From spreadsheets to Pipedrive

The good news

A team coming from spreadsheets has no legacy configuration to unpick. No custom objects. No abandoned workflows. You get to design the setup for how you sell today.

These projects are also the cheapest. A team of five with one clean sheet can be live in two weeks for a few thousand euro, or for internal time only if someone owns it.

The real problem

A spreadsheet is one flat table. A CRM has three linked record types. Organisations, people and deals. If you import the sheet as it stands, you get a thousand contacts with no companies, or a thousand companies with a single contact each.

So the first job is to split the file. Do it before anything else.

Splitting the sheet

Make three tabs.

Organisations. One row per company. Columns for name, domain, country, industry, owner. Deduplicate on domain, because company names are typed four ways.

People. One row per person. Columns for first name, last name, email, phone, and the company name exactly as written in the organisations tab. Deduplicate on email.

Deals. One row per opportunity. Columns for title, value, currency, expected close date, stage, owner, the company name and the contact email. Every deal needs a title that makes sense on a board, not just a company name.

Cleaning the columns

• Split full name into first and last name.
• Remove text from the value column so it holds numbers only.
• Put currency in its own column, using three letter codes.
• Convert status words into your new stage names, using a mapping table.
• Replace notes typed into cells with a separate notes column per deal.
• Set an owner per row. Blank owners create records nobody follows up.

Two hours here saves a week later. This is also the part you should do yourself, because you know which rows still matter.

Deciding what not to move

A sales sheet grows for years. It holds leads someone met at a conference in 2019, and rows with only a first name.

Move rows with a real contact detail and a real chance. Park the rest in a second file. If nobody asks for it in three months, you had your answer.

Configure before you import

Build the pipeline and the fields first. Match the field names to your column headers where you can, because it makes the mapping screen obvious.

Keep the field set small. A team leaving a sheet has few habits to preserve, so start with eight to twelve deal fields and add later.

The full setup order is on implementation in 6 steps.

Import order

Organisations first. Then people, matched to organisations by name. Then deals, matched to both. Then notes.

Run a test with twenty rows. Open three records and check that the person sits under the right company and the deal shows the right value and date. Delete the test rows, then run the full file.

Getting the team off the sheet

This is where these projects succeed or quietly fail. If reporting still happens in the sheet, the sheet stays alive, and the CRM becomes double work.

Three things end it.

First, connect email and calendar on day one, so logging is automatic rather than manual.

Second, stop asking for the sheet. Pull the Monday numbers from Pipedrive, even in week one, even when they look incomplete. Incomplete numbers create a reason to update the system.

Third, put the sheet in a read only archive folder after two weeks. Not deleted, not editable.

What good looks like after a month

Every open deal has a next activity. The pipeline total roughly matches what the team believes. Nobody has asked where the sheet went in the last week.

If those three are true, the migration worked. If not, the fix is usually in the process, not the configuration. Read common implementation mistakes next.

Questions