| name | competitor-displacement-targeting |
| description | Find companies using a competitor's product across multiple evidence vectors and target decision-makers for rip-and-replace campaigns.
|
| metadata | {"author":"amplemarket","version":"1.0.4","category":"Account Intelligence"} |
| compatibility | Requires Amplemarket MCP server |
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
-
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?"
-
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.
-
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
-
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
-
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
-
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.
-
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 criteriaperson_seniorities: appropriate seniority levelsfull_output: truepage_size: 10
For efficiency, batch companies with similar domains and run searches for the top 10-15 highest-evidence-strength companies first.
-
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.
-
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 searchoptions: 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
| Field | Description |
|---|
{{competitor_name}} | Name of the competitor product being displaced (e.g., "Salesforce Sales Cloud") |
{{competitor_evidence_type}} | Type of evidence found (e.g., "Case Study", "G2 Review", "Job Posting", "Tech Stack", "Social Proof") |
{{competitor_evidence_detail}} | Specific detail about their usage (e.g., "Featured in Salesforce's 2025 enterprise case study for pipeline management") |
{{competitor_evidence_url}} | URL of the source where evidence was found |
{{competitor_pain_points}} | Pain points mentioned in evidence (e.g., "Cited complexity and high cost in G2 review") |
{{competitor_company_name}} | Name of the company identified as a competitor customer |
{{competitor_company_size}} | Employee count or size range of the target company |
{{competitor_company_industry}} | Industry of the target company |
{{competitor_switch_reason}} | Inferred or stated reason the company might switch (e.g., "G2 review mentions frustration with reporting limitations") |
{{competitor_suggested_opener}} | Draft opening line referencing the competitor context (e.g., "I noticed your team uses Salesforce for pipeline management. Several companies your size have told us reporting flexibility was a pain point. Curious if that resonates.") |
{{competitor_evidence_strength}} | Evidence strength rating: Strong (3+ vectors), Medium (2 vectors), or Light (1 vector) |
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:
- 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
- Calls WebFetch on top results from each vector.
- Extracts and deduplicates 47 companies found across vectors.
- Calls
mcp__claude_ai_Amplemarket__enrich_company for each company.
- Filters to 23 companies matching ICP (mid-market SaaS, US, 201-5000 employees).
- 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
| Metric | Value |
|---|
| Competitor | Salesforce Sales Cloud |
| Companies Identified | 47 |
| Passed ICP Filter | 23 |
| Decision-Makers Found | 41 |
| Strong Evidence | 8 companies |
| Medium Evidence | 9 companies |
| Light Evidence | 6 companies |
Strong Evidence Accounts
| Company | Domain | Size | Evidence Types | Key Detail | Decision-Makers |
|---|
| CloudMetrics | cloudmetrics.io | 320 | Case Study + G2 + Job Posts | Featured in Salesforce case study; G2 review cites reporting gaps | 2 found |
| DataLoom | dataloom.com | 510 | Case Study + StackShare + Social | Listed on StackShare; founder posted about CRM frustrations | 3 found |
| RevStack | revstack.io | 280 | G2 + Job Posts + Social | 3-star G2 review mentioning cost concerns; hiring for "Salesforce admin" | 1 found |
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:
- Runs WebSearch queries across all 5 vectors using "Marketo" and "Adobe Marketo Engage" as search terms.
- Fetches and extracts companies from case studies on marketo.com/customers, G2 reviews, job postings requiring "Marketo experience," and StackShare profiles.
- Deduplicates 62 companies. Enriches via
mcp__claude_ai_Amplemarket__enrich_company.
- Filters to 31 B2B companies in the 500-2000 range.
- Searches for
person_titles: ["Head of Demand Gen", "Head of Demand Generation", "Director of Marketing", "Marketing Director"], person_seniorities: ["Head", "Director"].
- Returns results grouped by evidence strength with
{{competitor_*}} fields populated.
- Offers to create lead list: "Marketo Displacement - Demand Gen Leaders - B2B 500-2K - Mar 2026".
