Column structure, inconsistent values and cross-field checks

Updated Markdown

Beyond checking individual fields, ListPlus looks at your list as a whole — whether a column mixes types or date formats, whether the same value is written differently across rows, and whether values in different columns contradict each other. All of these are free, deterministic checks.

Column structure#

  • Mixed Types in Column — a column mostly numbers or dates has a minority (over 10%, at least three values) of a different type, often a misaligned import; text columns such as ZIP, phone, names or employee ranges are not checked.
  • Date Format Inconsistency — a date column mixes formats, e.g. YYYY-MM-DD with DD.MM.YYYY.
  • Empty Row — a row with no data in any column (a row that only holds enrichment data does not count); its Fix ("Ignore Empty Rows") marks it as ignored, restorable via the Ignored chip in the status bar.

Inconsistent values (Normalize)#

Nine content types get their own consistency check when your list has that column: State, Industry, Language, Salutation, Seniority, Department, Job Function, Lead Source and Lead Status Inconsistency. Each flags case or spelling variants of the same value (e.g. "california" vs "California") and offers a Normalize fix, which opens a review window where you confirm the standard value for each group. Fix all skips it — see "fixing-issues" for how Normalize fits into Quick fixes.

Cross-field conflicts#

  • Email-Company Mismatch — the email domain doesn't match the company name.
  • Name-Email Mismatch — the email address is not made of the person's name: not the name, not a short form (alex@ for Alexander), not initials (tvm@ for Thomas Van Maele). That can mean the wrong person is attached to that row. Role addresses (info@) are left to the role-address check.

ZIP and phone number mismatches against the country column are covered in "addresses-and-countries".

"What the Analyze panel checks and how the quality score works" · "Fix and Fix all — correcting issues automatically" · "Country, city and postal code checks" · "Selecting and highlighting rows from checks".

FAQ#

Does ListPlus flag an e-mail like alex@ for a contact named Alexander?#

No — Name-Email Mismatch accepts short forms, initials and the usual arrangements (john.smith, jsmith, smithj, tvm for Thomas Van Maele); it flags only an address that carries no part of the name, like jason@ for Thomas Bannister.

Can ListPlus catch a column that mixes text, numbers and dates?#

Yes, with Mixed Types in Column — it flags the minority-type values when a column is mostly one type (number or date) but over 10% of values, and at least three, are something else; text columns such as ZIP or phone are not checked.

How does ListPlus normalize inconsistent values like "california" and "California"?#

For nine content types (State, Industry, Language, Salutation, Seniority, Department, Job Function, Lead Source, Lead Status), the matching consistency check flags less common spellings and offers a Normalize fix that opens a review window where you confirm the standard value before anything changes.

What does "cross-field conflict" mean?#

A value in one column contradicts a value in another on the same row — for example the Email-Company Mismatch check flags when the email domain doesn't match the company name, and Name-Email Mismatch flags when the email is not made of the person's name.

Does ListPlus catch inconsistent date formats in a column?#

Yes — Date Format Inconsistency flags rows using a minority date format (e.g. a handful of DD.MM.YYYY values mixed into a mostly YYYY-MM-DD column).

Are these checks free?#

Yes — every check listed on this page is deterministic and free, and runs automatically the first time you open the Analyze panel.