News Signal Outreach
The catch-all signal composite. Every other composite handles a specific signal type (funding, hiring, leadership change, champion move). This one handles everything else — any piece of news or public event that could create a reason to reach out.
A regulation change. A product recall. A competitor acquisition. A market expansion. A layoff. An earnings miss. A new partnership. An industry report. A conference keynote. A viral LinkedIn post. Any external event that shifts a company's priorities, creates urgency, or opens a window for your product.
Why this composite exists: The world generates an infinite stream of potential outreach triggers. The four structured signal composites handle the most common patterns. This composite handles the long tail — the unpredictable, opportunistic moments that often produce the best outreach because nobody else is sending a templated sequence about them.
When to Auto-Load
Load this composite when ANY of these are true:
- User shares ANY URL (LinkedIn post, article, tweet, blog, news) and asks about a company or person mentioned in it
- User says "came across", "saw this post", "found this article", "check this out", "is this relevant", "is this company a fit", "should we reach out"
- User mentions a company or person they discovered from an external source (social media, news, conference, podcast, newsletter) and asks about relevance or fit
- User asks "can we reach out to anyone based on this?"
- User says "check if this news is relevant to our prospects", "news-based outreach", "trigger-based outreach"
- User has a list of companies and wants to check recent news for outreach angles
- The news doesn't fit neatly into funding, hiring, leadership change, or champion move categories
- An upstream workflow surfaces a news item that needs evaluation
Key principle: If the user shares an external signal (URL, post, article, mention) and asks ANY question about the companies/people in it — load this composite. Don't wait for the word "outreach." The composite handles both evaluation-only (Steps 1-3) and full outreach (Steps 1-6).
Input Flexibility
This composite accepts three input modes:
| Mode | Input | Example |
|---|
| News → Companies | A news item. Extract companies/people mentioned, qualify them. | "Here's an article about new FDA regulations on telehealth" |
| Companies → News | A list of companies. Find recent news about them, evaluate relevance. | "Check these 50 companies for any news we can use as an outreach angle" |
| Person → News | A person or list of people. Find recent news about them or their company, evaluate relevance. | "Check if any of these prospects have been in the news" |
Step 0: Configuration (One-Time Setup)
On first run for a client/user, collect and store these preferences. Skip on subsequent runs.
ICP Definition
| Question | Purpose | Stored As |
|---|
| What does your company do? (1-2 sentences) | Relevance matching | company_description |
| What problem do you solve? | Connection angle identification | pain_point |
| What industries do you sell to? | ICP filter | target_industries |
| What company sizes? | ICP filter | target_company_size |
| What geographies? | ICP filter (optional) | target_geographies |
| Any disqualifiers? | Hard no's | disqualifiers |
| Who are your buyers? (titles) | Contact finding | buyer_titles |
| Who are your champions? (titles) | Contact finding | champion_titles |
| Who are your users? (titles) | Contact finding | user_titles |
Your Company Context
| Question | Purpose | Stored As |
|---|
| What specific outcomes does your product deliver? | Relevance angle building | product_outcomes |
| Name 2-3 proof points (customers, metrics) | Email credibility | proof_points |
| What categories of news are most relevant to your product? | Helps prioritize | relevant_news_categories |
Examples of relevant_news_categories:
# For a cybersecurity product:
relevant_news_categories: ["data breach", "compliance regulation", "security incident",
"digital transformation", "cloud migration", "IPO/going public"]
# For a sales AI product:
relevant_news_categories: ["sales team scaling", "market expansion", "new product launch",
"competitor acquisition", "cost cutting", "revenue miss"]
# For an HR tech product:
relevant_news_categories: ["layoffs", "rapid hiring", "remote work policy",
"DEI initiative", "union activity", "culture crisis"]
Signal Detection Config
| Question | Options | Stored As |
|---|
| How should we find news? | Web search / Google News / RSS feeds / Social media | news_tool |
| How far back should we look? (when scanning companies for news) | 7 / 14 / 30 / 60 days | lookback_days |
Contact Finding & Outreach Config
| Question | Options | Stored As |
|---|
| How should we find contacts? | Apollo / LinkedIn / Clearbit / Web search | contact_tool |
| Where do you want outreach sent? | Smartlead / Instantly / Outreach.io / CSV export | outreach_tool |
| Email or multi-channel? | Email only / Email + LinkedIn | outreach_channels |
Store config in: clients/<client-name>/config/signal-outreach.json or equivalent.