Example output (abbreviated):
COMPETITOR DISPLACEMENT REPORT: Marketo
Summary
| Metric | Value |
|---|
| Competitor | Adobe Marketo Engage |
| Companies Identified | 62 |
| Passed ICP Filter | 31 |
| Decision-Makers Found | 48 |
| Strong Evidence | 11 companies |
| Medium Evidence | 12 companies |
| Light Evidence | 8 companies |
Strong Evidence Accounts
| Company | Domain | Size | Evidence Types | Key Detail | Decision-Makers |
|---|
| GrowthEngine | growthengine.io | 720 | Case Study + G2 + StackShare | Marketo customer story on Adobe site; listed on StackShare; G2 review mentions complexity | 3 found |
| LeadFlow | leadflow.com | 1,100 | G2 + Job Posts + Social | Hiring "Marketo Certified Expert"; founder posted about automation challenges | 2 found |
| PipelineHQ | pipelinehq.com | 580 | Case Study + Job Posts + BuiltWith | Featured in Marketo webinar; BuiltWith confirms Marketo tracking code | 2 found |
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:
- 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"
- Fetches top results across all three competitors.
- Deduplicates across competitors, and flags companies found using multiple competitors (e.g., "Previously used Outreach, reviewed Salesloft on G2").
- Enriches and filters to ICP: 200-1000 employees.
- Searches for decision-makers:
person_titles: ["VP of Sales", "VP Sales", "Head of Revenue Operations", "Director of Revenue Operations"], person_seniorities: ["VP", "Head", "Director"].
- 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
| Metric | Value |
|---|
| Competitors | Outreach, Salesloft, Apollo |
| Companies Identified | 89 (Outreach: 41, Salesloft: 33, Apollo: 28, overlap: 13) |
| Passed ICP Filter | 52 |
| Decision-Makers Found | 74 |
| Multi-Competitor Companies | 13 (highest priority) |
Multi-Competitor Accounts (Highest Priority)
| Company | Domain | Size | Competitors Detected | Evidence | Decision-Makers |
|---|
| RevOpsHub | revopshub.com | 450 | Outreach + Salesloft | Job posting mentions both tools; G2 review compares the two | 2 found |
| CloseFaster | closefaster.io | 310 | Salesloft + Apollo | StackShare lists Salesloft; recent job post mentions Apollo migration | 1 found |
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
| Problem | Solution |
|---|
| WebSearch returns noisy results for common competitor names | Use the full product name or add the product category to the query (e.g., "Monday.com" "project management" instead of "Monday"). Include the competitor's domain in queries when possible (e.g., site:outreach.io/customers). |
| Very few companies found across all vectors | Expand evidence types: 1) Try searching for the competitor's integration partners page. 2) Search for conference talks mentioning the competitor. 3) Try "powered by [competitor]" or "built on [competitor]" queries. 4) Ask the user if they know specific customers to use as seeds. |
| Company enrichment fails for extracted company names | Fallback chain: 1) Try inferring the domain from the company name (e.g., "Acme Corp" -> acme.com). 2) Run a WebSearch for the company name + "site:linkedin.com/company" to find the correct domain. 3) Try mcp__claude_ai_Amplemarket__enrich_company with the LinkedIn URL. 4) Skip unenrichable companies and note them separately. |
| No decision-makers found at enriched companies | Fallback chain: 1) Broaden seniority to include "Manager" and "Senior". 2) Try searching by company name instead of domain. 3) For companies with fewer than 100 employees, search for "Founder" and "C-Suite" roles. 4) Suggest the user try enrich_person with specific names if they have them from the evidence sources. |
| Evidence is outdated (old case studies, stale reviews) | Flag the date of each evidence source when presenting results. Prioritize evidence from the last 18 months. For older evidence, add a note: "Evidence from [year]. Company may have switched since then. Consider verifying before outreach." Deprioritize to Light evidence strength if older than 2 years. |
| Too many companies pass the ICP filter | Tighten filters progressively: 1) Narrow company size range. 2) Add geography constraints. 3) Restrict to Strong and Medium evidence only. 4) Limit to specific sub-industries. Show the user updated counts after each filter change. |
| G2/Capterra pages blocked by WebFetch | Try alternate review aggregation queries: "[competitor]" reviews OR "alternative to". Search for "switched from [competitor]" on Reddit or community forums. Some review content appears in Google snippets even when the full page is blocked. |
| Multiple competitors yield duplicate companies | This is a positive signal. Mark these companies as highest priority. Set {{competitor_name}} to list all detected competitors and use the multi-tool angle in {{competitor_suggested_opener}} (e.g., "I noticed your team has evaluated both Outreach and Salesloft. That tells me sales engagement tooling is top of mind"). |
