conference-event-lead-finder

name	conference-event-lead-finder
description	Scrape conference and event websites for speakers, sponsors, and exhibitors, enrich them via Amplemarket, filter against your ICP, and find decision-makers at those companies.
metadata	{"author":"amplemarket","version":"1.0.4","category":"Prospecting & Lead Generation"}
compatibility	Requires Amplemarket MCP server

Conference & Event Lead Finder

Scrape conference and event websites for speakers, sponsors, and exhibitors, enrich them via Amplemarket, filter against your ICP, and find decision-makers at those companies.

Instructions

When a user wants to find leads from a specific conference, event, or trade show, extract attendee and company data from the event website and enrich it into actionable prospect lists.

Steps

1. Gather event details from the user's request. Extract or ask for:

   • Event name and/or URL (e.g., "SaaStr Annual 2026", "https://saas-conference.example.com")
   • What to find: speakers, sponsors, exhibitors, attendees, or all
   • Target roles at discovered companies (e.g., "VP of Sales", "CTO", "Head of Marketing")
   • Specific sessions or tracks of interest (optional, e.g., "AI track", "Growth stage talks")
   • ICP filters (optional, company size, industry, geography)

   If the user provides only an event name without a URL, proceed to step 2 to find the event website.

2. Find the event website by calling WebSearch with the event name plus keywords like "speakers", "sponsors", or "agenda". Target queries:

   • "[Event Name] speakers 2026"
   • "[Event Name] sponsors exhibitors"
   • "[Event Name] agenda schedule"

   Identify the official event website and the specific pages for speakers, sponsors, and agenda.

3. Extract speaker and sponsor data by calling WebFetch on the identified pages:

   For speakers:

   • Fetch the speakers page URL
   • Extract: speaker name, title, company, talk title, session/track, bio snippet
   • If speakers are spread across multiple pages (pagination or separate session pages), fetch each page

   For sponsors:

   • Fetch the sponsors or partners page URL
   • Extract: company name, sponsorship tier (Platinum/Gold/Silver/Bronze), logo, website URL
   • Note the tier level as it indicates budget and commitment level

   For exhibitors:

   • Fetch the exhibitors or expo hall page URL
   • Extract: company name, booth number (if listed), website URL, description

4. Enrich discovered companies by calling mcp__claude_ai_Amplemarket__enrich_company with the domain for each unique company identified from speakers, sponsors, and exhibitors. Key data points to extract:

   • Industry and sub-industry
   • Company size and employee count
   • Headquarters location
   • Tech stack
   • Funding stage and recent rounds
   • Company description

   Batch efficiently. Deduplicate companies that appear in multiple roles (e.g., a company that is both a sponsor and has a speaker).

5. Filter companies against the user's ICP. If the user specified ICP criteria (company size, industry, geography), filter the enriched companies. If no ICP was specified, present all companies and ask: "Want me to filter these by company size, industry, or geography?"

6. Find decision-makers at matching companies by calling mcp__claude_ai_Amplemarket__search_people with:

   • company_domains: [matched company domain]
   • person_titles or person_seniorities: based on the user's target roles
   • full_output: true
   • page_size: 5

   For each matching company, find the contacts most relevant to the user's outreach goals.

7. Enrich speakers for deeper context. For speakers specifically, gather additional personalization data:

   • Use WebSearch to find their talk title, abstract, and bio if not already extracted
   • Call mcp__claude_ai_Amplemarket__enrich_person with name and company_name (or linkedin_url if found) to get career history, LinkedIn URL, and contact details. Set reveal_email to true if the user wants contact information. Credit note: Enabling reveal_email and reveal_phone_numbers consumes additional Amplemarket credits per reveal.
   • Note the talk topic as a personalization hook for outreach

8. Present results organized by conference role (speakers, sponsors, exhibitors) with the following dynamic fields available for outreach personalization:

   • {{conference_name}} - Name of the conference or event
   • {{conference_date}} - Date or date range of the event
   • {{conference_role}} - The lead's role at the event (Speaker, Sponsor, Exhibitor, Attendee)
   • {{conference_talk_title}} - Title of the speaker's talk or panel
   • {{conference_talk_topic}} - Topic area or track of the session
   • {{conference_company_name}} - Company name as listed at the event
   • {{conference_sponsorship_tier}} - Sponsor level (Platinum, Gold, Silver, Bronze)
   • {{conference_bio_highlight}} - Key highlight from the speaker's bio or abstract
   • {{conference_shared_session}} - Session or track the user and lead both attended
   • {{conference_suggested_opener}} - AI-generated opening line referencing the event context
   • {{conference_speaker_linkedin}} - Speaker's LinkedIn profile URL

