What you'll build: A multi-signal prospecting system that monitors hiring, funding, and leadership changes at your target accounts – then automatically qualifies signals, finds contacts, and drafts outreach. One pipeline that catches every buying trigger.
Time: 2-3 days to build manually | 60-90 minutes with AI
What you need: A target account list (50-200 companies), your ICP definition, clarity on what roles your product replaces or augments
Why This Matters
Most outbound prospecting is spray-and-pray: buy a list, write a template, blast 500 people, hope for 2% reply rate. Signal-based prospecting flips this entirely.
Instead of reaching out to everyone and hoping someone cares, you only reach out when there's evidence the company needs what you sell right now.
The three strongest buying signals are: hiring (they're budgeting for the role your product replaces), funding (they have cash and need to scale fast), and leadership changes (the new exec is evaluating their stack).
Each signal alone gets 3-5x the reply rate of cold outbound. Stack two or three signals together and you're operating in a completely different tier.
The problem: building this system manually means checking job boards, scanning Crunchbase, monitoring LinkedIn, and cross-referencing everything in a spreadsheet. Nobody sustains that. The system either needs to be automated or it dies after week two.
How to Do It with AI
This is a multi-tool workflow. We'll use Claude Code for building the data pipeline and monitoring scripts, and ChatGPT for designing the qualification framework and outreach strategy. The combination works because Claude Code excels at data processing while ChatGPT excels at strategic reasoning.
Step 1: Design Your Signal Framework
Start with ChatGPT or Claude (chat mode) to build the strategic layer — what signals matter and how they stack:
I sell [product description] to [ICP].
Help me design a signal-based prospecting framework:
1. HIRING SIGNALS: What job postings indicate a company needs my product?
- Roles my product REPLACES (strongest)
- Roles my product AUGMENTS (good)
- JD keywords that indicate relevance
2. FUNDING SIGNALS: What funding events trigger buying?
- Which stages matter most (Seed? Series B? Series C+?)
- How recent does the funding need to be? (30 days? 90 days?)
3. LEADERSHIP SIGNALS: What exec changes create a buying window?
- Which titles are buyers for my category?
- What types of changes matter? (new hire, promotion, role expansion)
4. COMPOUND SIGNALS: How do these stack?
- What combinations are strongest? (e.g., funding + hiring = highest priority)
The AI will help you build a signal taxonomy. Here's what a real one looks like:
Signal Type | Strength Alone | Stacked With | Combined Strength
Signal Type: Hiring for role you replace | Strength Alone: Strong | Stacked With: Recent funding | Combined Strength: Strongest — budget + acknowledged need
Signal Type: Recent funding (60 days) | Strength Alone: Medium | Stacked With: New CRO/VP Sales | Combined Strength: Very strong — cash + new decision-maker
Signal Type: New exec in buyer role | Strength Alone: Strong | Stacked With: Hiring for roles you replace | Combined Strength: Strongest — new leader actively building
Signal Type: Funding alone (90+ days) | Strength Alone: Weak | Stacked With: Nothing else | Combined Strength: Queue — signal is cooling
Step 2: Build the Signal Detection Pipeline
Now switch to Claude Code to build the actual data pipeline. You'll create three parallel monitors:
Help me build a signal detection pipeline for my 50 target companies.
I need three monitors running in parallel:
MONITOR 1 — HIRING SIGNALS:
- Scan LinkedIn Jobs, Indeed, and company careers pages
- Match against my role mapping: [paste roles_replaced and roles_augmented]
- Extract: company, role, comp, posted date, JD summary, signal type
MONITOR 2 — FUNDING SIGNALS:
- Check Crunchbase, PitchBook, press releases, Google News
- Look for funding events within the past 90 days
- Extract: company, amount, stage, date, lead investors
MONITOR 3 — LEADERSHIP CHANGES:
- Monitor LinkedIn profile updates, press releases, SEC filings
- Look for VP/Director/C-level changes in GTM roles
- Extract: company, person, old role, new role, date, context
Output everything as structured JSON so we can cross-reference.
Claude Code will build scripts for each monitor. From a real run on 12 companies:
- Hiring monitor: 11 of 12 companies had active signals (12 "replaces" postings across 9 companies)
- Funding monitor: 7 of 12 had funding within 6 months ($60M to $735M)
- Leadership monitor: 1 change detected in a 15-day window (Amplitude CRO → CCO)
Step 3: Cross-Reference and Score
This is where the system gets powerful. Have Claude Code merge all three signal feeds and score each company:
Cross-reference the three signal feeds and score each company.