Step 1: Parse & Extract
Purpose: Take the raw news input — whatever form it arrives in — and extract structured entities (companies, people) and the core event.
Input Contract
Three modes:
Mode A: News → Companies/People
news_input: {
mode: "news_to_targets"
items: [
{
type: "url" | "text" | "structured"
content: string # URL to article, raw text, or structured summary
source: string | null # "TechCrunch", "LinkedIn post", "user provided", etc.
}
]
}
Mode B: Companies → News
news_input: {
mode: "targets_to_news"
companies: [
{
name: string
domain: string
industry?: string
}
]
lookback_days: integer
}
Mode C: People → News
news_input: {
mode: "people_to_news"
people: [
{
full_name: string
company: string
linkedin_url?: string
}
]
lookback_days: integer
}
Process
Mode A: News → Companies/People
-
Fetch and parse the news content:
- If URL → fetch the page, extract article text
- If raw text → use as-is
- If structured → use as-is
-
Extract entities:
- Companies mentioned (name, role in the story — subject, affected party, partner, competitor)
- People mentioned (name, title, company, role in the story)
- The core event (what happened, in one sentence)
- Event category (regulation, acquisition, partnership, product launch, market event, crisis, expansion, contraction, etc.)
- Date of event
- Affected industries
-
Expand if needed: If the news implies a broader set of affected companies beyond those mentioned:
- "New FDA regulation on telehealth" → all telehealth companies, not just ones in the article
- "Major data breach at [company]" → the breached company AND their competitors (who can capitalize)
- "Industry report shows X trend" → companies in that industry
Mode B: Companies → News
-
For each company, search for recent news using configured news_tool:
- Web search:
"{company_name}" AND (news OR announced OR launches OR raises OR expands OR partners) within lookback_days
- Filter results against
relevant_news_categories from config
- Extract the same fields as Mode A for each news item found
-
Group results: Company → list of news items, ranked by relevance to your product
Mode C: People → News
-
For each person, search for recent news/activity:
- Web search:
"{full_name}" AND "{company}" within lookback_days
- LinkedIn activity (if available): recent posts, shares, comments
- Look for: promotions, speaking engagements, published articles, quoted in press, new projects
-
Group results: Person → list of news items/activity
Output Contract
extracted_signals: [
{
entity: {
type: "company" | "person"
name: string
company: string # Company name (same as name if type=company)
domain: string | null
role_in_news: string # "subject", "affected", "partner", "competitor", "mentioned"
}
news: {
headline: string # One-line summary of what happened
event_category: string # "regulation", "acquisition", "expansion", "crisis", etc.
event_date: string
full_summary: string # 2-3 sentence summary
source_url: string | null
affected_industries: string[]
}
}
]
Human Checkpoint
## Extracted Signals
Source: [news source/input description]
Event: [one-line summary]
Category: [event category]
### Companies/People Extracted
| Entity | Type | Role in News | Industry |
|--------|------|-------------|----------|
| Acme Corp | Company | Subject | Healthcare |
| Jane Doe | Person | Quoted (CEO) | Healthcare |
| HealthTech sector | Industry | Affected | Healthcare |
Also evaluating: X companies in [affected industry] not directly mentioned
Proceed with ICP qualification? (Y/n)
Step 2: Qualify Against ICP
Purpose: For each extracted entity, determine if they're an ICP fit. Drop companies/people that don't match. Pure LLM reasoning — inherently tool-agnostic.
