competitive-account-research

name	competitive-account-research
description	Generate a comprehensive account brief by combining company enrichment, decision-maker mapping, and engagement history from Amplemarket.
metadata	{"author":"amplemarket","version":"1.0.4","category":"Account Intelligence"}
compatibility	Requires Amplemarket MCP server

Competitive Account Research

Generate a comprehensive account brief by combining company enrichment, decision-maker mapping, and engagement history from Amplemarket.

Instructions

When a user asks for research on a target account, compile a multi-source intelligence brief using the following workflow.

Steps

1. Identify the target account. Extract the company name or domain from the user's request. If ambiguous, ask for clarification.

2. 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, and more.

3. Check internal account data by calling mcp__claude_ai_Amplemarket__list_accounts with the company name or domain to find if this account already exists in the system. If found, call mcp__claude_ai_Amplemarket__get_account with the account id to retrieve:

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

4. Map key decision makers by calling mcp__claude_ai_Amplemarket__search_people with:

   • company_names or company_domains: [target company]
   • person_seniorities: ["C-Suite", "VP", "Head", "Director"]
   • full_output: true
   • page_size: 20

   Then organize results by department to identify the buying committee.

5. Identify stakeholder gaps. Based on the decision makers found, flag any missing roles that would typically be part of the buying committee for your product category (e.g., if no IT stakeholder was found, note this as a gap).

6. Compile the account brief with five sections:

   Section 1: Company Overview

   • Company name, domain, industry, size, type
   • Headquarters and other locations
   • Description and value proposition
   • Tech stack (if available)
   • Recent funding or growth signals

   Section 2: Key Decision Makers

   • Table of identified stakeholders grouped by department
   • Seniority level and LinkedIn URL for each
   • Highlight the likely economic buyer and champions

   Section 3: Engagement History (if account exists in system)

   • Email engagement metrics
   • Previous outreach attempts and responses
   • CRM opportunity status and stage
   • Account owner

   Section 4: Competitive Landscape

   • Known competitors based on industry and tech stack
   • Potential competitive positioning angles
   • Technology overlap or displacement opportunities

   Section 5: Recommended Approach

   • Suggested entry point (which stakeholder to contact first)
   • Messaging angle based on company profile and pain points
   • Multi-threading strategy across the buying committee
   • Timing considerations

Important Notes

• If list_accounts returns no results, the account is net-new. Note this and skip the engagement history section.
• Prioritize the search for decision makers in departments most relevant to the user's product.
• If the user mentions which product or solution they sell, tailor the Competitive Landscape and Recommended Approach sections accordingly.

Examples

Example 1: Domain-Based Research

User prompt: "Research this account for me: datadog.com"

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "datadog.com".
2. Calls mcp__claude_ai_Amplemarket__list_accounts with domain: "datadog.com".
3. If account found, calls mcp__claude_ai_Amplemarket__get_account with the account ID.
4. Calls mcp__claude_ai_Amplemarket__search_people with company_domains: ["datadog.com"], person_seniorities: ["C-Suite", "VP", "Head", "Director"], full_output: true.
5. Compiles the five-section account brief.

Example output (abbreviated):

---

ACCOUNT BRIEF: Datadog

1. Company Overview

Field	Detail
Company	Datadog, Inc.
Domain	datadog.com
Industry	Computer Software / Cloud Monitoring
Size	5001-10000 employees
Type	Public Company (NASDAQ: DDOG)
HQ	New York, NY
Description	Cloud-scale monitoring and analytics platform for infrastructure, applications, and logs

2. Key Decision Makers

Revenue/Sales

Name	Title	LinkedIn
Alex Rivera	CRO	linkedin.com/in/arivera
Kim Tanaka	VP of Sales, Americas	linkedin.com/in/ktanaka

Engineering & Technical

Name	Title	LinkedIn
Jordan Lee	VP of Engineering	linkedin.com/in/jlee
Sam Gupta	Director of Platform Engineering	linkedin.com/in/sgupta

Marketing

Name	Title	LinkedIn
Lisa Park	CMO	linkedin.com/in/lpark

Gap identified: No IT/Security stakeholder found at Director+ level.

3. Engagement History

Metric	Value
Emails Sent	12
Opens	8
Replies	1
Last Contact	2026-01-15
CRM Stage	Prospecting
Account Owner	[e-mail]

4. Competitive Landscape

• Direct competitors in their space: New Relic, Splunk, Dynatrace
• Tech stack may include: AWS, Kubernetes, Go
• Displacement angle: If selling complementary tooling, position as enhancing their existing stack

5. Recommended Approach

• Entry point: Jordan Lee (VP Eng), most likely technical evaluator
• Multi-thread to Alex Rivera (CRO) for budget authority
• Messaging: Focus on how your solution complements their cloud-native architecture
• Timing: Previous engagement showed interest (1 reply). Follow up on that thread

---

Example 2: Company Name Research

User prompt: "Give me intel on Stripe before my call tomorrow"

What the skill does:

1. Calls mcp__claude_ai_Amplemarket__enrich_company with domain: "stripe.com".
2. Checks for existing account via mcp__claude_ai_Amplemarket__list_accounts.
3. Maps decision makers via mcp__claude_ai_Amplemarket__search_people.
4. Returns a concise account brief focused on call preparation.

Example 3: Account with Specific Product Context

User prompt: "Deep dive on Shopify. We sell sales engagement software."

What the skill does:

1. Enriches Shopify and maps decision makers.
2. Focuses decision-maker search on Revenue and IT departments.
3. Tailors competitive landscape to sales engagement tools (Outreach, Salesloft, Apollo).
4. Customizes recommended approach for selling sales engagement software to an e-commerce platform company.

Troubleshooting

Problem	Solution
Company not found in enrichment	Fallback chain: 1) Try the company domain in enrich_company. 2) Try the LinkedIn company URL. 3) Try the parent company domain. 4) Ask user: "Could not find [company]. Can you confirm the exact domain or LinkedIn URL?"
No decision makers found	Fallback chain: 1) Broaden seniority to include "Manager" and "Senior". 2) Try searching by company name instead of domain. 3) Try broader departments (e.g., add "Senior Leadership" if only searching specific functions). 4) For companies with <50 employees, search for "Founder" and "C-Suite" only.
Account not in system	This is normal for net-new accounts. Skip Section 3 and note this is a new target.
Too many decision makers	Filter by specific departments relevant to the user's product. Focus on the 5-8 most relevant stakeholders.
User wants deeper research on one person	Pivot to the enrich-and-score-lead skill for individual prospect deep dives.
Company enrichment succeeds but search_people finds no decision makers	Try broadening seniority to include "Manager" and "Senior". Also try searching by company name instead of domain, as some companies have multiple domains.
Engagement history data seems stale	Flag with: "[Data may be stale, last updated DATE]. This may not reflect recent off-platform interactions." Then suggest: "Want me to re-enrich this account for the latest data?"
search_companies returns 0 for a company that definitely exists	Fallback chain: 1) Try enrich_company with the domain directly. 2) Try the LinkedIn company URL. 3) Try alternate or parent company domains. Some companies are indexed by domain but not searchable by name.