentioned to validate which departments are already engaged.

1. Map the org across multiple departments by calling mcp__claude_ai_Amplemarket__search_people multiple times with:

• company_names or company_domains: [target company]
• person_seniorities: user-specified seniority levels (default: ["C-Suite", "VP", "Head", "Director"])
• person_departments: vary this across calls to cover all relevant departments (e.g., "Marketing", "Revenue", "Engineering & Technical", "Finance & Accounting", "Operations", "Human Resources", "IT & Information Security")
• full_output: true
• page_size: 20

Run separate searches for engaged departments (to confirm known contacts) and whitespace departments (to discover new contacts).

1. Cross-reference against known contacts. Compare search results with:

• Contacts from get_account engagement data
• Departments the user identified as already sold into
• Any CRM contact data available

Classify each department as: Engaged (existing contacts and outreach), Partially Engaged (some contacts but gaps in seniority or sub-teams), or Whitespace (no contacts found in Amplemarket data).

1. Research company org structure by calling WebSearch for:

• "[Company name] org chart" or "[Company name] leadership team"
• "[Company name] [department] head" for specific whitespace departments
• "[Company name] new hires [department]" for recent additions to target departments
• "[Company name] department structure" for insight into how teams are organized

Use this to fill gaps where search_people returned limited results and to understand reporting structures.

1. Present the expansion map. Organize findings into a clear visual format:

Engaged Departments - list known contacts, engagement status, and any gaps in coverage.

Whitespace Departments - list discovered contacts, their titles, and why the department is a relevant expansion target.

Partially Engaged Departments - list known contacts alongside newly discovered contacts who have not been reached.

Include a summary table showing department, status, number of known contacts, number of new contacts found, and recommended action.

1. Generate dynamic fields for each whitespace contact. Populate the {{expansion_*}} fields described in the Dynamic Fields Generated section below. These fields enable personalized outreach that references the existing relationship at the company.
2. Offer to create a lead list with whitespace contacts. If the user agrees, call mcp__claude_ai_Amplemarket__create_lead_list with:

• name: a descriptive name (e.g., "Expansion - Datadog - Marketing & RevOps - Mar 2026")
• type: "linkedin" (if LinkedIn URLs are available) or "email"
• leads: array of lead objects from the whitespace search results
• options: ask about enrichment preferences (enrich, validate_email, reveal_phone_numbers)

Optionally, call mcp__claude_ai_Amplemarket__add_leads_to_lead_list if the user wants to add contacts to an existing list.

Important Notes

• Always confirm which departments are already engaged before mapping. False whitespace leads to wasted outreach.
• For companies with fewer than 100 employees, expect fewer distinct departments. Adjust the department search accordingly and note that one person may cover multiple functions.
• For companies with 1000+ employees, expect sub-departments and regional teams. Search with more specific titles to avoid missing relevant contacts.
• If the user does not specify target departments, suggest departments based on the product category (e.g., for sales tools, suggest Marketing, RevOps, Customer Success, Finance).
• When generating the internal reference field, always use verifiable information from the engagement data. Never fabricate contact names or engagement details.

Dynamic Fields Generated

Examples

Example 1: Full Expansion Map for a Large Account

User prompt: "Find expansion opportunities at Datadog. We already sell to their Engineering team. I think Marketing, RevOps, and IT could benefit too. Target Director and above."

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "datadoghq.com".
2. Calls mcp__claude_ai_Amplemarket__list_accounts with domain: "datadoghq.com". Account found, so calls mcp__claude_ai_Amplemarket__get_account with the account ID.
3. Calls mcp__claude_ai_Amplemarket__search_people with company_domains: ["datadoghq.com"], person_departments: ["Engineering & Technical"], person_seniorities: ["Director", "VP", "Head", "C-Suite"], full_output: true, to confirm known Engineering contacts.
4. Calls mcp__claude_ai_Amplemarket__search_people three more times for whitespace departments:

• person_departments: ["Marketing"], same seniority and company filters
• person_departments: ["Revenue"], same filters (for RevOps contacts)
• person_departments: ["IT & Information Security"], same filters

1. Cross-references search results with get_account engagement data.
2. Calls WebSearch with "Datadog marketing leadership" and "Datadog RevOps team" for additional context.
3. Presents expansion map and generates dynamic fields for whitespace contacts.