Input Contract
extracted_signals: [...] # From Step 1 output
icp_criteria: {
target_industries: string[]
target_company_size: string
target_geographies: string[]
disqualifiers: string[]
}
your_company: {
description: string
pain_point: string
}
Process
For each entity:
-
If entity is a company:
- Check industry against
target_industries
- Estimate company size (from news context or quick web search)
- Check geography if relevant
- Check against
disqualifiers
- Result: Pass / Fail with reasoning
-
If entity is a person:
- Identify their company
- Qualify the company through the same ICP checks above
- Additionally check: is this person's role relevant? (matches
buyer_titles, champion_titles, or user_titles)
- Result: Pass / Fail with reasoning
-
For entities implied but not mentioned (e.g., "all telehealth companies" from a regulation news):
- Use web search or existing company lists to identify specific companies in the affected space
- Qualify each against ICP
- This step may surface new companies not in your existing pipeline
Output Contract
icp_qualified: [
{
entity: { ... } # From Step 1
news: { ... } # From Step 1
icp_assessment: {
fit: "strong" | "moderate"
industry_match: boolean
size_match: boolean | "unknown"
reasoning: string # Why they're a fit
}
}
]
icp_disqualified: [
{
entity_name: string
reason: string
}
]
Human Checkpoint
## ICP Qualification
### Qualified (X entities)
| Entity | Type | Industry | Size | ICP Fit | Reasoning |
|--------|------|----------|------|---------|-----------|
| Acme Corp | Company | Healthcare SaaS | ~200 | Strong | Core ICP industry, right size |
| MedTech Inc | Company | HealthTech | ~500 | Moderate | Adjacent industry, large |
### Disqualified (X entities)
| Entity | Reason |
|--------|--------|
| BigPharma Co | Enterprise (50K+ employees) — above target size |
Approve qualified list?
Step 3: Identify Connection Angle
Purpose: This is the critical thinking step. For each ICP-qualified entity, determine the specific connection between the news event and your product. Why should they care about your product RIGHT NOW because of THIS news? Pure LLM reasoning — inherently tool-agnostic.
Input Contract
icp_qualified: [...] # From Step 2 output
your_company: {
description: string
pain_point: string
product_outcomes: string[]
proof_points: string[]
relevant_news_categories: string[]
}
Process
For each qualified entity, answer three questions:
Question 1: "Why does this news create urgency for our product?"
Map the news event category to a product relevance pattern:
| Event Category | How It Creates Urgency | Example |
|---|
| Regulation change | They need to comply, your product helps them comply or adapt faster | "New data privacy law → they need [your compliance tool] before enforcement date" |
| Acquisition / Merger | Systems need integration, processes need standardization, new leadership evaluates stack | "Acquired a company → need to unify [function your product handles]" |
| Market expansion | New market = new challenges, need tools that scale | "Expanding to EMEA → need [your product] for localized [function]" |
| Product launch | Scaling up means scaling operations | "Launching enterprise tier → need [your product] to handle enterprise [function]" |
| Competitive pressure | Competitor moved, they need to respond | "Competitor launched [X] → they need to level up [area your product addresses]" |
| Cost cutting / Layoffs | Do more with less, automation becomes essential | "Cut 15% of staff → need [your product] to maintain output with smaller team" |
| Crisis / Incident | Reactive buying — they need a solution NOW | "Data breach → urgently need [your security product]" |
| Partnership | New partner = new workflows, new opportunities | "Partnered with [company] → need [your product] to support the integration" |
| Earnings / Growth | Over-performing = scaling challenges. Under-performing = efficiency pressure | "Revenue grew 3x → [function your product handles] can't keep up manually" |
| Industry trend / Report | Category awareness is high, they're thinking about this | "Industry report says [trend] → they're likely evaluating solutions in this space" |
| Person-level news | Published an article, spoke at a conference, posted on LinkedIn about a topic you solve | "Posted about [pain] → they're actively thinking about this problem" |
Question 2: "What's the specific angle?"
Craft a one-sentence connection:
"Because [news event], [company] now needs [specific outcome your product delivers]."
Examples:
- "Because Acme just acquired BetaCo, they need to unify two separate CRM systems — exactly what [product] does in 30 days."
- "Because the new HIPAA amendment takes effect in Q3, [company] needs to audit their data handling — [product] automates this."
- "Because [person] just posted about struggling with [pain], they're actively looking for a solution — [product] solves this."
