marketingskills/skills/prospecting/references/saas-prospecting.md

# SaaS Prospecting Reference

For when the user sells SaaS or digital services to other SaaS companies / digital businesses.

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## ICP Signals That Matter (SaaS branch)

Beyond standard firmographics (industry, size, geography), SaaS prospects are qualified by:

### Technographic signals

- **Tech stack** — do they use complementary tools (your integration target) or competing tools (a switch opportunity)?
- **Recent stack changes** — adding/removing tools signals active vendor evaluation
- **Custom-built vs off-the-shelf** — DIY tooling often means a buyer who'd benefit from your product
- **Free/freemium plan signals** — using a free competitor means they may be ready to upgrade

### Growth signals

- **Funding round** — Series A / B / C in last 6 months = budget + new hires + tool needs
- **Headcount growth** — 10%+ growth in last quarter signals scaling pressure
- **Hiring signals** — specific role openings (e.g., "Head of RevOps" → ICP for revops tooling)
- **Product velocity** — frequent shipping, new features, blog posts = healthy growth motion
- **Open positions for your buyer's role** — if you sell to Marketing Ops and they're hiring one, that's a signal

### Decay signals (downgrade scoring)

- Layoffs in target department
- Funding round >2 years ago with no follow-up
- Product hasn't shipped in 6+ months
- Team page shows founders only (very early — may not have budget)

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## Discovery Sources (SaaS branch)

Combine 2+ sources for cross-verification.

### Tier 1 — primary discovery

- **Apollo**: firmographic + technographic + contact data. Good for building large initial lists.
- **Clay**: waterfall enrichment, custom scoring, multi-source merges. Best for high-quality smaller lists.
- **ZoomInfo**: enterprise-grade firmographic + intent signals. Expensive; mid-market+.
- **LinkedIn Sales Navigator**: decision-maker mapping. Use manually, never bulk scrape.

### Tier 2 — technographic / growth signals

- **BuiltWith**: tech stack lookups, find sites using specific tools
- **Wappalyzer**: free browser extension + API; lighter tech stack signal
- **Crunchbase**: funding rounds, headcount, founders
- **Pitchbook**: deeper investor data (enterprise/paid)
- **ProductHunt**: recent launches, builder audience
- **Hacker News / Show HN**: technical builders launching products

### Tier 3 — buying signals

- **Job boards** (LinkedIn Jobs, Indeed, AngelList): role openings as signals
- **RB2B / Clearbit Reveal**: visitor identification (warm anonymous traffic)
- **GitHub stars/forks of competitor or adjacent repos**: developer-level intent signal (see `tools/integrations/github.md` and the `github-prospects.js` CLI). Especially strong for dev-tool SaaS — a developer who starred `vercel/next.js` last week is in-market for adjacent Next.js infrastructure.
- **Recent blog posts / changelog**: product direction signals
- **G2 reviews mentioning competitor switches**: explicit dissatisfaction signal

#### GitHub prospecting pattern (when audience is developers)

For dev-tool SaaS, GitHub is one of the highest-quality discovery channels:

1. Identify 3–5 "anchor" repos: your direct competitors, your category leader, complementary tools your buyer uses
2. Pull stargazers (or forks for stronger intent) via `node tools/clis/github-prospects.js stargazers <owner/repo> --enrich --with-company --format csv`
3. Filter to users with `company` set — these are the easiest to enrich downstream
4. Pair with Apollo/Clay/Hunter to lookup email by name + company
5. Validate with Truelist before adding to outreach list

Tradeoffs: GitHub yields email for only ~5–20% of users directly. The strength is the signal quality — a stargazer of a niche dev tool is genuinely in-market in a way Apollo firmographics alone can't tell you.

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## Qualification Checklist (SaaS branch)

For each candidate, verify:

- [ ] Industry vertical matches ICP
- [ ] Company size (headcount) within range
- [ ] Tech stack includes (or notably excludes) a target technology
- [ ] Funding stage matches buyer maturity
- [ ] At least one growth signal in last 90 days (funding, hiring, product velocity)
- [ ] Decision-maker role exists at the company (named or inferable from job listings)
- [ ] Email contact verifiable
- [ ] No disqualifiers (closed, acquired-and-paused, layoffs, ICP miss)

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## Output Columns (SaaS branch)

Recommended CSV columns:

```csv
score,company,domain,industry,size_band,country,funding_stage,last_round_date,tech_stack_match,signal,signal_date,contact_name,contact_title,contact_email,email_status,linkedin_url,source_urls,why_prospect,confidence,verified_date,notes
```

For chat table, condense to: Score | Company | Industry | Size | Signal | Contact | Email status | Confidence.

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## Top Outreach Targets Selection (SaaS)

Prioritize for the top 3–5 hot leads:

1. **Strongest signal recency** — funding 30 days ago beats funding 9 months ago
2. **Tech stack match strength** — known integration partner beats inferred fit
3. **Decision-maker named with verified email** — beats role-pattern-guessed email
4. **Multi-source confidence** — both Apollo + Crunchbase agree beats one source

Each top target gets a one-sentence outreach rationale that names the specific signal: "Raised Series B 30 days ago; hiring Head of RevOps; verified VP of Ops email."

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## Common Mistakes (SaaS)

1. **Buying lists from Apollo wholesale** without re-verifying email and re-checking firmographics. Stale data is the norm.
2. **Treating tech stack data as 100% accurate**. BuiltWith and Wappalyzer miss things; Clay's waterfalls miss things. Cross-check.
3. **Targeting Series C+ for early-stage SaaS sellers**. The buyer profile is wrong — too many procurement hoops, too much red tape.
4. **Targeting Series Pre-Seed seed** for products requiring meaningful budget. They have neither budget nor evaluator bandwidth.
5. **Ignoring intent data when it exists** (ZoomInfo Intent, 6sense, etc.) — pre-warm signals beat cold every time.