   Format the output as:

   Event Summary

   • Event name, date, location
   • Total speakers, sponsors, and exhibitors found
   • Companies matching ICP

   Speakers (table with: Name, Title, Company, Talk Title, Topic/Track, LinkedIn)

   Sponsors (table with: Company, Tier, Domain, Industry, Size, ICP Match)

   Exhibitors (table with: Company, Domain, Industry, Size, ICP Match)

   Decision-Makers Found (table with: Name, Title, Company, Conference Role, Email, LinkedIn)

   Personalization Hooks - For each top lead, a suggested opener referencing their conference participation.

9. Offer to create a lead list with event context. If the user wants to proceed:

   • Call mcp__claude_ai_Amplemarket__create_lead_list with a name like "[Event Name] - [Role] Leads"
   • Call mcp__claude_ai_Amplemarket__add_leads_to_lead_list with the discovered leads
   • Confirm the list was created and provide the count of leads added

Important Notes

• WebFetch may be blocked by some event websites that use JavaScript-heavy rendering or anti-scraping protections. If WebFetch returns empty or blocked content, ask the user to paste the speaker/sponsor list directly and proceed from step 4.
• Deduplicate across speakers, sponsors, and exhibitors before enriching to avoid redundant API calls.
• Speaker personalization is the highest-value output. A reference to someone's specific talk topic is a strong outreach hook.
• Sponsorship tier correlates with budget: Platinum/Diamond sponsors are investing heavily and may be more receptive to vendor conversations.
• For large conferences with 100+ speakers, ask the user to narrow by track or session topic before enriching all companies.
• Never fabricate speaker names, talk titles, or company associations. Only use data extracted from the event website or returned by enrichment APIs.

Dynamic Fields Generated

The following dynamic fields are populated during the workflow and can be used in outreach templates:

Field	Description	Source
{{conference_name}}	Name of the conference or event	User input / WebFetch
{{conference_date}}	Date or date range of the event	WebFetch from event page
{{conference_role}}	Lead's role at the event (Speaker, Sponsor, Exhibitor)	WebFetch extraction
{{conference_talk_title}}	Title of the speaker's presentation or panel	WebFetch from agenda/speaker page
{{conference_talk_topic}}	Topic area, track, or theme of the session	WebFetch from agenda page
{{conference_company_name}}	Company name as listed at the event	WebFetch extraction
{{conference_sponsorship_tier}}	Sponsor level (Platinum, Gold, Silver, Bronze)	WebFetch from sponsor page
{{conference_bio_highlight}}	Key highlight from the speaker's bio or talk abstract	WebFetch + WebSearch
{{conference_shared_session}}	Session or track the user and lead both attended	User input + agenda matching
{{conference_suggested_opener}}	AI-generated opening line referencing event context	Synthesized from all fields
{{conference_speaker_linkedin}}	Speaker's LinkedIn profile URL	enrich_person result

Examples

Example 1: SaaS Conference Speaker List

User prompt: "Find leads from SaaStr Annual 2026. I want to reach out to speakers who are VP+ at companies with 200-2000 employees"

What the skill does:

1. Calls WebSearch with "SaaStr Annual 2026 speakers list" and "SaaStr Annual 2026 agenda schedule".
2. Calls WebFetch on the SaaStr speakers page to extract speaker names, titles, companies, and talk titles.
3. Calls mcp__claude_ai_Amplemarket__enrich_company for each unique company (e.g., domain: "gong.io", domain: "salesloft.com").
4. Filters companies to 200-2000 employees.
5. Calls mcp__claude_ai_Amplemarket__search_people at matching companies with person_seniorities: ["VP", "Head", "Director", "C-Suite"].
6. Calls mcp__claude_ai_Amplemarket__enrich_person for each speaker with reveal_email: true.
7. Compiles results with personalization hooks.

