Robert, Bob, and R. Smith are all the same person.

Robert Smith signed up on your website. Bob Smith bought something at an event. R. Smith mailed in a check. Your database counts three people. Your mail budget pays for three pieces. And one slightly annoyed customer opens all of them.

How duplicates sneak in

Duplicate data is easy to get. Merge a few files together, rely on customer input for a few years, and the same person starts appearing multiple times — each entry a little different, none of them matching exactly, all of them real to your database. The costs pile up quietly: postage and printing paid two or three times over for one household, response rates that look worse than they are because the denominator is inflated, and the small negative moment every time someone receives multiple copies of the same mailer.

Simple dedupe or full merge/purge

Some projects just need duplicates found within one dataset. Others are bigger: several lists from different sources that need combining into one clean file. We handle both. For merge/purge work, that includes list priority — so your house list wins over a rented one — custom suppression lists, and building a best record that keeps the strongest data from each source instead of picking one copy and discarding the rest.

The matching itself is built for how real data actually varies. Robert, Bob, and R. Smith can be recognized as the same person — nicknames match their formal names, initials are handled, and common misspellings are caught — not just records that agree character for character.

You also decide how wide the net goes. Matching works at three levels — individual (the same person), household (different people living at the same address), and address (one record per delivery point) — so you can bring a mailing down to one piece per person, one per family, or one per door, depending on what the campaign needs.

What you get back

A clean, consistent view of your data — each customer once — along with detailed reports showing match statistics for every list. You see what was found, where it came from, and what survived. As with everything we run, the improvements come back to you, documented; nothing is silently deleted.

Why run it with us

Matching rules involve judgment calls — how aggressive to be, what counts as the same household, which record deserves to survive. That's a conversation, not a checkbox. Start there: a real person will look at how your data is built and tell you what it needs, and what it doesn't, before you ever send a file.

How it works

1. 1

   Start with a conversation

   Tell us where your data comes from and how it gets used. A simple dedupe and a multi-list merge/purge are different jobs — we'll tell you which one your data actually needs.

2. 2

   We find the matches

   Records that represent the same person get identified across your data — including the ones that don't match character-for-character.

3. 3

   For merge/purge, your rules apply

   List priority decides which source wins, custom suppression lists drop who you never want mailed, and a best record can be built from the strongest data in each source.

4. 4

   Results come back to you, documented

   A clean, single view of each customer, plus detailed reports with match statistics for every list — so you can see exactly what was found and where.