Example output:

ACCOUNT EXPANSION MAP: Datadog

Department Coverage Summary

Engaged Department: Engineering

Whitespace Department: Marketing

Whitespace Department: Revenue / RevOps

Whitespace Department: IT & Security

Dynamic fields for Marco Silva (VP of Demand Generation, Marketing):

• {{expansion_company_name}}: Datadog
• {{expansion_existing_department}}: Engineering
• {{expansion_existing_contact}}: Jordan Lee, VP of Engineering
• {{expansion_target_department}}: Marketing
• {{expansion_contact_name}}: Marco Silva
• {{expansion_contact_title}}: VP of Demand Generation
• {{expansion_internal_reference}}: Your Engineering team has been evaluating our platform. Jordan Lee's team has seen the product in action
• {{expansion_department_relevance}}: Marketing teams at companies like Datadog use our analytics to tie campaign performance to pipeline impact across their cloud monitoring product lines
• {{expansion_company_context}}: Datadog recently expanded its product suite with new APM and security features, creating more demand gen needs across product lines
• {{expansion_suggested_opener}}: "Your engineering team at Datadog has been exploring our platform, and I thought your demand gen team might benefit from the same analytics capabilities, especially with the new product lines launching."
• {{expansion_company_size}}: 5001-10000 employees

Would you like me to create a lead list with these 18 whitespace contacts?

Example 2: Small Company Expansion (Fewer Departments)

User prompt: "Who else can I sell to at Loom? We've been working with their Product team."

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "loom.com".
2. Calls mcp__claude_ai_Amplemarket__list_accounts with domain: "loom.com". Account found, so calls mcp__claude_ai_Amplemarket__get_account with the account ID.
3. Notes company size is 201-500 employees, and expects fewer distinct departments and smaller leadership teams.
4. Calls mcp__claude_ai_Amplemarket__search_people with company_domains: ["loom.com"], person_seniorities: ["C-Suite", "VP", "Head", "Director", "Manager"], full_output: true, page_size: 20, a broad search to map the entire leadership team at once.
5. Cross-references results with engagement data to identify whitespace.
6. Calls WebSearch with "Loom leadership team" for org context.
7. Presents a compact expansion map suited to the company size.

Example output:

ACCOUNT EXPANSION MAP: Loom

Department Coverage Summary

Note: At 201-500 employees, Loom has fewer distinct departments. Finance, Legal, and Operations may be handled by a small shared team or at the Atlassian parent level.

Whitespace Department: Engineering

Dynamic fields for Jay Patel (VP of Engineering):

• {{expansion_company_name}}: Loom
• {{expansion_existing_department}}: Product
• {{expansion_existing_contact}}: [Known product contact name and title from engagement data]
• {{expansion_target_department}}: Engineering
• {{expansion_contact_name}}: Jay Patel
• {{expansion_contact_title}}: VP of Engineering
• {{expansion_internal_reference}}: Your Product team has been using our platform. They can speak to the value firsthand
• {{expansion_department_relevance}}: Engineering teams at product-led companies like Loom typically benefit from the same tooling their Product counterparts use, especially for async collaboration workflows
• {{expansion_company_context}}: Loom's integration into the Atlassian ecosystem may create new engineering workflow needs
• {{expansion_suggested_opener}}: "Your Product team at Loom has been working with us, and given how closely your Engineering and Product teams collaborate, I thought it might be relevant for your team too."
• {{expansion_company_size}}: 201-500 employees

Example 3: Multiple Accounts Batch Analysis

User prompt: "Account expansion for Snowflake, Confluent, and Databricks. We sell to Engineering at all three. Find Marketing and Finance contacts."

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_company three times with domains: "snowflake.com", "confluent.io", "databricks.com".
2. Calls mcp__claude_ai_Amplemarket__list_accounts three times to check existing engagement at each account.
3. For each account found, calls mcp__claude_ai_Amplemarket__get_account to retrieve engagement data.
4. Calls mcp__claude_ai_Amplemarket__search_people six times total (Marketing + Finance for each of the three companies):

• company_domains: ["snowflake.com"], person_departments: ["Marketing"], person_seniorities: ["Director", "VP", "Head", "C-Suite"], full_output: true
• company_domains: ["snowflake.com"], person_departments: ["Finance & Accounting"], same filters
• Repeat for confluent.io and databricks.com

1. Cross-references all results with engagement data.
2. Calls WebSearch for leadership pages at each company to fill gaps.
3. Presents a consolidated expansion map across all three accounts.

Example output (abbreviated):

BATCH EXPANSION SUMMARY

Total whitespace contacts found: 26 across 3 accounts.

[Full expansion map for each company follows the same format as Example 1]

Would you like me to create separate lead lists per company, or one combined list with all 26 whitespace contacts?

Troubleshooting