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Custom fields are how Pipedrive bends to your business. They are also how owners create chaos in 60 days — 47 fields, half empty, three that say the same thing. Here is the discipline that prevents the cleanup project.
Who this is forSales ops leads and founders configuring Pipedrive for their actual motion. Or anyone inheriting an account where someone added 30 custom fields in a weekend. If your Deal sidebar looks like a tax form, this tutorial is for you.
What you'll need
Step 1
Pipedrive ships ~30 default fields per object (Deal, Person, Organization, Lead). Most of what you need already exists. Search before you create.
Open Company settings → Data fields. Pick the object (Deal, Person, Organization, Lead, Activity, Product).
Filter by "All fields" and read through the defaults. Common ones owners re-create unnecessarily: Source (Lead source), Industry, Annual revenue, Number of employees, Title, Phone, LinkedIn URL, Expected close date.
For each field your team wants, ask: "is there already a default that does this?" Most teams over-create custom fields by 40-60% because they did not audit defaults.
Mark which default fields you will actually use; hide the rest (uncheck "Visible") so the record sidebar stays clean.
Only after this audit should you create custom fields.
Step 2
Pipedrive offers Text, Large text, Numerical, Monetary, Single option, Multiple options, Date, Date range, Time, Time range, Address, User, Phone, Person, Organization, Autocomplete. Picking wrong loses data.
Text vs Large text: Text is single-line (max ~255 chars); Large text is multi-line. Use Large text for notes-style fields ("Pain point details") and Text for short tags ("Account tier").
Numerical vs Monetary: Monetary is locale-aware and respects deal currency. Always use Monetary for any dollar-amount field — never Text or Numerical with a currency symbol in the name.
Single option vs Multiple options: Single option = dropdown (one value). Multiple options = checkboxes (many values). Use Single for mutually exclusive categories ("Tier 1 / Tier 2 / Tier 3"); Multiple for non-exclusive tags ("Industries served").
Date vs Date range: Date for single moments ("Trial start"). Date range for periods ("Contract term").
Person / Organization / User: link fields. Use these instead of typing a name into Text — link fields create real relationships that drive reports.
Changing a field type after data exists in it usually wipes the data. Pick once, carefully.
Step 3
Required fields force reps to fill key data at object creation. Set them sparingly — too many requirements and reps create junk values to bypass.
Company settings → Data fields → click any field → toggle "Important field" or set as required via Workflow Automation (Professional+).
On Lead/Deal: require Source (drives marketing attribution), Owner, Expected close date (for forecast).
On Person: require Email or Phone (one or both — without contact info, the person record is useless).
On Organization: require Domain or Website (drives dedup — see below).
Rule: 3-5 required fields per object max. Past that, reps fill garbage just to get through the form. The point of required is "this data is so important the record cannot exist without it" — not "we would prefer to have this."
Step 4
Conditional fields appear only when another field has a specific value. Lets you have many fields total without overwhelming the sidebar.
Company settings → Data fields → click any field → "Conditional logic" → "Show field if [other field] [equals/contains] [value]."
Example: "Renewal date" should only appear when Deal type = Renewal. "Trial conversion date" only appears when Lead source = Trial signup.
Conditional fields drastically reduce visual noise. Reps see only the fields relevant to the specific deal they are working.
Build conditionals after the base field set is stable. Conditionals on top of unstable fields create maintenance pain.
Document conditional logic somewhere outside Pipedrive (a Notion doc or shared sheet). When you forget why a field is hidden three months later, you will thank yourself.
Step 5
Pipedrive allows custom field groups (Power+) — visual sections that organize the sidebar. Without groups, fields appear in creation order, which is chaotic.
Company settings → Data fields → "Customize layout."
Create groups by workflow purpose: "Qualification" (Pain Point, Budget, Decision Maker), "Logistics" (Expected close, Contract term, Start date), "Source & Attribution" (Lead source, Campaign, UTM).
Order groups by frequency-of-use in the deal lifecycle: Qualification first (referenced every call), Logistics middle (referenced at proposal stage), Attribution last (referenced for reporting).
Drag-and-drop fields between groups. Save layout as the company default.
On Advanced/Professional (no group feature), order individual fields top-down by frequency-of-use. The top 5 fields should be the ones reps look at every day.
Step 6
Every custom field should have a description visible to all users. Future-you in 18 months will not remember what "Account intensity v2" means.
When creating a custom field, fill the description. Pipedrive shows the description on hover in the sidebar.
Format: "What this field captures + when to fill it + who owns it." Example: "Pain point details — fill during Discovery call. Owned by AE."
For dropdown fields, also document what each option means in an external doc (Notion, Google Doc). The options themselves cannot have descriptions in Pipedrive.
