You've got 2,000 contact rows in a spreadsheet. Names, emails, phone numbers, maybe a column for "last contact date" and another for "status." You decide to move to a real CRM. You export the CSV, upload it, and expect to see your entire customer history waiting for you on the other side. Then you log in and realize: half your context is gone. The spreadsheet-to-CRM migration is deceptively simple at first glance and brutally messy in execution. Contacts themselves transfer fine. Everything else—deal progression, interaction history, relationship context, and the unstructured notes that actually drive your business—either survives in fragments or dies entirely. Most teams discover this too late, after they've already committed to the new platform. Here are the four data types that break during migration, why they break, and what you can do about it. 1. Deal History and Pipeline Stage Progression A spreadsheet typically captures current status —a single column with values like "prospect," "qualification," "proposal," "closed." That's a snapshot. Your actual deal work is the journey: when it moved from prospect to qualification, who pushed it, what changed, why it stalled for three months in early talks. Most spreadsheets don't record that history at all. You might have one cell saying "Proposal sent on 15 Jan," buried in a note field. When you migrate, you have two choices: Migrate current status only. You lose the timeline. Your CRM shows every deal as if it started today at its current stage. You cannot see velocity, cannot reconstruct why a deal fell apart, cannot recognize patterns. Manually recreate stages from notes. This works for 50 deals. For 500, it is not a workable path. Some platforms (including Orin's CRM ) let you map historical stage changes into activity logs during import, so you preserve a rough timeline. But most standard CSV uploads cannot. The stage progression—the actual sales motion—is lost. Fix: Before you migrate, audit your spreadsheet for stage transition dates. If you have them buried in notes, extract them into a separate column. If you don't have them, accept that you're starting from today forward. Do not try to backfill two years of deal history from memory; you will invent data, and invented data is worse than no data. 2. Interaction and Communication History Your spreadsheet has a "notes" column. It might say "Called 10 Dec, interested in Q3 pilot. Email sent about pricing. Follow up after New Year." That's the entire conversation thread with a customer, compressed into one cell, written in whatever shorthand your sales team used that day. A real CRM captures interactions as discrete records: call logs, email threads, meeting notes, tasks with outcomes. When you migrate from a spreadsheet, that single notes cell becomes a single note activity. You lose granularity. You cannot see the email thread itself. You cannot filter "all calls with this customer in the last 60 days" because the calls are not logged as calls—they're text in a note. And if your communication actually happened in Gmail, WhatsApp, or Slack, it is not in the spreadsheet at all. It is scattered across three different apps with no connection to the contact record. Fix: Before you migrate, decide: are you archiving the spreadsheet notes as context, or are you starting fresh from your migration date? If you have email threads or WhatsApp conversations, use your new CRM's unified messaging inbox to pull in historical threads. Some platforms can backfill recent communication from your email server. Do not try to manually copy-paste emails into CRM note fields; you will create an unmaintainable mess. 3. Relationship Mapping and Account Hierarchy A spreadsheet treats each row as an island. "John Smith" is a contact. "Sarah Chen" at the same company is another contact. You might have a column for company name, but the CRM has no way to know that they work together, that John is the decision-maker and Sarah is the influencer, or that Sarah's answer depends on John's approval. Real CRMs support account hierarchies: one company record, multiple contacts, roles, relationships between them. A spreadsheet cannot express this structure in a way that a CRM can automatically parse. During migration, you get two outcomes: Duplicate company records—one for each contact. Flat contact lists with company names pasted in but no relational structure underneath. Neither is workable at scale. You end up spending months after migration manually rebuilding your account structure, reassigning contacts to the right company records, and re-documenting who reports to whom. Fix: Before migration, create a clean company list. Deduplicate it ruthlessly. Then map your contacts to companies with a clear company ID or name. During import, import companies first, then contacts linked to them. Most platforms require this order; if you import contacts first, you will create duplicates and have to clean them up by hand. Allocate 2-4 hours per 500 c