Scoring rules:
- 3 points: Hiring for a role we replace
- 2 points: Funding within 60 days
- 2 points: New exec in buyer role (within 30 days)
- 1 point: Hiring for a role we augment
- 1 point: Funding 60-180 days ago
- 1 point: Leadership change (30-90 days ago)
Priority tiers:
- Tier 1 (5+ points): Act today — multiple strong signals
- Tier 2 (3-4 points): Act this week — clear buying trigger
- Tier 3 (1-2 points): Queue — worth monitoring but not urgent
- Tier 0 (0 points): No current signals — check back next cycle
Real output from a 12-company scan:
Tier 1 (Act Today): Encord — 6 points: Series C ($60M, Feb 2026) + hiring SDR + hiring BDR. Post-raise, building outbound from scratch. Replit — 5 points: New round (~$400M, Jan 2026) + hiring SDR (JD mentions Clay, SmartLead). Budget + acknowledged need.
Tier 2 (Act This Week): Pave — 4 points: Hiring SDR ($95K) + hiring Jr. GTM Engineer ($106-125K). No funding signal but multiple replacement roles. Lovable — 4 points: Series B ($330M, Dec 2025) + hiring Demand Dev Manager. Growth phase. Amplitude — 4 points: CRO promoted to CCO (Feb 2026) + hiring SDR. New mandate + building team.
Step 4: Find Contacts and Draft Outreach
For Tier 1 and Tier 2 companies, use Claude Code to find contacts and draft outreach using the strongest signal as the hook:
For each Tier 1 and Tier 2 company:
1. Find 2-3 contacts using Apollo or LinkedIn:
- The hiring manager or new exec (primary)
- A VP/Director-level buyer (secondary)
2. Draft outreach using the STRONGEST signal as the hook:
- If hiring + funding: Lead with the hiring signal (more specific)
- If new exec + hiring: Lead with the leadership change (more timely)
- If funding only: Lead with the post-raise scaling challenge
3. Note in the email if multiple signals exist — stacking signals
increases credibility ("I saw you just raised AND are hiring...")
Framework: Signal-Proof-Ask for "replaces" signals, BAB for "augments"
Word limit: 50-90 words per email
A real stacked-signal email:
Subject: Post-raise + the SDR hire at [Company]
Hi [Name] — congrats on the Series C. I noticed you're also hiring an SDR and a BDR — building outbound from scratch post-raise is a lot of operational ramp-up.
[Product] handles that outbound execution autonomously — prospecting, enrichment, sequencing, pipeline management. It's like having your outbound engine running while you hire and ramp the team.
Worth 15 minutes to see how it maps?
This email is stronger than a funding-only or hiring-only email because it shows you've done your research across multiple signals.
Step 5: Automate the Cycle
The one-time scan is valuable, but the real leverage comes from running this weekly on your full target list:
Set up a weekly signal detection cycle:
MONDAY 8 AM:
1. Run all three monitors across the full target list
2. Cross-reference and score new signals
3. Merge with previous week's data (decay old signals by 1 point per week)
4. Generate a prioritized outreach list
MONDAY 9 AM:
5. For any new Tier 1 signals: auto-draft outreach emails
6. For Tier 2: add to outreach queue with suggested angles
7. Send summary to Slack: "This week's signals: X new hiring, Y funding, Z leadership"
FRIDAY:
8. Check for new signals mid-week (leadership changes move fast)
9. Update scores
After 4-6 weeks of running, the system builds compound intelligence — you start seeing patterns like "Company X has been hiring aggressively for 3 weeks AND just had their VP Sales leave."
These compound signals are impossible to detect manually but trivial for an automated system.
What You Get
A complete signal-based prospecting system that:
Component | Manual Effort | With AI
Component: Weekly signal scan (50 companies) | Manual Effort: 8-12 hours | With AI: 15 minutes (automated)
Component: Cross-referencing signals | Manual Effort: 2-3 hours in spreadsheets | With AI: Instant (scripted)
Component: Contact finding | Manual Effort: 1-2 hours | With AI: 10 minutes
Component: Email drafting | Manual Effort: 2-3 hours | With AI: 15 minutes
Component: Total weekly effort | Manual Effort: 13-20 hours | With AI: 40-60 minutes
And the quality is higher because you're only reaching out when there's a real buying trigger — not hoping your cold email lands at the right time.
The Easier Way
Goose runs all three signal monitors continuously across your target accounts and automatically cross-references them into a single prioritized pipeline.Try Goose free →
When a compound signal appears — like a funding round plus a new SDR hire — Goose scores it, finds the right contacts, and drafts outreach with the signals stacked in.Try Goose free →
You get a weekly Slack digest of who to reach out to and why, without managing any scripts or APIs.Try Goose free →
What to Do Next
- How to Automate Hiring Signal Outbound Using AI — Deep dive into the hiring signal monitor
- How to Automate Funding Signal Outreach Using AI — Deep dive into the funding signal monitor
- How to Automate Leadership Change Outreach Using AI — Deep dive into the leadership change monitor