CRMs are great for keeping me organized. But the second you try to do anything that resembles real research in a CRM workflow, it gets messy.
I started with a list of leads (investment funds). My goal was to get everything into HubSpot, where I'm treating funds as companies and contacts as the people linked to those companies.
The problem: getting the data into the right shape pre-HubSpot upload
I often find myself struggling with the data I actually want living across two levels:
- Fund-level context (scores, research hooks, team size)
- Contact-level rows (name, title, email)
If you upload contacts without the fund-level context attached, you end up doing extra work inside the CRM. And if you try to keep fund-level data in one table and contacts in another, you end up constantly transferring columns between tables as your scoring or research changes.
What I wanted instead was one flat export where every contact row already includes the fund-level fields.
How I flattened everything
In everyrow.io, I used the merge tool to join my contacts table with my funds table on the fund name column.
The green columns reflect fund-level research hooks now being attached to every contact row.
That gave me a single export where each contact row included:
- Contact info (name, title, email)
- Fund info (differentiation strategy, research tool likelihood, team size)
- Outreach hooks (research_1, research_2, research_3)
From there, it was exactly what I wanted: one CSV upload to HubSpot, and everything linked correctly.
That's the whole reason I use merge: do the heavy lifting once, then upload a clean, CRM-ready list.
If your bottleneck is "I have the research, but I can't get it into my CRM cleanly," this workflow is worth trying.
You can test it on your own data at everyrow.io/merge.