Topics covered in this article include:

This article has basic instructions on preparing data and then importing it into Pipeliner. The information below is intended to give more detail and help with preparing more complex data.

What does this mean in practice? It means that when you are importing Account records you need to ensure that there are NO duplicate Account names. This can easily be the case if your data contains branches or sites of the same company.

The way that most customers deal with this is to add, for example, the State or City name to the Account name — e.g. “BP Petroleum – Dallas” and “BP Petroleum – Austin”. Don’t forget to make the same updates to your Contacts file so that the right Contacts will end up linked to “BP Petroleum – Dallas” vs “BP Petroleum – Austin”.

If you just leave the name as “BP Petroleum” and import two Account records with the same name, when you then import Contacts, Pipeliner will have no way of knowing which of the two Accounts the Contacts should be linked to and you’ll have to sort it out manually post-import.

Excel has a really handy tool to help you identify duplicates. Highlight your column — e.g. "Account Name" — and then use the Conditional Formatting option to highlight duplicate values ⤵

You can then clearly see which records to work on ⤵

It’s really common to get a list from your users that are based on Contacts. You’ll probably need to use this to prepare your Accounts list for import (remember you need separate CSV files for Accounts and Contacts). If you import Accounts from the original file, you’re likely to end up with lots of duplicate Account records and your matching will be sunk when you try to then import your Contacts!

First, clean your data — sort everything into the right columns, clean up email and physical addresses etc. Once your Contacts list is beautifully clean and free of duplicate Contacts and bad data save a copy as "CONTACTS.CSV". Now, use Excel’s “Remove Duplicates” function to remove duplicate rows based on any field — or combination of fields — that you choose.

In my example, I’m de-duplicating based solely on the Account name. In real life, I’d probably use at least one address field as well, just to be sure that I’m not removing too many records ⤵

Once it’s done, you’ll be left with a single row per Account and can then "Save As" and save your "ACCOUNTS.CSV" and import it.

Once your Accounts are imported, your original Contacts file can then be used to import your Contacts and will match perfectly as long as you remove all duplicate Account names.

You may have data with a single column “Name” for Contacts. To get the best out of using that data in Pipeliner — e.g. being able to use personalisation in templates so you can send emails to "Dear {Firstname}" — you need to split the name into "First Name" and "Lastname".

You can use the “Text to Columns” function in Excel. First, move your column to the end of your spreadsheet so you don’t risk overwriting data in adjacent columns and then highlight your Name column and choose “Text to Columns” then “Delimited”.

Change the Delimiter option to "Space" and then select "Next" and "Finish" ⤵

Your name will be split into two columns and all you have to do is then rename the header for ease of mapping ⤵

All records in Pipeliner need to have a Sales Unit and an Owner. You can include these in your data — Sales Unit names have to match those you have created in the "Admin Module" in "Units" within the "Units, Users and Roles" section.

Use the user’s email address to map to the "Owner" field (it is possible to map Firstname and Lastname if you know for sure that all your users have already logged in, but it’s easier and more reliable to use their email address). All owners have to be active Pipeliner users.

If you don’t include "Owner" and "Sales Unit" in your data, all records will be owned by the importing user and the Sales Unit will be set to the importing user’s default sales unit. You'll then need to manually manage the reassignment of records.

Make sure that the Owner is assigned to the Sales Units you’re using in your data. You can’t map a record to a Sales Unit that the Owner you’ve selected is not assigned to.

Email addresses need to follow the “test@example.com” format exactly — no spaces must have an "@" symbol, must have .com or .org etc...

Problems with email addresses can be difficult to spot in large datasets. Leading or trailing spaces are a very common issue — use the Trim function in Excel to make sure there are none in your data ⤵

Also, do a "Find & Replace" on “mailto:” as this is often inadvertently added by users when copying and pasting emails.

If you’re importing data into a dropdown field in Pipeliner, you won’t be able to import records that have values in them that don’t match those you have in Pipeliner.

For example, let’s say you’re importing Accounts and that your file contains a column called Industry. You’ll want to map this column to the Industry field that already exists as a default field in Pipeliner. Let’s say you have rows in your data that are companies that are in the industry “Engineering” — before you can import successfully, you’ll need to check-in Pipeliner that “Engineering” is already in the dropdown list for the Industry field. You can do this from the "Admin Module" in "Fields & Forms" or by checking in the app by clicking in the Industry field against any account record and looking for Engineering in the list. If it’s not there, you’ll need to add it from the "Admin Module" and publish your changes to apply it to the app before you start importing. This article will help.

If you’re importing into a multi-select dropdown field or importing Tags, you need to separate multiple values in your column using a separator and no spaces. The preferred separator is a pipe symbol, e.g. you have an “Interest” field with the following values set up in Pipeliner — Golf, Football, Sailing. To successfully import into this field, prepare your data in the following format: “Golf|Sailing”.

When you come to do your import, make sure you then set the Multiple Checkbox Separator to match:

Dates are best formatted as "yyyy-mm-dd" which is the internationally accepted ISO date format.

Having said that, you can select the date format used in your file from the most common standard date options when choosing your csv file settings.

We currently support the following date formats:

Numbers should not have a thousand delimiters — e.g. use 10000 NOT 10,000. Currency values should not include the currency code — e.g. 10000.00 NOT $10,000.00.

For a tick, the value supplied should be 1 whereas for unchecked, use 0