Deal Contact Gap Analysis

Deal Contact Gap Analysis

Analyze open HubSpot deals to map existing contacts against the typical buying committee, identify missing roles, and find recommended contacts to fill the gaps via Amplemarket.

Instructions

When a user wants to analyze the buying committee coverage on their deals, pull deal and contact data from HubSpot, map roles to a buying committee framework, identify gaps, and find candidates to fill them.

Steps

1. Gather analysis parameters by asking the user:

   (a) "Which deals should I analyze?" Accept:

   • A specific deal name or list of deal names
   • "All deals in [stage]" (e.g., "all deals in Negotiation")
   • "My top deals" (sorted by amount)
   • "All open deals" (with a warning about volume)

   (b) "What does your typical buying committee look like?" Offer a default framework and let the user customize:

   • Economic Buyer (CFO, VP Finance, CEO - person who controls budget)
   • Champion (day-to-day advocate - often the person who initiated the evaluation)
   • Technical Evaluator (CTO, VP Engineering, IT Director - validates technical fit)
   • End User / Operator (Manager, Director of the team that will use the product)
   • Legal / Procurement (General Counsel, Procurement Director - handles contracts)
   • Executive Sponsor (C-Suite or SVP - signs off on strategic decisions)

   If the user has a different framework (e.g., MEDDPICC roles, custom roles), adapt to their model.

   (c) "Which deal stages should I focus on?" Default to mid-to-late stages where multi-threading matters most (Qualification, Demo, Proposal, Negotiation). Allow the user to adjust.

2. Retrieve the current user's identity by calling mcp__claude_ai_HubSpot__get_user_details to get the authenticated user's name, email, and HubSpot user ID for owner-based filtering.

3. Search for target deals by calling mcp__claude_ai_HubSpot__search_crm_objects with:

   • object_type: "deals"
   • filter_groups: Based on user input - deal name, stage, owner, or open status
   • properties: ["dealname", "amount", "dealstage", "pipeline", "hubspot_owner_id", "closedate", "createdate", "hs_lastmodifieddate"]
   • associations: ["contacts", "companies"]
   • sorts: [{"propertyName": "amount", "direction": "DESCENDING"}]
   • limit: 20

4. Retrieve associated contacts for each deal by calling mcp__claude_ai_HubSpot__get_crm_objects with:

   • object_type: "contacts"
   • object_ids: contact IDs associated with each deal
   • properties: ["firstname", "lastname", "email", "jobtitle", "company", "phone", "linkedin_url", "hs_lead_status"]

   Also retrieve company data:

   • object_type: "companies"
   • object_ids: company IDs from the deals
   • properties: ["name", "domain", "industry", "numberofemployees"]

5. Map contacts to buying committee roles. For each deal, analyze each contact's job title and map them to the buying committee framework:

   Mapping logic by title keywords:

   • Economic Buyer: titles containing CFO, VP Finance, CEO, COO, "Head of Finance", "Finance Director"
   • Champion: the primary contact or the one with the most engagement (often the deal creator)
   • Technical Evaluator: titles containing CTO, VP Engineering, "VP of Technology", "IT Director", "Head of Engineering", "Solutions Architect"
   • End User / Operator: titles containing Manager, Director (non-C-suite, non-VP), "Team Lead", "Head of [functional area]"
   • Legal / Procurement: titles containing "General Counsel", "Legal", "Procurement", "Vendor Management", "Contracts"
   • Executive Sponsor: titles containing CEO, President, COO, SVP, "Managing Director", "General Manager"

   Flag contacts that cannot be clearly mapped to a role as "Unclassified" and note their title for user review.

6. Identify gaps in each deal. Compare the mapped contacts against the full buying committee framework:

   • List which roles are covered and by whom
   • List which roles are missing
   • Calculate a coverage score: (roles filled / total roles) as a percentage
   • Flag single-threaded deals (only 1 contact) as highest risk

7. Find candidates for missing roles by calling mcp__claude_ai_Amplemarket__search_people for each deal company with gaps:

   • company_domains: [deal company domain]
   • person_titles: titles matching the missing roles (e.g., if missing Technical Evaluator, search for "CTO", "VP Engineering", "VP Technology", "Director Engineering")
   • person_seniorities: appropriate seniority levels for the missing role
   • full_output: true
   • page_size: 5

   Limit people searches to the top 10 deals by amount to manage credit usage.