Question 3: "How strong is this connection?"
| Strength | Criteria | Example |
|---|
| Direct | The news explicitly describes a problem your product solves | Layoff in your product's department → they need automation |
| Adjacent | The news implies a downstream need your product addresses | Market expansion → implies scaling, which implies need for your tool |
| Thematic | The news is in the same category as your product's domain | Industry report about the trend you're in → awareness play |
Output Contract
connection_angles: [
{
entity: { ... }
news: { ... }
icp_assessment: { ... }
connection: {
urgency_reason: string # Why this news creates urgency
specific_angle: string # One-sentence connection
connection_strength: "direct" | "adjacent" | "thematic"
timing_note: string # How time-sensitive this outreach is
recommended_framework: string # Which email framework fits best
}
}
]
Framework Selection Based on Connection Strength
| Connection Strength | Recommended Framework | Why |
|---|
| Direct | Signal-Proof-Ask | The news IS the hook — reference it directly, show proof, ask |
| Adjacent | PAS | Problem (implied by the news) → Agitate (what happens if they don't act) → Solve |
| Thematic | AIDA | Attention (news reference) → Interest (how it relates to them) → Desire (your product) → Action |
Human Checkpoint
## Connection Angles
### Direct Connections (X entities) — Act quickly
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| Acme Corp | Acquired BetaCo | "Need to unify CRM systems — [product] does this in 30 days" | This week (integration planning starts immediately) |
### Adjacent Connections (X entities)
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| MedTech Inc | Expanding to EMEA | "Localized [function] becomes a requirement — [product] supports 15 languages" | This month |
### Thematic Connections (X entities)
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| HealthCo | Industry report on [trend] | "They're likely evaluating [category] solutions" | Flexible |
Approve these angles before we find contacts?
Step 4: Find Relevant People
Purpose: For each qualified entity with a connection angle, find the right people to contact.
Input Contract
connection_angles: [...] # From Step 3 output
buyer_titles: string[] # From config
champion_titles: string[] # From config
user_titles: string[] # From config
max_contacts_per_company: integer # Default: 3-5
Process
-
If the entity is already a person (Mode C or person mentioned in news):
- They're the primary contact. Still find 1-2 additional contacts at their company (buyer if they're a champion, champion if they're a buyer) for multi-threading.
-
If the entity is a company:
- Use configured
contact_tool to find people matching buyer_titles, champion_titles, user_titles
- Prioritize people whose role is closest to the news event:
| News Category | Prioritize These Contacts |
|---|
| Regulation / Compliance | Legal, Compliance, Operations leadership |
| Acquisition / Merger | COO, CTO, VP Operations, Integration leads |
| Market expansion | VP Sales, VP Marketing, Country/Regional leads |
| Cost cutting / Layoffs | COO, CFO, VP Operations |
| Product launch | CTO, VP Product, VP Engineering |
| Crisis / Incident | CISO, VP Engineering, CTO (for security), CEO/COO (for operational) |
| General growth | Default to buyer_titles from config |
-
For each contact, note their relevance to the news:
- Are they directly affected by the news? (Their department, their function)
- Are they the decision-maker for the response to this news?
- Are they the person who will feel the pain this news creates?
Output Contract
contacts: [
{
person: {
full_name: string
first_name: string
last_name: string
title: string
email: string | null
linkedin_url: string | null
role_type: "buyer" | "champion" | "user"
news_relevance: string # Why THIS person for THIS news
}
company: {
name: string
domain: string
}
connection: {
specific_angle: string
connection_strength: string
urgency_reason: string
}
news: {
headline: string
event_category: string
source_url: string | null
}
}
]
Human Checkpoint
## Contacts Found
### Acme Corp — "Acquired BetaCo" (Direct connection)
| Name | Title | Role | Why This Person |
|------|-------|------|----------------|
| Sarah Kim | COO | Buyer | Owns post-acquisition integration |
| David Park | VP Operations | Champion | Will manage unified workflows |
| Amy Chen | Director of Sales Ops | User | Directly affected by CRM unification |
### MedTech Inc — "Expanding to EMEA" (Adjacent connection)
| ... |
Total: X contacts across Y companies
Approve before we draft emails?
Step 5: Draft Personalized Outreach
Purpose: Draft outreach where the news event is the hook, your product is the solution, and the email demonstrates you understand their specific situation. Pure LLM reasoning — inherently tool-agnostic.
