For a successful import of your audience from a CSV file, please follow these recommendations while creating your file before uploading it to your Retently account.
Also read: How to import a CSV list with customers?
1. Make sure that it is a CSV format file (with the .csv extension). Other file types, such as Excel are not supported. If however, you have your customers stored in a .xls file, open it in Microsoft Excel or Google Spreadsheets and export it as a CSV file.
2. It is mandatory to have a column with your customers' email addresses. Also make sure these addresses have a valid email structure (firstname.lastname@example.org).
3. It is highly recommended (although optional) to include in the file a few other columns for First Name, Last Name, or Company. Retently provides a list with default customer properties, such as Country or Job Tile, but you can also create custom properties for other types of data, for instance, your customers' signup date.
4. It is recommended to name your columns in the first row of your file. This will help our system automatically identify the right labels for your columns (ex: First Name, Last Name, Email, Company)
5. Make sure that your CSV file is encoded in UTF-8 characters encoding.
6. In a CSV file (Comma-separated values), the columns are usually separated by comma ",". Retently recognizes the majority of separators, but to make sure that the file is uploaded and processed successfully, it is recommended to use either the comma or semicolon separators.
It is important to keep in mind that there are no duplicates in the CSV file when importing it. The CSV uploader will successfully identify if the customer has a duplicate contact in Retently when importing a new one, but it can't identify duplicate contacts within a CSV file that is being uploaded, because our system has no reference for duplicates in the customer's account.
We recommend making sure that your CSV file doesn’t include any duplicate email addresses. You can do this easily in Excel or Google Sheets by applying the function Remove Duplicates from the Data menu.
You can follow these tutorials: