Discovering gold in email auto-responses

With every 10,000+ newsletter send, dozens of out-of-office replies quietly land in your inbox.

Most publishers ignore them.

Instead, you can turn them into one of your highest-quality prospect lists—automatically. This report shows you how to build a Claude Code skill that extracts the right contact information from every auto-response, organizes it into a spreadsheet, and gives you a ready-to-use replacement contact list in minutes.

Why mine this list?

First, the auto-responses show who left the company and, most of the time, who replaced them—a chance to keep the contact list clean.

Then, when people go on vacation, the email of the person left in charge is often there, most of the time, in the auto-response email.

So, if the media or advertisers are interested in going deeper into the same company to gain more influencers, often critical to a B2B sale, that could be an important strategy. One use case showed 1% of a B2B  newsletter audience is on vacation any given week.

So how are media companies mining this gold? One simple way is to set up a Claude skill, let’s call it “OOO Data,” to do the job. A skill in Claude CoWork is simply a repeatable task that can be run on a schedule, such as once a week or month. Other AI tools will do the same thing, but Claude CoWork is often best at repeating tasks.

Attached is a template excel-style sheet to use, with the legend and column headers already in place: Template OOO data

Here’s the prompt for your AI agent:

Step 1 – Connect with email and create a Google Sheet for the output of an email search.  Name the file  OOO data.

The column headers will be:

Remove email
Add email
First name
Last name
Title
Company
Scenario
Notes

Step 2 – Connect to [the email account that accepts auto-responders].

Step 3 – For the dates from [x] to [y], or for [campaign titled z], search Gmail for auto-replies. To do this, search subject lines for “automatic reply,” “out of office,” and “auto-reply” variants, using a date filter. If a reply’s preview snippet cuts off mid-sentence—especially where a replacement email might be truncated—open the full thread rather than guess.

Step 4 — Classify each reply into one of five scenarios.

On vacation, backup named → add the backup contact to “new email” (keep the original on the list).
On vacation, no backup email given → no action.
Left the company, replacement named →  the original to remove goes in remove column, add the replacement(s) to new column.
Same person, new company/email → remove the old address, add the new one.
Left or retired, no replacement → remove column only.
Additional Contacts (catch-all) → anything legitimately new but missing a key detail (cut-off snippet, a role instead of a name, “contact one of the people below” with no visible list) gets logged separately for manual review rather than guessed at or skipped.

Step 5 — Put this data from the auto-responses you found into the spreadsheet “OOO Data.”

Each of the first four scenarios gets its own color coding (green, orange, purple, grey).

Here is the legend:

LEGEND:

Scenario 1 – On vacation, backup contact named: ADD backup to list (keep original) – green

Scenario 2 – Left company, replacement named: REMOVE original, ADD replacement – orange

Scenario 3 – Same person, new company or email: REMOVE old, ADD new – pink

Scenario 4 – Left/retired, no replacement: REMOVE only – grey

Additional Contacts that are ambiguous go in a fifth row.

Add a new tab for each new date run.

Run every [week or month].

That’s all there is to it. The list is ready to hand over to an admin or agent who handles email.

There will be a few edge cases you can decide how to handle: multiple replacements for one departure, generic inbox addresses given as replacements, and vacation backups with only a phone number (no email = no action).

But essentially, the data is cleaned and added to on a regular basis.

So what are the metrics on this? For media that have not cleaned the list this way, the first four sends show about a 2% gain in new internal emails and a 2% remove-and-replace rate—an important number.

Remove only was much smaller, 0.01%, but an important number for the overall quality score.

After running once, remove and replace also drops to an insignificant number.

However, the gains from “on vacation, here’s the backup” continue at about 1% per send.