Input Contract
contacts: [...] # From Step 4 output
your_company: {
description: string
pain_point: string
product_outcomes: string[]
proof_points: string[]
}
sequence_config: {
touches: integer # Default: 3
timing: integer[] # Default varies by connection strength (see below)
tone: string # Default: "casual-direct"
cta: string # Default: "15-min call"
}
Process
-
Adjust sequence timing by connection strength:
| Strength | Timing | Rationale |
|---|
| Direct | Day 1 / 3 / 7 | Urgency is real — they're actively dealing with this |
| Adjacent | Day 1 / 5 / 12 | Standard timing — urgency is implied, not immediate |
| Thematic | Day 1 / 7 / 14 | Slower cadence — this is awareness, not crisis response |
-
Build the email around the news, not the product:
The news is the subject. Your product is the punchline. Never lead with the product.
| Element | Source | How to Use |
|---|
| News hook | Step 1 news.headline | Open with what happened — show you're informed |
| Impact on them | Step 3 connection.urgency_reason | Explain what this means for their specific role |
| Your angle | Step 3 connection.specific_angle | Connect the dots to your product naturally |
| Proof | Config proof_points | Show a peer who faced a similar situation |
| CTA | Config | Low-friction ask |
-
Email structure by connection strength:
Direct connection (Signal-Proof-Ask):
Hook: Reference the specific news event
Impact: What this means for them (1 sentence)
Proof: A peer who faced the same situation and used your product
Ask: Soft CTA
Adjacent connection (PAS):
Problem: The downstream challenge the news creates
Agitate: What happens if they don't address it (1 sentence)
Solve: How your product helps, with a proof point
Ask: Soft CTA
Thematic connection (AIDA):
Attention: Reference the news/trend
Interest: How it relates to their company specifically
Desire: What your product does in this context
Action: Soft CTA
-
Personalization layers:
| Layer | What Gets Personalized | Source |
|---|
| News reference | The specific event and its relevance | Step 1 news data |
| Company context | What their company does, their industry, their situation | Step 2 ICP research |
| Role context | Why THIS person cares about this news | Step 4 news_relevance |
| Your company fit | How your product specifically helps in this scenario | Step 3 connection angle |
-
Follow email-drafting skill hard rules. Additionally:
- Never sensationalize negative news. If the signal is a layoff, breach, or crisis, be empathetic, not opportunistic. "I know this is a challenging time" not "Your layoffs mean you need our tool!"
- Don't pretend you just happened to see the news. Be direct: "Saw the news about [event]" not "I came across an interesting article."
- If the news is about a crisis, wait 48-72 hours before reaching out. Immediate outreach during a crisis looks predatory.
Output Contract
email_sequences: [
{
contact: { full_name, email, title, company_name, role_type, news_relevance }
news_context: { headline, event_category, source_url }
connection: { specific_angle, connection_strength }
sequence: [
{
touch_number: integer
send_day: integer
subject: string
body: string
framework: string
personalization_elements: {
news_reference: string # How the news was referenced
company_context: string # How their company situation was used
role_context: string # How their specific role was leveraged
product_connection: string # How the product was positioned
}
word_count: integer
}
]
}
]
Human Checkpoint
Present samples grouped by connection strength:
## Sample Outreach for Review
### Direct Connection: Sarah Kim, COO @ Acme Corp
News: Acme acquired BetaCo | Angle: CRM unification | Framework: Signal-Proof-Ask
**Touch 1 — Day 1**
Subject: Unifying Acme + BetaCo systems
> Hi Sarah — saw the BetaCo acquisition. Congrats. The integration
> sprint typically surfaces a CRM unification challenge fast —
> two systems, overlapping data, different workflows.
>
> [Peer company] faced the same thing after their acquisition last year.
> [Product] had both systems unified in 30 days. Happy to share how.
>
> Worth a 15-minute call?
**Touch 2 — Day 3**
> [New angle — data migration complexity, with a specific metric]
**Touch 3 — Day 7**
> [Breakup with offer to share the integration playbook]
---
### Adjacent Connection: Dr. Lee, VP Product @ MedTech Inc
News: EMEA expansion | Angle: Localization needs | Framework: PAS
**Touch 1 — Day 1**
Subject: EMEA expansion + [function] localization
> [full email]
---
Approve these samples? I'll generate the rest in the same style.
Step 6: Handoff to Outreach
Identical to the other signal composites. Package contacts + email sequences for the configured outreach tool.
