Prepare a Wisery contact export for a manual import into a spreadsheet or customer relationship management (CRM) tool. A CSV (comma-separated values) file stores contacts as rows, with a separate column for each field. This guide helps you match those columns, preserve phone numbers, and check the result before importing the full file.
Before you start
You need a contact CSV exported from Wisery using Pro access and permission to import data into your destination tool. If you have not downloaded the file, follow Export collected contacts as a CSV file first. The Wisery export is named exportedContacts.csv.
1. Keep an untouched copy of the export
Keep the original Wisery CSV unchanged.
Create a working copy for preparing the import.
Open the working copy through your spreadsheet’s CSV import option so you can check the delimiter and column formats.
Use the original export to recover any values changed during preparation. Editing a downloaded CSV does not change the contacts stored in Wisery.
2. Match the Wisery columns to destination fields
The table below explains the Wisery CSV columns. During import, choose the matching destination field for each column you want to use. Field names vary between tools.
Wisery CSV column | Destination field or handling |
firstName | First name |
lastName | Last name |
Email address | |
phone | Phone number; preserve as text |
title | Job title or position; this comes from Position in the contact form |
company | Company or organization |
LinkedIn profile URL, if the destination supports it | |
createdAt | Submission date and time, if needed and supported |
id and pageId | Wisery reference values; keep in the original file and leave unmapped during contact import |
Check the required date format and source timezone before importing createdAt. If the timezone has not been established, leave that field unmapped rather than converting it using an assumption. Leave unsupported or unnecessary columns unmapped.
Keep the original export and test one or two contact rows in a separate CSV with the same headers before the full import.
3. Check values and possible duplicates
Import the phone column as text where your tool allows it. Preserve country prefixes such as +1 and any leading zeros.
Check for blank fields required by the destination, such as an email address or last name. Use its import rules to decide how to handle those rows.
Review possible duplicates using email addresses or phone numbers where available. Check matches before removing rows from the working copy.
Keep the first row as column headers if your destination expects a header row.
4. Test a separate CSV with one or two contacts
Create a separate test CSV with the same column headers as your working file and one or two contact rows.
Import the test CSV using your destination’s CSV import tool and the field mapping you prepared.
Open the imported records and check names, email addresses, phone numbers, job titles, company names, and LinkedIn links.
The test succeeds when the imported records appear, their phone values match the original export, and the imported contact count matches the number of contact rows in the test CSV.
If a check fails, correct the field mapping or working file and repeat the small test before continuing.
5. Import the remaining contacts
Create the final CSV with the same headers and remaining contact rows, excluding the test rows already imported. Import that file into the destination.
Review the import report and compare imported, skipped, and rejected rows with the final CSV.
Fix common import problems
Everything appears in one column: check that the import uses a comma delimiter.
A phone number changed: return to the original export and import that column as text. Formatting an already changed value may not restore it.
Names contain unexpected characters: choose UTF-8 in the destination’s file-encoding setting and retry with the original export.
Rows were skipped: read the import report for missing required fields, invalid values, or duplicate-handling rules.