ORCA LinkedIn Engagement

Where each column comes from

• Person, engagement, dates — the manual LinkedIn capture behind the previous version of this tracker (~91 posts, 16 Sep 2025 to 28 Aug 2026). Not a live feed.
• Role, company, industry, headcount, email — Apollo standard match, run on all 150 external engagers. Apollo resolved 39; those 39 also supply an exact LinkedIn profile URL. Where Apollo had nothing, role and company are read off the person's own LinkedIn headline and the LinkedIn link falls back to a name search (marked with a faded icon).
• CRM state and Close links — live lookups against ORCA's Close org. Every external engager was searched by name; everyone scoring In ICP or Watch was then re-checked against their employer's lead, which is how 22 people filed as contacts under a company lead were caught.
• Open deal — opportunities on the matched Close lead, excluding won and lost.

How the ICP score is calculated

A transparent 0–100 rubric built from ORCA's own qualification drivers, so it can be argued with rather than trusted blindly. Open any row's + to see the exact points that person scored and why.

• Segment fit, 0–40 — mapped onto ORCA's 17-value Lead Segment picklist. Family offices and trust companies score highest; corporates and operating companies mid; agencies, recruiters, engineers and coaches score zero.
• Authority, 0–25 — can this person buy or drive an evaluation: C-level, founder and partner at the top, individual contributors at the bottom.
• Entity complexity, 0–20 — fiduciary, governance, corporate-secretarial, multi-jurisdiction, tax, UBO/KYC and multi-entity signals in their role and firm.
• Engagement intensity, 0–15 — how many distinct ORCA posts they engaged with.
• Out-of-ICP penalties — ORCA's own disqualifiers: direct competitors, performance-reporting-only focus, engineering-led orgs inclined to self-build, and profiles with no entity use case at all.

Bands: In ICP is 60+, Watch is 35–59, Not relevant is under 35. The score is a firmographic fit estimate from a LinkedIn headline plus Apollo data — it is not a qualification percentage. Nobody here has had a qualification call, so treat it as a queue order, not a verdict.

Known gaps — read before acting on a row

• No comment / reaction / repost split. The capture never recorded interaction type, so neither the team leaderboard nor the lead rows can tell a like from a comment. That needs a fresh scrape.
• Apollo matched only 39 of 150. The rest have no verified email or exact profile URL — mostly people whose headline names no employer, which is exactly what Apollo needs to match. Waterfall enrichment would fill some of these in at a variable credit cost.
• Close matching is name-based and imperfect. 11 matches are medium confidence, and the 54 Not-relevant engagers only ever got the first name-only pass — a few of those may sit in Close under a company lead. Check the record before creating anything.
• Some Close records are stale. Where Close itself flags a contact as departed or a test record, the row says so in its expanded notes.
• Nine people are not in Close but their employer is — those rows carry a "company in CRM" link. Attach the contact to the existing lead rather than creating a duplicate.
• High-reaction posts may be undercounted. LinkedIn lazy-loads reactor lists, so posts with roughly 20+ reactions were more likely to be captured partially.
• Internal / likely-internal flags are inferred from title text and co-occurrence with known staff, not from an HR roster. Two people are marked "Internal?".