Output Contract
campaign_package: {
tool: string
file_path: string
contact_count: integer
sequence_touches: integer
estimated_send_days: integer
next_action: string
}
Human Checkpoint
## Campaign Ready
Tool: [configured tool]
Signal type: News-triggered
News event: [headline]
Connection strengths: X direct, Y adjacent, Z thematic
Contacts: N people across M companies
Sequence: 3 touches (timing varies by connection strength)
Ready to launch?
Execution Summary
| Step | Tool Dependency | Human Checkpoint | Typical Time |
|---|
| 0. Config | None | First run only | 5 min (once) |
| 1. Parse & Extract | Web fetch (for URLs) or none (for text) | Review extracted entities | 2-3 min |
| 2. Qualify ICP | Web search (for company research) | Approve qualified list | 2-3 min |
| 3. Connection Angle | None (LLM reasoning) | Approve angles + strength ratings | 3-5 min |
| 4. Find People | Configurable (Apollo, LinkedIn, etc.) | Approve contact list | 2-3 min |
| 5. Draft Emails | None (LLM reasoning) | Review samples, iterate | 5-10 min |
| 6. Handoff | Configurable (Smartlead, CSV, etc.) | Final launch approval | 1 min |
Total human review time: ~15-25 minutes
Key Difference from Other Signal Composites
| Dimension | Structured Signals (Funding, Hiring, etc.) | News Signal |
|---|
| Signal type | Predefined, narrow | Arbitrary, broad — anything can be a trigger |
| Detection | Targeted search (job boards, funding databases) | Open-ended (any news source) |
| Extra step | — | Step 3: Connection Angle identification. Other composites have obvious connections (funding = money to spend). News requires explicit reasoning about WHY this event matters for your product. |
| Input modes | Companies in → signals out | Three modes: News→Companies, Companies→News, People→News |
| Timing | Predictable windows (post-raise, pre-hire) | Varies wildly by event type — crisis = 48hr delay, trend = flexible |
| Sensitivity | Generally positive (funding, hiring, growth) | Can be negative (layoffs, crises, failures). Requires empathy calibration. |
Sensitivity Guidelines
Some news events require careful tone calibration:
| Event Type | Tone | What NOT to Do |
|---|
| Layoffs | Empathetic. "I know this is a tough time." | Don't say "your layoffs mean you need us!" |
| Data breach / Security incident | Helpful, not salesy. "If you need help with [specific thing]." | Don't pile on or blame. Don't reach out same-day. |
| Earnings miss / Revenue decline | Efficiency-focused. "Do more with what you have." | Don't reference the miss directly in the subject line. |
| Executive departure / Fired CEO | Skip the drama entirely. Focus on the new leader or the company's direction. | Don't mention the departure unless it's public and amicable. |
| Lawsuit / Legal trouble | Generally avoid unless your product directly helps with compliance/legal. | Don't reference the lawsuit. It looks ambulance-chasey. |
| Product failure / Recall | Only reach out if you have a direct solution. | Don't gloat or compare. |
Rule of thumb: If you wouldn't bring it up in a face-to-face conversation at a conference, don't put it in a cold email.
Tips
- Direct connections are rare but powerful. Most news creates adjacent or thematic connections. When you find a direct one, prioritize it — these convert at 2-3x the rate.
- Speed matters for direct connections. The first vendor to reference a relevant news event looks informed. The fifth looks like they're running the same playbook.
- Don't force weak connections. If you can't articulate the angle in one sentence, the connection is too weak. Drop it.
- News about competitors is gold. If a competitor raises funding, gets acquired, has a security breach, or launches a product — their customers and prospects are suddenly open to conversations.
- Negative news requires a 48-72 hour cooling period. Reaching out the day of a layoff or breach is predatory. Wait, then lead with empathy.
- Industry reports and trend pieces make great thematic triggers. "The Gartner report on [category] just dropped — here's what it means for [company]" positions you as thoughtful, not reactive.
- Combine with other signal composites. News often contains embedded signals: an acquisition article mentions the acquiring company is hiring 50 people (hiring signal), a new CEO is named (leadership change signal), or the company just raised funding (funding signal). Route these to the appropriate specialist composite for better outreach.
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