When you sunset a field, do not delete it immediately — rename to "[ARCHIVE] Old field name" and hide. Wait 90 days; if no one asks where it went, delete.
Run a quarterly field audit: Pipedrive → custom field → check fill rate. Anything under 20% fill after 90 days is either poorly explained or genuinely unused. Decide and act.
Common mistakes
Creating 30+ custom fields in the first week
What goes wrong: Sidebar becomes overwhelming. Fill rate drops to under 25% within 60 days. Reports built on these fields are unreliable because half the data is missing. You waste $200-400/mo of rep time on field maintenance that produces no signal.
How to avoid: Start with 5-10 custom fields max. Add more only when a specific report or workflow requires data the current fields cannot capture. Quarterly audit, prune low-fill fields.
Using Text fields for currency, dates, or options
What goes wrong: Cannot filter, sum, or report on the data because it is unstructured text. '$15K,' '15,000,' '15000,' 'fifteen thousand' all stored as different text strings. Reports show 4 deals at the same amount as 4 different categories.
How to avoid: Always pick the typed field: Monetary for money, Date for dates, Single/Multiple option for categorical data. Text only for genuinely free-form notes.
Making too many fields required
What goes wrong: Reps cannot create a deal until 8 fields are filled. They type 'TBD' into half of them just to get through the form. Required fields now contain junk data. Forecast accuracy suffers because Expected close = '01/01/2030' on 40% of deals.
How to avoid: Cap required fields at 3-5 per object. Reserve required for data that the record cannot exist without (Owner, Source, Email-or-Phone). Use Workflow Automation to enforce stage-transition requirements instead — those check at progression, not creation.
Duplicate fields that capture the same data
What goes wrong: You have 'Industry' (default), 'Vertical' (custom), and 'Account type' (custom) all capturing variations of industry. Reports pick the wrong one and show different splits depending on which the rep happened to fill. Marketing-sales handoff breaks because both sides assumed different fields were canonical.
How to avoid: Quarterly field audit. Pick one canonical field per data point. Migrate data to canonical (use Workflow Automation or bulk edit). Hide the duplicates.
Skipping field descriptions
What goes wrong: Every new rep asks the same questions in onboarding: 'what goes in Account intensity?' 'is Tier 1 a good thing or a bad thing?' Onboarding takes 3-4 extra hours per hire. Reps fill fields wrong because they guessed at meaning.
How to avoid: Description is the first thing you fill on every new custom field. Format: what + when + who.
Never running the field-fill audit
What goes wrong: Fields you created in year one have 8% fill in year two but nobody removes them. Reps spend cumulative hours per week on fields nobody uses. Add up across 10 reps and the wasted time is $1,500-3,000/mo.
How to avoid: Quarterly: Export deal list with all custom fields. Calculate fill % per field. Hide or archive anything under 25% fill after 90 days of existence.
Recap
Done — what's next
How to set up Pipedrive from scratch without rebuilding in 90 days
Read the next tutorial
Hand it off
Field design compounds. A clean field set holds up for years; a sprawling one creates a quarterly cleanup job forever. A Pipedrive specialist who has built 30+ accounts knows which fields are theater and which drive reports. EverestX specialists handle this at $14-16/hr — typically a $200-400 audit-and-rationalize engagement.
See specialist rates
Essential and Advanced: 30 custom fields per object (Deal, Person, Organization, Lead). Professional: 100. Power and Enterprise: unlimited. Important: just because you can have 100 does not mean you should. Most healthy accounts have 8-15 custom fields per object.
Usually no — most type changes wipe existing data because the storage format differs. Pipedrive will warn you. Limited exceptions: Text ↔ Large text usually preserves data; Single option ↔ Multiple options sometimes preserves data. Always export the field's data first as a CSV backup before any type change.
'Important' is a Pipedrive UI flag — the field shows up prominently in the sidebar but the record can still be saved without it. 'Required' (set via Workflow Automation on Professional+) actually blocks save until filled. Use Important for fields you really want filled; use Required (sparingly) for fields the record cannot exist without.
Yes. When importing a CSV (Tools and integrations → Import data), you map each CSV column to a Pipedrive field — default or custom. For Single/Multiple option fields, the CSV value must exactly match an existing option (case-sensitive) or the import fails for that row. Always run a 20-row test import first.
Two paths: (1) Deals list view → filter to the target set → select rows → 'Edit' menu → choose the field and the new value. Caps at ~5,000 records per bulk edit. (2) Export to CSV, modify the field column, re-import with 'Update existing items' enabled (matches on Deal ID). For 10,000+ updates, the re-import path is faster.
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