Competitor Displacement Targeting

Competitor Displacement Targeting

Find companies using a competitor's product across multiple evidence vectors and target decision-makers for rip-and-replace campaigns.

Instructions

When a user wants to identify companies using a competitor's product and prospect into those accounts, execute this multi-step research and targeting workflow.

Steps

1. Gather targeting criteria from the user. Ask for:

   • Competitor name(s): Which product(s) or vendor(s) to target (e.g., "Salesforce", "HubSpot CRM", "Outreach and Salesloft")
   • Evidence types to search: Which vectors matter most: case studies, G2/Capterra reviews, job postings, tech stack databases, social proof (default: all)
   • Target decision-maker roles: Who should receive outreach (e.g., "VP of Sales", "CTO", "Head of Revenue Operations")
   • ICP filters: Industry, company size, geography, or other firmographic constraints

   If the user provides only a competitor name, ask: "What roles should I target at these companies, and any filters on industry, size, or geography?"

2. Construct WebSearch queries across multiple evidence vectors. Run at least 4 of the following searches per competitor using WebSearch:

   • Case studies: "[competitor]" "case study" OR "customer story" OR "success story"
   • Review sites: "[competitor]" site:g2.com OR site:capterra.com
   • Social proof: "[competitor]" "we use" OR "we switched to" OR "we chose" OR "we implemented"
   • Job postings: "[competitor]" "experience with" OR "proficiency in" job OR hiring
   • Tech stack databases: "[competitor]" site:stackshare.io OR site:builtwith.com

   For multiple competitors, run separate query sets for each competitor name. Adapt queries to include the competitor's common product names and abbreviations.

3. Extract company names by calling WebFetch on the top 5-10 results from each evidence vector. For each fetched page, extract:

   • Company name mentioned as a customer or user
   • Type of evidence (case study, review, job posting, etc.)
   • Specific details about their usage (pain points, use case, duration)
   • URL of the source for reference

4. Deduplicate companies found across vectors. Merge companies that appear in multiple evidence sources and track which vectors confirmed each company. Assign an evidence strength score:

   • Strong (3+ vectors): Company appears in case studies AND reviews AND job postings
   • Medium (2 vectors): Company appears in two different evidence types
   • Light (1 vector): Company appears in only one source

5. Enrich each identified company by calling mcp__claude_ai_Amplemarket__enrich_company with the company domain (inferred from the company name or extracted from evidence URLs). Key data points to extract:

   • Industry and sub-industry
   • Company size and employee count
   • Headquarters and locations
   • Company type and description

6. Filter against the user's ICP. Remove companies that do not match the user's firmographic criteria:

   • Industry mismatch
   • Company size outside target range
   • Geography outside target region
   • Company type mismatch (e.g., user only wants privately held)

   Report how many companies passed vs. were filtered out.

7. Search for decision-makers at matching companies by calling mcp__claude_ai_Amplemarket__search_people with:

   • company_domains: [matching company domain]
   • person_titles: target titles from user's criteria
   • person_seniorities: appropriate seniority levels
   • full_output: true
   • page_size: 10

   For efficiency, batch companies with similar domains and run searches for the top 10-15 highest-evidence-strength companies first.

8. Present results grouped by evidence strength. Format the output as a Competitor Displacement Report with dynamic fields populated for each prospect:

   Displacement Opportunity Summary

   • Competitor targeted
   • Total companies identified
   • Companies passing ICP filter
   • Decision-makers found
   • Breakdown by evidence strength (Strong / Medium / Light)

   Strong Evidence Accounts (table with company, evidence types, evidence detail, decision-makers found)

   Medium Evidence Accounts (same format)

   Light Evidence Accounts (same format)

   For each decision-maker, populate the {{competitor_*}} dynamic fields described in the Dynamic Fields Generated section below.

9. Offer to create a lead list with competitor context. If the user agrees, call mcp__claude_ai_Amplemarket__create_lead_list with:

   • name: descriptive name (e.g., "Salesforce Displacement - VP Sales - SaaS - Mar 2026")
   • type: "linkedin" (using LinkedIn URLs from search_people results)
   • leads: array of lead objects from the decision-maker search
   • options: enrichment settings as requested

   Include competitor context in the list name so the outreach team understands the targeting angle.

Important Notes

• Evidence from case studies and review sites is the strongest signal. A company featured in a competitor's case study is almost certainly a current customer.
• Job postings mentioning a competitor tool indicate usage but may also indicate planned adoption. Flag this ambiguity when presenting results.
• Social proof from blog posts or social media can be outdated. Note the date of the source when available and flag evidence older than 18 months.
• Always respect rate limits when running multiple WebSearch and WebFetch calls. Space out requests and prioritize the highest-value evidence vectors first.
• Some competitors have common names that produce noisy results (e.g., "Monday" for monday.com). Use the full product name or domain in queries to reduce false positives.

Dynamic Fields Generated

Examples

Example 1: Finding Salesforce Users for CRM Displacement

User prompt: "Find companies using Salesforce that could switch to our CRM. Target VP of Sales and CROs at mid-market SaaS companies in the US."