Example output:

---

EVENT LEAD REPORT: SaaStr Annual 2026

Event Summary

Field	Detail
Event	SaaStr Annual 2026
Date	September 15-17, 2026
Location	San Mateo, CA
Speakers Found	87
Unique Companies	72
ICP Matches (200-2000 employees)	28

Speakers at ICP-Matching Companies

Name	Title	Company	Talk Title	Track	LinkedIn
Maria Santos	VP of Revenue Ops	CloudMetrics	"Scaling RevOps from $10M to $100M ARR"	Growth Stage	linkedin.com/in/msantos
James Liu	CRO	DataSync	"Building a Predictable Pipeline Machine"	Sales Leadership	linkedin.com/in/jliu
Priya Sharma	VP of Customer Success	FlowStack	"Net Revenue Retention as a Growth Engine"	CS & Retention	linkedin.com/in/psharma
...	...	...	...	...	...

Additional Decision-Makers at Speaker Companies

Name	Title	Company	Conference Role	Email	LinkedIn
Tom Rodriguez	Head of Partnerships	CloudMetrics	Speaker's Company	[e-mail]	linkedin.com/in/trodriguez
Sarah Kim	VP of Engineering	DataSync	Speaker's Company	[e-mail]	linkedin.com/in/skim
...	...	...	...	...	...

Personalization Hooks

Maria Santos (CloudMetrics):

• {{conference_suggested_opener}}: "Maria, your SaaStr talk on scaling RevOps from $10M to $100M really resonated. We have been seeing similar patterns with our customers around the ops bottlenecks that emerge at that growth stage."
• {{conference_talk_topic}}: Revenue Operations, Scaling
• {{conference_bio_highlight}}: Scaled CloudMetrics RevOps team from 2 to 18 in 18 months

James Liu (DataSync):

• {{conference_suggested_opener}}: "James, caught the description of your 'Predictable Pipeline Machine' session at SaaStr. Curious whether the framework you outline accounts for the signal-to-noise problem most teams hit at scale."
• {{conference_talk_topic}}: Pipeline Generation, Sales Leadership
• {{conference_bio_highlight}}: Former VP Sales at Outreach, grew ARR 4x in 2 years

Shall I create a lead list called "SaaStr Annual 2026 - Speaker Leads"?

---

Example 2: Sponsor Logo Extraction

User prompt: "Get me the sponsors from AWS re:Invent 2026. I want to find decision-makers at Gold and Platinum sponsors"

What the skill does:

1. Calls WebSearch with "AWS re:Invent 2026 sponsors partners page".
2. Calls WebFetch on the sponsors page to extract company names and sponsorship tiers.
3. Filters to Gold and Platinum tier sponsors only.
4. Calls mcp__claude_ai_Amplemarket__enrich_company for each sponsor domain.
5. Calls mcp__claude_ai_Amplemarket__search_people at each sponsor company with person_seniorities: ["VP", "Head", "Director", "C-Suite"], page_size: 5.
6. Presents results organized by sponsorship tier.

Example output:

---

EVENT LEAD REPORT: AWS re:Invent 2026 - Sponsors

Sponsors by Tier

Platinum Sponsors

Company	Domain	Industry	Size	HQ
Datadog	datadog.com	Cloud Monitoring	5001-10000	New York, NY
HashiCorp	hashicorp.com	Infrastructure Software	2001-5000	San Francisco, CA
Snowflake	snowflake.com	Data Cloud	5001-10000	Bozeman, MT

Gold Sponsors

Company	Domain	Industry	Size	HQ
LaunchDarkly	launchdarkly.com	Feature Management	501-1000	Oakland, CA
Fivetran	fivetran.com	Data Integration	1001-2000	Oakland, CA
...	...	...	...	...

Decision-Makers at Sponsor Companies

Name	Title	Company	Tier	LinkedIn
Jordan Lee	VP of Engineering	Datadog	Platinum	linkedin.com/in/jlee
Anita Patel	Head of Alliances	HashiCorp	Platinum	linkedin.com/in/apatel
...	...	...	...	...

{{conference_suggested_opener}}: "Anita, saw that HashiCorp is a Platinum sponsor at re:Invent this year. Given your investment in the AWS ecosystem, I'd love to discuss how we complement that strategy."

