We move teams off Google Sheets onto a real CRM. The work is straightforward when we agree on the structure up front: one Attio record type per tab in your sheet, real links between them where Sheets used repeated columns, and clean data filled in as we go. We finalize the model on paper before any data moves.
Last verified June 2026
Why teams move
Sheets works at some scale. It breaks at others. The break usually shows up the first time the team needs cross-tab reporting, fund-aware permissions, or an audit trail of who changed what when.
01 · Relationships
Sheets stores cross-object context in repeated columns: Deals has a company name, Companies has the same company name, People has it again. Attio makes the relationships explicit references, which is what unlocks the reporting partners actually want.
02 · Permissions
Sheets either shares everything or nothing. Fund-aware visibility, LP confidentiality, partner-only notes: none of it models in a spreadsheet. Multi-fund VCs feel this first.
03 · Workflow
Stage triggers, task creation, intake forms, email and calendar sync, enrichment: these are CRM-native. In Sheets, the team builds the workflow by hand every Monday morning.
Tab by tab
One Attio object per tab is the default mapping. Each tab becomes the source for one object; rows become records; columns become attributes. The standardized taxonomy: Direct mapping (carries over as-is), Caveats (needs a schema decision or workaround), Unavailable (cannot transfer; plan around it).
What's different
The Companies tab is usually the most populated sheet. It often lacks domains, LinkedIn URLs, industry, or headcount, and company names may differ across tabs (Acme vs. Acme Inc.).
How we handle it
We import these rows into Attio as company records. When enrichment is part of the plan, Apollo or Clay can help fill missing domains, LinkedIn URLs, industry, and headcount. Because domain is the main key for company deduplication, we work to land it on as many records as possible before import.
What's different
Deal rows usually include stage, owner, check size, dates, and notes. They often reference companies by name rather than by a stable identifier.
How we handle it
We migrate deals into Attio's standard Deal object or a custom Deal object, with a proper reference back to the matched company. If stage and pass reason were combined in one sheet column, we split them into separate Attio attributes so reporting can separate current pipeline position from rejection reason.
What's different
The People tab often includes founders, board members, advisors, GPs, LPs, and scouts. Email is the strongest key for person deduplication, but it is sometimes missing.
How we handle it
We import these rows as Attio person records. When enrichment is part of the plan, Apollo can help fill missing email and LinkedIn data. We then connect people back to companies through the resolved company match.
What's different
Some VC spreadsheets use one tab per fund, such as Fund I, Fund II, and Fund III. Each fund may include vintage, size, status, thesis, and LP commitment information.
How we handle it
We represent funds as custom Fund records in Attio. Deals and LPs then reference the relevant Fund record. A tab-per-fund workbook becomes one Fund object with one record for each fund.
What's different
LP tabs usually track limited partners, commitments, close dates, outreach status, and contact details. This information is often sensitive.
How we handle it
We represent LPs with a custom LP object related to Funds and Commitments. Sensitive fundraising details live behind list-level access controls so analysts and external admins do not see them by default.
What's different
Most spreadsheet CRMs store context in unstructured note columns. Those columns may contain partner observations, last-meeting notes, or follow-up reminders.
How we handle it
We migrate those values as Attio notes attached to the main record they describe. Once inboxes are connected, future notes can live in Attio's native note and meeting flow.
What's different
The same company may appear as "Acme" on the Deals tab, "Acme Inc" on the Companies tab, and "Acme Co" on the People tab. Domain can resolve many of those cases when it is present, but name-only matches require more review.
How we handle it
We combine fuzzy name matching with domain matching where domains exist. AI-assisted matching can help at volume, and unclear matches go through manual review. We resolve these relationships before import so Attio starts with clean company, person, and deal links.
What's different
Spreadsheet formulas such as VLOOKUP, INDEX/MATCH, QUERY, and IMPORTRANGE often act as the glue between tabs. They make the sheet behave more like a database, but they are not CRM records themselves.
How we handle it
We do not migrate formulas as formulas. Attio represents the underlying relationships with reference attributes, while saved filters, automations, and custom integrations replace the spreadsheet logic where it is needed.
What's different
Google Sheets has version history, but it does not expose per-cell change history in a way that can be imported into Attio. That history cannot become native Attio record history.
How we handle it
We archive the source workbook as the historical reference. Attio's native audit log begins when the migrated workspace starts operating in Attio.
Migration playbook
Sheets-to-Attio is straightforward once the schema is decided. The upfront data review makes that decision, and the remaining work covers the import path, enrichment, matching, references, and UI.
One Attio object per tab is the default. Refining that into reference attributes and collapsing redundant columns is the first deliverable.
