Most spreadsheet-to-CRM migrations fail not because the new platform is bad, but because the sales team never really leaves the spreadsheet behind. They run both in parallel forever, enter data twice, trust neither system, and you end up with a £2K/month tool nobody uses and a spreadsheet that still owns the truth. Three months in, you've lost deals to bad sync, hired someone who doesn't know where the real pipeline lives, and your CFO is asking why you're paying for two systems. The fix is a disciplined 90-day migration that treats the CRM switch like a product launch: planned phases, clear owners, training before go-live, and a hard cutover date. This playbook works for a typical 5-person sales team with 200 active prospects. Adjust the timelines by ±20 days if you're smaller or have messier data. Days 1–30: Clean and load your historical data Before you touch your new CRM, your spreadsheet data needs to be worse than useless—it needs to be honest . Most teams have contact records with no email, deals with missing close dates, and companies with three different spellings. Loading garbage in will just mean you clean it in the new system, wasting the first month. Week 1 of this phase is audit and planning. Export your current spreadsheet(s) in full. If you're running separate sheets for deals, contacts, and activities, export all three and note which columns are used and which are orphaned. Count your actual records. You probably think you have 200 prospects. Count rows. Then count unique companies. You likely have duplicates: "ABC Corp", "ABC Corporate", "abc corp". Audit contact fields. For each contact, you need: company name, contact name, email, phone (optional but useful), and role/title. That's the minimum for a CRM handoff. If your spreadsheet has 30 columns and half are empty or outdated, now is the time to drop them. Document your deal pipeline. List every stage you use: "Prospect", "Qualified", "Proposal", "Negotiation", "Closed Won", "Closed Lost". If you use stage names that mean something only to you ("Warm", "Hot"), now is the time to map them to stages the CRM will recognize. Week 2–3 is deduplication and standardization. Remove duplicates. If you have 5 records for "John Smith at Acme", keep one and merge the deal history into a single contact record. Most spreadsheet dupes are typos, inconsistent capitalization, or acquisitions you forgot to merge. Flag each one and decide: keep or delete. Standardize company names. Run a find-and-replace on common variants: "Inc." vs "Inc", "Limited" vs "Ltd", "&" vs "and". Create a standard name for each company and use it everywhere. Fix email and phone fields. Remove any test or internal emails (like "test@test.com" or your own address). If a phone number has formatting chaos, normalize it to a single format (E.164, or just "555-1234"). A CRM will refuse malformed data during import, so catch it now. Assign last-activity dates. In the CRM, you'll want to know when a deal was last touched. If your spreadsheet has an "Updated" column, keep it. If not, add one: "2024-01-15" for deals you worked last month, blank for ones you haven't touched in 6+ months. This matters for filtering "dead" prospects later. Week 4 is import and validation. Create a test import in the CRM. Load a small batch (20–30 records) and check: Did all the contacts land in the right fields? Did deal amounts import as numbers, not text? Did dates parse correctly? Fix the mapping and try again. This is your rehearsal. Load the full dataset. Once the test passes, import all 200 contacts and their deal history. The CRM will either accept them or reject them with a specific error. Fix errors in the source file and retry. Spot-check 10 random records in the CRM. Open them and verify: correct company, correct contact, correct deal stage, correct dollar amount, and correct last-touched date. If 9 of 10 are right, you're good. If 1 of 10 is right, stop and re-audit the source data. Data-cleaning checklist for spreadsheet export Before you import anything: A spreadsheet with garbage data will make your team distrust the CRM. One hour of cleaning now saves 10 hours of frustration later. Duplicate contacts: 1 record per person, 1 record per company Standardized company names: "Acme Inc", not "Acme Inc." or "acme incorporated" Valid emails: no "test@", no internal addresses, no typos Phone numbers in one format (or left blank if you don't have them) Deal stages match your chosen pipeline (no "Pending" and "In Progress" mixing) Deal amounts as numbers, not text ("10000", not "$10,000.00") Close dates in YYYY-MM-DD format or blank if unknown Last-activity date for every contact (so you can identify stale leads) Company size, industry, or location fields if you use them (optional, but useful for reporting) No orphaned columns: if a column is 90% empty, delete it Days 31–60: Run dual-entry and train the team Now that your data is in the CRM, the sales team needs to live in it, not in the spreadsheet. B