8. Enrich top recommended contacts by calling mcp__claude_ai_Amplemarket__enrich_person for the #1 candidate for each missing role (limit to 10-15 enrichments):

   • Use linkedin_url from the search results
   • Set reveal_email: true
   • Extract verified email, current title, and background for outreach

9. Generate approach suggestions for each gap. Based on the missing role and the existing contacts:

   • Suggest who on the existing contact list could introduce the missing person
   • Draft an approach strategy (direct outreach, warm introduction, event-based)
   • Generate a suggested opener referencing the deal context and the missing person's role

10. Present the gap analysis with:

• Summary across all deals: average coverage score, most commonly missing roles, single-threaded deals
• Per-deal breakdown: contacts mapped to roles, gaps identified, recommended candidates
• Risk assessment: deals at highest risk due to gaps (large amount + low coverage)
• Action plan: prioritized list of contacts to add, with approach strategies

Important Notes

• The buying committee framework should be adapted to the user's sales motion. Enterprise deals may need 6-8 roles; SMB deals may only need 2-3 (champion + economic buyer).
• Title-to-role mapping is imperfect. Titles vary widely across companies. "Head of Growth" could be marketing, sales, or product. Present mappings as suggestions and let the user correct them.
• Single-threaded deals (only 1 contact) in mid-to-late stages are the highest-risk finding. Prioritize these in the output.
• Limit search_people and enrich_person calls to the top 10 deals by value to manage Amplemarket credits. Offer to expand: "Shall I also search for missing contacts on the remaining X deals?"
• If a deal has no associated contacts at all, flag it immediately: "This deal has zero contacts. It cannot progress without at least a champion identified."

Dynamic Fields Generated

The following dynamic fields are populated for each identified gap and can be used in multi-threading outreach templates:

Examples

Example 1: Full Pipeline Gap Analysis

User prompt: "Map the buying committee on my open deals. My email is smartinez@ourcompany.com"

What the skill does:

1. Calls mcp__claude_ai_HubSpot__get_user_details to confirm user identity.
2. Calls mcp__claude_ai_HubSpot__search_crm_objects with object_type: "deals", filtering for open deals owned by the user, sorted by amount descending.
3. Calls mcp__claude_ai_HubSpot__get_crm_objects for contacts and companies associated with each deal.
4. Maps each contact to a buying committee role based on their title.
5. Identifies missing roles for each deal.
6. Calls mcp__claude_ai_Amplemarket__search_people at each deal company to find candidates for missing roles.
7. Calls mcp__claude_ai_Amplemarket__enrich_person for the top candidate per missing role.
8. Presents the comprehensive gap analysis.

Example output:

BUYING COMMITTEE GAP ANALYSIS - Sarah Martinez

Executive Summary

• Deals analyzed: 8
• Average coverage score: 42%
• Single-threaded deals: 2 (CRITICAL)
• Most commonly missing role: Technical Evaluator (missing in 6 of 8 deals)
• Second most missing: Legal/Procurement (missing in 7 of 8 deals)
• Total pipeline at risk from poor multi-threading: $620K

Deal-by-Deal Analysis

1. Acme Corp - Enterprise License ($185,000 | Proposal Sent)

Coverage: 50% (3 of 6) | Risk: Medium

Recommended contacts to fill gaps:

2. TechFlow - Platform Deal ($210,000 | Negotiation)

Coverage: 17% (1 of 6) | Risk: CRITICAL - single-threaded at $210K in Negotiation

Recommended contacts:

3. DataSync - Annual License ($125,000 | Qualification) ...

PRIORITY ACTION PLAN

1. URGENT: TechFlow ($210K) - Single-threaded in Negotiation. Multi-thread to VP Finance and CEO immediately.
2. HIGH: Acme Corp ($185K) - Add Technical Evaluator before POC discussion.
3. MEDIUM: DataSync ($125K) - Find champion; current contact is too junior.

Example 2: Single Deal Deep Dive

User prompt: "Who's missing from the Acme Corp deal? We typically need a champion, economic buyer, technical evaluator, and legal."

What the skill does:

1. Calls mcp__claude_ai_HubSpot__search_crm_objects searching for deals with name containing "Acme Corp".
2. Retrieves all associated contacts via mcp__claude_ai_HubSpot__get_crm_objects.
3. Maps contacts to the user's 4-role framework.
4. Searches for missing roles at Acme Corp via mcp__claude_ai_Amplemarket__search_people.
5. Enriches top candidates via mcp__claude_ai_Amplemarket__enrich_person.
6. Presents a focused gap analysis for that single deal.

Example 3: Stage-Based Analysis

User prompt: "Check multi-threading on all deals in Negotiation stage. Flag any that are single-threaded"

What the skill does:

1. Calls mcp__claude_ai_HubSpot__get_user_details.
2. Calls mcp__claude_ai_HubSpot__search_properties to find the correct stage ID for "Negotiation".
3. Calls mcp__claude_ai_HubSpot__search_crm_objects filtering for deals in the Negotiation stage.
4. Retrieves contacts for each deal.
5. Flags deals with only 1 contact as single-threaded.
6. Maps contacts to roles and identifies gaps.
7. Prioritizes results by deal amount, highlighting single-threaded deals first.

Example output (abbreviated):

MULTI-THREADING ANALYSIS - Negotiation Stage

Summary: 5 deals in Negotiation | 2 single-threaded (CRITICAL) | Average contacts per deal: 2.4

Immediate action needed: TechFlow and GlobalTech are both single-threaded in Negotiation with $375K combined value. These deals are at high risk of stalling or being lost.

Troubleshooting