We run Apollo or Clay during the import, not after it. Sheets data is usually sparse, and enrichment helps Attio records land with domain, LinkedIn, industry, and headcount.
Domain is the unique key on Companies. We make sure it exists on as many records as possible before the import completes; everything else follows.
We map every tab in your workbook to an Attio object, typically Deals, Companies, People, Funds, and LPs. We decide which columns become reference attributes between objects and which stay local attributes on the source object, and we make those decisions before any data moves.
We choose the import path based on volume and schema complexity. The CSV importer fits low-volume Sheets with a straightforward schema, and the API fits high-volume or schema-complex Sheets where relationships and validation are easier to script.
We run Apollo or Clay during the import rather than after it. Sheets data is usually sparse, and enrichment helps each Attio record land with domain, LinkedIn, industry, headcount, and the other attributes the team will actually use.
We resolve cross-tab name mismatches like "Acme" versus "Acme Inc" versus "Acme Co" before they reach Attio. We combine fuzzy matching with domain matching where domains exist, use AI-assisted matching at volume, and pull anything unclear into a manual review queue.
We wire reference attributes between Deals, Companies, People, Funds, and LPs. The spreadsheet approach hides relationships inside repeated columns, but Attio makes them explicit references, and that explicitness is what unlocks cross-object reporting.
We configure the lists, views, and operating surfaces partners use every week. We build the day-one automations for stage triggers, task creation, and notifications, and we stand up the integrations the team needs at launch. Deeper work belongs to phase two.
Enrichment + matching
Sheets data is usually sparse. Company name only, no domain, no LinkedIn, no industry. The Attio data model wants more. We bridge the gap during import, not after, so the records land clean.
Enrichment
Both work for our purposes. We pick based on the team's existing tooling, the data shape of the Sheets, and the depth required. Either lets us go from company name to company name plus domain plus LinkedIn plus industry plus headcount in the same step as the import.
Matching
For cross-tab name mismatches and ambiguous records, we use AI-assisted matching at volume and Attio's record search for one-offs. The records that do not resolve cleanly get manual review. The goal is final records that are unambiguous from day one.
Guides
Eight questions we hear most often from teams running their CRM out of a workbook.
Yes. The shape of the work is the same as any CRM migration: schema design first, then import, then connect objects, then enrichment and cleanup. The difference is that Sheets has no schema by default. The first outcome of the engagement is the Attio data model the team will use, and that decision is made during discovery and the data review rather than inside the import script.
Yes. We have seen VC firms with over a billion dollars in assets under management running their entire deal flow out of a Google Sheets workbook. It works at some scale, breaks at others, and the team usually only realizes it broke once they need cross-tab reporting, fund-aware permissions, or a real audit trail. Sheets-to-Attio is one of the most common migration shapes we run.
One Attio object per tab is the default mapping. For a VC, that usually means: a Deal object, a Company object, a People object, a Fund object (sometimes one tab per active fund), and an LP object. Each tab becomes the source for one object, with rows becoming records and columns becoming attributes. The data model gets cleaner when we collapse repeated context across tabs into reference attributes instead of duplicated columns.
Depends on volume and schema complexity. Low-volume Sheets with a straightforward schema usually move through Attio's CSV importer because it is faster to set up and the team can verify a sample easily. High-volume Sheets, or Sheets where the schema needs reference attributes between objects, move through the API because the relationships and the validation are easier to script.
This is the common case. The Deals tab might list a company as 'Acme', the Companies tab as 'Acme Inc.', and the People tab as 'Acme Co.' We resolve these through a mix of automated matching (fuzzy match plus domain match where possible) and manual review for the records that do not have a clean match. Where the volume is high, we use AI-assisted matching and Attio's record search to find the right canonical record.
During the import, not after. Sheets data is usually sparse: company name only, no domain, no LinkedIn, no industry. We run enrichment (Apollo or Clay) at import time so the Attio records land with domain, LinkedIn URL, industry, headcount, and the other attributes you would want from day one. Domain is the unique key on Companies; we make sure it exists on as many records as possible before the import completes.
Once the migration is done, we configure the UI (lists, views, saved filters, the operating views partners actually use), build the day-one automations (stage triggers, task creation, Slack or email notifications), and stand up any integrations the team needs on launch. Deeper integrations or reporting beyond Attio's native dashboards typically belong in a phase-two engagement.
Clean Sheets with a small number of tabs, decisive owners, and well-formed company names land in 1 to 2 weeks. Sheets with heavy mapping, mismatched company names, many tabs to consolidate, or multiple funds tracked across spreadsheets take 3 weeks or more. The upfront data review produces the defended estimate before the project starts.
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Book a 30-minute discovery call. We will walk through your tabs, the data model that fits the business, and a realistic timeline.