Searching the Reachium Database
What it does: builds a lead list from Reachium's own global people and company database, instantly, without spending any LinkedIn account capacity.
Run it when: the ICP is investors, or it is standard B2B and you would rather not wait two hours for a scrape.
Time: 10 minutes.
Before you start
The ICP one-liner
A decision on whether email addresses are genuinely needed
A target list size
The steps
1. Decide whether this is the right source.
Investor ICPs belong here first: VCs, family offices, PE firms are the database's strongest coverage, and LinkedIn scraping is a poor substitute. Standard B2B works well too. What the database cannot do is behavioural targeting: if the trigger is "commented on this post" or "posted in the last 30 days", that is a scrape, not a search.
2. Build the person side of the filter.
Title keywords, seniority, department, and the person's own country, state or city. Title keywords behave like the job-titles filter elsewhere: write the variations, because matching is on words, not meaning.
3. Build the company side.
Company keyword, industries, verticals, headcount buckets, and the company's own HQ location. Company location and person location are separate filters for a reason; a remote executive lives in one and works for the other.
4. For investors, switch the company type over.
Set company type to investor, then use the investor-specific filters: investor types such as Venture Capital or Family Office, and the sectors they invest in. Sectors go in the target-sectors filter, never in industries, which describes the firm rather than what it funds. This is the most common mistake on investor searches.
5. Only filter on email if the workflow needs it.
Email coverage is much thinner than LinkedIn coverage, so requiring a verified email shrinks the result set hard. If the campaign runs on LinkedIn, leave it off.
6. Preview before you import.
Run the search and read the sample rows. Do the titles and companies actually look like the ICP? A sample that reads slightly wrong becomes a list that reads very wrong at 2,000 rows. Never import a search you have not previewed.
7. Check what got dropped.
Industry, vertical and investor-type values are resolved against a controlled vocabulary. Anything that does not resolve comes back in a dropped-filters list rather than failing loudly. Read it. A search that silently ignored your industry filter is a much broader search than you think you ran.
8. Watch the ceiling.
Results cap at 10,000 rows. Hitting the cap means the real audience is larger than what you are seeing, so narrow before importing rather than taking an arbitrary slice.
9. Import into a named list.
Straight into a new or existing lead list, named after the ICP. From here it behaves like any other list: mergeable, splittable, ready for a campaign.
Do this / Not this
Do this | Not this |
|---|---|
Investors from the database | Investors from a LinkedIn scrape |
Target sectors for what an investor funds | Sectors in the industries filter |
Read the preview sample properly | Import blind because the count looked right |
Check dropped filters every time | Assume every value resolved |
Leave email filters off for LinkedIn campaigns | Require verified email and lose 80 percent of the list |
Narrow when the count hits the cap | Import 10,000 and sort it out later |
Person location and company location chosen deliberately | Both set, cancelling each other out |
Done when
The search previewed and the sample matches the ICP
Dropped filters checked and none of them mattered
Result count under the cap and inside the target band
Leads imported into a list named after the ICP
No LinkedIn account capacity was spent getting here
