Skip to main content
ICP = Ideal Customer Profile. It’s how you describe who you sell to. Guhan uses this to search LinkedIn + to score every prospect it finds.

​
The four ICP fields

Job titles

Their role. What do they do at the company?

Location

Where they live or work.

Industry

What industry their company is in.

Company size

Headcount range.
Plus optional filters:
  • HQ country — stricter than person-location; the company has to be headquartered there
  • Funding stage — company’s most recent funding round
  • Custom rules — via the Branch node in sequences (Advanced)

​
Getting job titles right

The most impactful filter. Start broad, narrow later. Broad approach (recommended for first Watchlist Agent): 
VP of Sales
Head of Sales
Chief Revenue Officer
CRO
Guhan will auto-expand each into variants (Vice President Sales, SVP Sales, Head of Revenue, etc.). Narrow approach (for tightly-targeted campaigns): 
VP of Sales Operations
Head of Sales Enablement
Director of RevOps
More specific = fewer prospects but higher precision.
Auto-variants are shown as ✨ chips under each rule row. Delete any that don’t fit your ICP.

​
Location tips

“United States” catches every US resident. Good for broad geo targeting.
“California” or “Bay Area” catches everyone in that state/region. Better for pipeline focused on specific tech hubs.
“San Francisco” catches SF-metro only. Narrowest.
Add them as separate chips. Logic is OR — a prospect matches if they’re in any of the locations.

​
Industry tips

LinkedIn’s industry taxonomy is specific. Common ones:
  • Software Development — the “SaaS” bucket
  • Financial Services — fintech, banks, brokers
  • Marketing Services — agencies + consultancies
  • Information Technology & Services — IT services + integrators
  • Computer & Network Security — cybersecurity
Full list is searchable in the picker. Multi-select with OR logic.
“SaaS” isn’t in LinkedIn’s taxonomy. Use Software Development + narrow via signals or company size.

​
Company size tips

Guhan reads LinkedIn’s employee count for each company:
RangeTypical company shape
1–10Solo founders, pre-seed
11–50Seed / Series A
51–200Series B / early scaleup
201–500Mid-market
501–1,000Larger mid-market
1,001–10,000Enterprise
10,001+Fortune 500
Leave both ends blank → no size filter.

​
Must-match vs. nice-to-have

You can mark each filter as Required or Nice-to-have:
  • Required — prospects failing this filter are rejected
  • Nice-to-have — contributes to the match score but doesn’t reject
By default, Titles and Location are Required (they’re the definitional filters). Everything else is Nice-to-have. Change with the chip next to each rule row on the Watchlist Agent WHO panel.

​
Score threshold

Every prospect gets a match score 0–100. Below the workspace threshold (default 50) = filtered out. You can override the threshold per Watchlist Agent:
  • Tighter (70+) — fewer prospects, higher quality
  • Looser (30+) — more prospects, wider funnel
Find it on the Watchlist Agent detail page → Settings.

​
Debugging your ICP

If your first sweep found zero prospects:
  1. Titles too narrow? Try broader — “Sales Leader” instead of “VP of Enterprise Sales”
  2. Signals too tight? Remove or loosen your signal
  3. Industry mismatched? Pick a broader LinkedIn category
  4. Company size mismatched? Widen the range
If your first sweep found 200+ prospects of dubious quality:
  1. Narrow titles — remove overly broad chips
  2. Add signals — turn on funding OR hiring to focus on active buying windows
  3. Tighten the score threshold

ICP scoring

How the score is calculated.

Signal catalog

Every signal with examples.