---

Example 3: Specific Track or Session Leads

User prompt: "Find people speaking on the AI/ML track at Web Summit 2026. We sell MLOps tools and want to reach out to engineering leaders"

What the skill does:

1. Calls WebSearch with "Web Summit 2026 AI ML track speakers agenda".
2. Calls WebFetch on the agenda page, filtering for AI/ML track sessions.
3. Extracts speakers from AI/ML sessions only: names, titles, companies, talk titles.
4. Calls mcp__claude_ai_Amplemarket__enrich_company for each speaker's company.
5. Filters for companies where MLOps tooling is relevant (tech companies, 100+ employees, engineering-heavy orgs).
6. Calls mcp__claude_ai_Amplemarket__search_people at matching companies with person_titles: ["VP of Engineering", "Head of ML", "Director of Data Science", "CTO", "Head of AI"], full_output: true.
7. Calls mcp__claude_ai_Amplemarket__enrich_person for each speaker to get LinkedIn and email.
8. Presents results with MLOps-specific personalization hooks.

Example output:

---

EVENT LEAD REPORT: Web Summit 2026 - AI/ML Track

Event Summary

Field	Detail
Event	Web Summit 2026
Track	AI/ML & Data Science
Sessions in Track	24
Speakers Found	31
ICP Matches (tech, 100+ employees)	18

AI/ML Track Speakers

Name	Title	Company	Talk Title	LinkedIn
Dr. Lin Wei	VP of AI	ScaleML	"Production ML: From Notebook to Pipeline"	linkedin.com/in/linwei
Marcus Johnson	Head of Data Science	FinanceAI	"Building Responsible AI at Scale"	linkedin.com/in/mjohnson
Elena Kovar	CTO	DeepOps	"The MLOps Stack of 2027"	linkedin.com/in/ekovar
...	...	...	...	...

Engineering Leaders at Speaker Companies

Name	Title	Company	Email	LinkedIn
Raj Patel	Director of Platform Eng	ScaleML	[e-mail]	linkedin.com/in/rpatel
...	...	...	...	...

Personalization Hooks

Dr. Lin Wei (ScaleML):

• {{conference_suggested_opener}}: "Dr. Wei, your Web Summit talk on moving from notebook to production pipeline is exactly the problem we are solving at [Company]. Would love to compare notes on the infrastructure gaps you highlighted."
• {{conference_talk_topic}}: MLOps, Production ML
• {{conference_bio_highlight}}: Led ML infrastructure at Google Brain before joining ScaleML

Shall I create a lead list called "Web Summit 2026 - AI/ML Track Leads"?

---

Troubleshooting

Problem	Solution
WebFetch returns empty or blocked content	Many event websites use JavaScript rendering or anti-bot protections that prevent scraping. Fallback: Ask the user to paste the speaker list, sponsor list, or agenda content directly into the chat. Then proceed from step 4 (enrichment) using the pasted data. You can also try fetching a cached version via WebSearch with "cache:[URL]".
Event website not found via WebSearch	Fallback chain: 1) Try alternate search queries. Include the year, city, or organizer name. 2) Search for "[Event Name] speakers list" on LinkedIn or Twitter. 3) Ask the user for the direct URL: "I could not find the official website for [Event]. Can you share the URL or paste the speaker/sponsor list?"
No ICP matches among conference companies	Expand ICP filters: 1) Broaden company size range. 2) Include adjacent industries. 3) Remove geography restrictions. If still no matches, present all discovered companies and let the user decide which to target.
Speaker page is paginated or split across multiple pages	Fetch each page sequentially. Look for URL patterns like /speakers?page=2 or /speakers/day-1, /speakers/day-2. If pagination is JavaScript-driven and WebFetch cannot access subsequent pages, ask the user to share the full list.
Too many speakers or sponsors (100+)	Ask the user to narrow the scope: "This event has 150+ speakers. Want me to focus on a specific track (e.g., AI/ML, Growth, Sales) or filter by seniority (VP+ only)?" Process in batches of 20-30 companies to manage API usage.
Speaker enrichment returns wrong person	Disambiguate by using both name and company_name in mcp__claude_ai_Amplemarket__enrich_person. If still ambiguous, use the speaker's LinkedIn URL if found on the event page. Confirm with the user before including in the lead list.
Sponsor tier information not available	Some event pages list sponsors without explicit tiers. Infer tier from logo size or page placement (larger logos at top = higher tier). If uncertain, label all sponsors as "Sponsor" without a tier and note: "Tier information was not available on the event page."
Company enrichment succeeds but search_people finds no decision-makers	Fallback chain: 1) Broaden seniority to include "Manager" and "Senior". 2) Try searching by company name instead of domain. 3) For small companies (<50 employees), search for "Founder" and "C-Suite" only. 4) Use mcp__claude_ai_Amplemarket__enrich_person with the speaker's name and company as a direct fallback.