The Five Most Common Mistakes in Contact Databases — and How to Fix Them

Mistake 1: Broken names. “MEIER”, “meier”, “Méier ”, “Hans-peter” — people type their names in every variation imaginable. Without normalization, your personal salutation becomes an embarrassment generator. The fix: automatic capitalization, whitespace cleanup, plausibility checks.

Mistake 2: Fantasy entries. Donald Duck, asdf, Test Test. Especially with free offers, people sign up with nonsense. These contacts never open, never buy — and they dilute your statistics. So: detection rules for obvious fake names, and regular clean-outs.

Mistake 3: Missing or wrong salutation. The overlooked classic. The gender field exists in almost every system — and is almost always empty. You know the result: “Dear Sir/Madam” or no salutation at all. Yet gender can be derived from the first name — rule-based, via simple name lists, fully traceable. I have been maintaining lists like these for years. It is not magic. It is diligence that can be automated.

Mistake 4: Duplicates. The same person with two addresses, or the same address imported twice. You pay twice — many systems bill per contact —, you annoy twice, and you measure wrong. A duplicate check before every import is basic hygiene.

Mistake 5: Untapped potential. Strictly speaking not a mistake, but the biggest loss of all. A zip code, an email domain, or a first name can tell you far more than most marketers ever use: region, B2B or B2C, age cohort. If you only store instead of enriching, you are giving away segmentation power.

Pro tip: take one hour and check your list against exactly these five points. Export 200 random contacts and look at them. Whatever you find there exists, extrapolated, across your entire database.