What the skill does:

1. Runs WebSearch queries:

• "Salesforce" "case study" OR "customer story" OR "success story"
• "Salesforce" site:g2.com OR site:capterra.com
• "Salesforce" "we use" OR "we switched to" OR "we chose" site:linkedin.com OR site:reddit.com
• "Salesforce CRM" "experience with" OR "proficiency in" job OR hiring
• "Salesforce" site:stackshare.io OR site:builtwith.com

2. Calls WebFetch on top results from each vector.
3. Extracts and deduplicates 47 companies found across vectors.
4. Calls mcp__claude_ai_Amplemarket__enrich_company for each company.
5. Filters to 23 companies matching ICP (mid-market SaaS, US, 201-5000 employees).
6. Calls mcp__claude_ai_Amplemarket__search_people with person_titles: ["VP of Sales", "CRO", "Chief Revenue Officer"], person_seniorities: ["VP", "C-Suite"] for each company.

Example output (abbreviated):

COMPETITOR DISPLACEMENT REPORT: Salesforce

Summary

Strong Evidence Accounts

Dynamic fields for CloudMetrics contacts:

• {{competitor_name}}: Salesforce Sales Cloud
• {{competitor_evidence_type}}: Case Study, G2 Review, Job Posting
• {{competitor_evidence_detail}}: Featured in Salesforce 2025 mid-market case study; left 3-star G2 review citing reporting limitations
• {{competitor_evidence_url}}: https://salesforce.com/customer-stories/cloudmetrics
• {{competitor_pain_points}}: Reporting inflexibility, high per-seat cost, complex admin overhead
• {{competitor_company_name}}: CloudMetrics
• {{competitor_company_size}}: 320 employees
• {{competitor_company_industry}}: Computer Software / Cloud Analytics
• {{competitor_switch_reason}}: G2 review explicitly mentions evaluating alternatives for better reporting
• {{competitor_suggested_opener}}: "I noticed CloudMetrics uses Salesforce for pipeline management. A few analytics companies your size have told us the reporting flexibility wasn't keeping up with their growth. Curious if that's something your team has run into."
• {{competitor_evidence_strength}}: Strong (3 vectors)

Would you like me to create a lead list from these 41 decision-makers?

Example 2: Targeting Companies Using a Marketing Automation Tool

User prompt: "Find companies using Marketo. I want to reach Heads of Demand Gen and Marketing Directors at B2B companies with 500-2000 employees."

What the skill does:

1. Runs WebSearch queries across all 5 vectors using "Marketo" and "Adobe Marketo Engage" as search terms.
2. Fetches and extracts companies from case studies on marketo.com/customers, G2 reviews, job postings requiring "Marketo experience," and StackShare profiles.
3. Deduplicates 62 companies. Enriches via mcp__claude_ai_Amplemarket__enrich_company.
4. Filters to 31 B2B companies in the 500-2000 range.
5. Searches for person_titles: ["Head of Demand Gen", "Head of Demand Generation", "Director of Marketing", "Marketing Director"], person_seniorities: ["Head", "Director"].
6. Returns results grouped by evidence strength with {{competitor_*}} fields populated.
7. Offers to create lead list: "Marketo Displacement - Demand Gen Leaders - B2B 500-2K - Mar 2026".

Example output (abbreviated):

COMPETITOR DISPLACEMENT REPORT: Marketo

Summary

Strong Evidence Accounts

Dynamic fields for GrowthEngine contacts:

• {{competitor_name}}: Adobe Marketo Engage
• {{competitor_evidence_type}}: Case Study, G2 Review, StackShare
• {{competitor_evidence_detail}}: Featured in Adobe customer story for lead scoring automation; G2 review rates 3.5 stars citing steep learning curve
• {{competitor_pain_points}}: Complex setup requiring dedicated admin, steep learning curve, high cost relative to usage
• {{competitor_company_name}}: GrowthEngine
• {{competitor_company_size}}: 720 employees
• {{competitor_company_industry}}: Computer Software / Marketing Technology
• {{competitor_switch_reason}}: G2 review mentions "considering simpler alternatives", which indicates active evaluation
• {{competitor_suggested_opener}}: "I saw GrowthEngine uses Marketo for lead scoring. A few marketing teams your size have told us the complexity of maintaining Marketo workflows was pulling their ops team away from strategy work. Is that something you've experienced?"
• {{competitor_evidence_strength}}: Strong (3 vectors)

Example 3: Multi-Competitor Search

User prompt: "Find companies using Outreach, Salesloft, or Apollo for sales engagement. Target VP Sales and Revenue Operations leaders at companies with 200-1000 employees."

What the skill does:

1. Runs separate WebSearch query sets for each competitor:

• 5 queries for "Outreach.io" / "Outreach sales engagement"
• 5 queries for "Salesloft" / "SalesLoft sales engagement"
• 5 queries for "Apollo.io" / "Apollo sales engagement"

2. Fetches top results across all three competitors.
3. Deduplicates across competitors, and flags companies found using multiple competitors (e.g., "Previously used Outreach, reviewed Salesloft on G2").
4. Enriches and filters to ICP: 200-1000 employees.
5. Searches for decision-makers: person_titles: ["VP of Sales", "VP Sales", "Head of Revenue Operations", "Director of Revenue Operations"], person_seniorities: ["VP", "Head", "Director"].
6. Presents results with the specific competitor each company uses in {{competitor_name}}, enabling tailored messaging per competitor.

Example output (abbreviated):

COMPETITOR DISPLACEMENT REPORT: Outreach / Salesloft / Apollo

Summary

Multi-Competitor Accounts (Highest Priority)

Example note: Multi-competitor accounts are flagged as highest priority because active tool evaluation or dissatisfaction with current tooling is strongly implied. The {{competitor_name}} field lists all detected tools (e.g., "Outreach + Salesloft") and {{competitor_switch_reason}} references the multi-tool complexity angle (e.g., "Company appears to be evaluating multiple sales engagement tools, which indicates active buying cycle").

Troubleshooting