RB2B Non-ICP to ICP Contacts

What this skill does

Filters ProfileType = Person rows from an RB2B export for visitors whose title/role doesn't match your ICP - but whose company does. Then finds contacts at those companies and enriches with email and phone.

Useful when, for example, an engineer from a target account visited your site but your ICP is VP Sales - the company is a signal, the visitor isn't the target.

Chain

RB2B CSV (Person rows)
  to define ICP (to identify non-ICP visitors)
  to filter: company-fit visitors who aren't ICP
  to extract their companies (deduplicated)
  to search_people  (find ICP contacts per company - filter by seniority/department)
  to linkedin_to_business_email     (per-URL parallel calls)
  to linkedin_to_mobile_phone              (batch array, optional)

Inputs

• RB2B CSV export - upload or paste (Person rows only)
• ICP definition - what role/seniority/department counts as ICP (to filter out)
• Acceptable risk scores - which A-F deliverability grades to keep (recommended: A, B, C and F; D excluded)

Output

Visitors processed:   X
Non-ICP visitors:     X
Unique companies:     X
ICP contacts found:   X
Business emails:      X/N (XX%)

Email risk scores:
  A: X   B: X   C: X   D: X   F: X
  kept (accepted grades):  X
  filtered (excluded):     X

Search filters

search_people filters on query, company, company_domain, country, state, city, seniority, department or functional_area, industry, linkedin_industry, naics_code, employee_range, and revenue_range.

• Filters are free precision - execution cost comes from the free-text query, so a filters-only search runs ~40x cheaper and lands more precisely. A bare query is never the right shape; at minimum it gets country attached (~99% fill rate).
• Size and revenue apply to the person's current employer - "VPs at 51-200 employee software companies" is one call, not a company search followed by a people search.
• Three industry vocabularies - broad industry buckets, LinkedIn's ~150 niche labels, or a NAICS code at any depth. Shortening the code widens the net.
• Sparse filters are the usual cause of a thin result set - seniority/industry/department are ~60% filled and naics_code/linkedin_industry ~50%, and every filter is exact-match, silently dropping records where the field is empty.
• Sizing is free - count_only: true returns a match count without fetching data or charging tokens, and every response carries results.total to read before paginating.

Tips

• The visitor is the signal; the ICP contact is the target
• Companies with multiple non-ICP visitors = higher intent account - prioritised automatically
• For visitors who are ICP and need direct enrichment, use Enrich RB2B Visitors instead
• Every returned email carries an A-F deliverability risk score; you're asked up front which grades to keep, and out-of-range grades are shown and flagged rather than silently dropped