Finding People
Outcome: a person-grain table holding the right two or three contacts per qualified company, with LinkedIn URLs ready for enrichment.
- Surface
- Sync GTM app and MCP server
- Level
- Beginner
- Uses
- find_people_within_company · find_people
- Credits
- ~5 for 25 companies at 2 contacts each
- Prerequisite
- Lesson 06's company table
Only from qualified rows
The first instruction is a filter, not an action. Run people-finding against the qualified view from lesson 04, never against the whole company table.
Contacts multiply. Twenty-five companies at three contacts each is 75 rows, and every person-level column downstream bills against that number rather than 25. Filtering first is what keeps the course under 60 credits.
Two ways in
| Action | Use when |
|---|---|
| find_people_within_company | You have the companies and want people at each — the normal case here |
| find_people | You want people matching criteria across all companies, e.g. every RevOps lead in a country |
This lesson uses the first. find_people is the route for signal-led plays where the person matters more than the account.
Title versus seniority
Filtering on exact titles is the most common way to miss the right person.
Seniority is portable, titles are not
“VP” and “Head of” and “Director” mean different things at 50 people and at 5,000. A seniority filter travels across company sizes; a title list does not.
Function is what you actually mean
You want whoever owns the problem. Filter on the function — sales, marketing, engineering, finance — plus a seniority band, and let the titles fall where they fall.
Title keywords are for narrowing, not defining
Add them once the function-plus-seniority result is too broad. "revenue operations" OR "sales operations" narrows well; using it as the primary filter misses “Director, GTM Systems” entirely.
A workable default: function = the relevant department, seniority = director and above, plus C-level. Then read the output and adjust.
How many per account
| Company size | Contacts | Who |
|---|---|---|
| Under 50 | 1 | Founder or the single functional lead |
| 50–200 | 2 | The functional lead and their manager |
| 200–1,000 | 2–3 | Practitioner-level owner, their director, sometimes the VP |
| 1,000+ | 3 | Multiple business units — expect to need account research |
Two is the right default. One misses the person who left; four starts to look like you are spamming the company, and several of them will compare notes.
Contacting five people at a 60-person company in the same week is noticed and it reads badly. Scale contact count to company size, and put a cap in your plan rather than discovering it from a reply.
Build it as a second table
Person rows go in a person-grain table, keyed on LinkedIn profile URL, carrying the company domain so it joins back.
Carry across only the company fields you will use in copy or filtering — usually the domain, the company name, and the one or two research answers from lessons 09 and 10. Copying twenty company columns onto every person row makes the table unreadable and helps nothing.
Do this now
Filter to qualified companies
Use the qualified view. Confirm the count before running anything.
Configure the people search
Function plus seniority. Resist the title list on the first pass.
Run on five companies
The test-5 habit applies here too. Read every person returned.
Check the output
Are they at the right company, in the right function, at a plausible seniority? Is anyone obviously the wrong person — an assistant, a contractor, someone who left?
Adjust and widen
Add title keywords only if the sample was too broad. Then run across the qualified set with a per-company cap of two.
Create the person table
Person grain, LinkedIn URL as identifier, company domain carried across.
Check your work
- People were pulled only from qualified companies
- Every row has a LinkedIn profile URL
- Contact count per company matches the size band
- You read at least ten rows and they are plausibly the right people
- The company domain is present on every person row
Where this breaks
A too-narrow title filter produces a small, clean-looking list with a large silent gap: the companies where nobody holds that exact title simply return nothing, and they vanish from the pipeline without appearing as a failure. Always compare the number of companies you searched against the number that returned at least one person. If that ratio is below about 70%, your filter is too tight, not your market.
Further automation
Once the person search works, it becomes the second half of a standing pipeline: newly qualified companies flow in from the saved search in lesson 06, and people are pulled automatically for each. The pairing is the core of Automated Outbound.
Next lesson
08 — Finding local businesses, the geographic route into a list when your buyers are not on LinkedIn.
Reference for this lesson: find_people_within_company, find_people, Person, Current role.