Every public company reports on a set date. We mapped 658,166 of them so our client knew when to reach out.

The drawing

The problem

Reporting dates were looked up by hand, one campaign at a time. There was no store, no record of where a date came from, and no way to tell which rows could be shared.

Here's the case we made. A listed company reports on a date it sets in advance. The weeks around that date are when the numbers get written, read, and questioned. That's the buying day. It's the one date a seller can know a year ahead.

You can argue with that. Plenty of deals close on a random Tuesday. But a random Tuesday isn't on a calendar. A reporting date is.

What we built

A reporting calendar database, built from public filings and exchange sources.

Every row records where it came from. That's what lets the publishable subset be a view instead of a second build.

Some venue directories don't allow redistribution. We classified them and kept them out of every published view.

We also traced index membership to the right providers instead of assuming it.

What it produced

• 658,166 rows.
• Coverage in one market corrected from 904 companies to 1,692. The first pass missed 788 companies, an 87 percent undercount.
• A 12 month forward sending schedule, with each company's outreach window placed around its reporting date.
• A marketplace actor built on the same data.

The first count was wrong by 788 companies. We only found it because we checked the providers instead of trusting the first list.

This study doesn't report a reply rate. It's about when to send. That part holds whatever the copy says.

Your scenario

If your buyers are listed companies, send us the list. We'll show you each one's next buying day.