Migrate from a spreadsheet (Excel or Google Sheets) to Thawly

Move your lost deals from Excel or Google Sheets to Thawly in 10 minutes. If your team never adopted a CRM — or if you've already exported from one and tidied the data in a spreadsheet — this is the simplest route. Thawly's importer is built around CSV upload, and Excel and Google Sheets both produce CSVs cleanly.

What you need#

• An Excel workbook (.xlsx, .xls) or a Google Sheets tab containing your lost-deal records.
• A modern browser. No add-ins, no Power Query, no Apps Script.
• About 10 minutes.

You don't need any specific column structure — Thawly's importer auto-maps any reasonable header. The only hard requirement is a column with the company name on every row. Everything else (deal value, lost date, lost reason, notes) is helpful but optional.

Step-by-step extraction#

From Excel

1. Open your workbook in Excel — desktop or Excel on the web both work.
2. If your data spans multiple sheets, copy the lost-deals tab into a new workbook so you've got one tab with the rows you want to upload. Mixing won/open/lost rows on a single tab and relying on Thawly to filter them is a recipe for mis-imports.
3. Make sure the first row contains column headers (e.g. Company, Value, Lost date, Lost reason, Notes). Thawly's importer reads the first row as headers and auto-maps from there.
4. Sanity-check the data:
   • Company column is populated on every row (rows without a company name get rejected on the preview screen).
   • Date columns use a sensible format — Excel handles 2025-03-14, 14/03/2025, Mar 14 2025 and similar variants. Avoid Excel's number-encoded dates (45739) which sometimes leak through; convert to a text date first if you spot any.
   • Value column is plain numbers (12500, not £12,500.00 or 12,500 GBP). Strip currency symbols and thousand-separators in Excel via Find & Replace before exporting.
5. Use File → Save As → CSV UTF-8 (Comma delimited) (*.csv). [Screenshot: Excel Save As dialogue with CSV UTF-8 format selected]
6. Don't use the legacy "CSV (Comma delimited)" option — it drops the BOM and mangles non-ASCII characters in company names like Tewkesbury Group Ltd if any of them have unusual characters (accents, ampersands, em-dashes).
7. Save the file somewhere easy to find on your machine.

From Google Sheets

1. Open your Google Sheet at sheets.google.com.
2. If your data spans multiple tabs, isolate the lost-deals tab — duplicate it into a fresh sheet (right-click the tab → Duplicate) and remove the other tabs. Cross-tab data is a common source of upload errors.
3. Make sure the first row contains column headers. Same rules as Excel — concrete headers (Company, Value, Lost date, Lost reason, Notes) make Thawly's auto-mapping more reliable than ambiguous ones (Col A, Field 2).
4. Sanity-check the data:
   • Company column populated on every row.
   • Dates in a parseable format (Sheets is more flexible than Excel — most common formats work).
   • Values as plain numbers (use Format → Number → Plain number to strip Sheets's currency formatting).
5. Use File → Download → Comma-separated values (.csv). [Screenshot: Google Sheets File menu with Download → CSV highlighted]
6. The file downloads directly to your machine. Open it in a text editor (or Excel) just to glance at the row count if you want — usually it's exactly what was on the visible tab.

Field mapping#

Thawly's importer auto-maps any reasonable header. For reference:

• Company (or Account, Organisation, Customer) → name (used for Companies House matching)
• Value (or Amount, Deal value) → deal_value (GBP — strip currency symbols)
• Lost date (or Close date, Date lost) → lost_date
• Lost reason (or Reason, Why we lost) → lost_reason
• Competitor (or Lost to, Won by) → lost_to
• Notes (or Description, Comments, History) → notes

If your column headers don't match any of these patterns, the preview screen lets you map them manually before importing — no need to rename in Excel first.

What to do with the Notes column#

Don't pre-clean. If you've kept a Notes or History column in your spreadsheet — call summaries, follow-up history, AE freeform thoughts — paste it in raw. Thawly's AI summarises long notes and extracts structured signals (objection type, decision-maker title, competitor name, budget threshold) on its own. Editing notes by hand to make them "tidy" before upload is wasted work and usually throws away signal.

If your notes live in a separate document (a Word doc, a Notion page, a folder of OneNote files), the simplest approach is to copy-paste the relevant chunk into a Notes column in your spreadsheet keyed by company. Thawly's AI works on whatever you give it — even a single freeform paragraph per row produces useful drafts.

Common gotchas#

• Duplicate companies. Spreadsheet exports often have several rows for the same account (Marsden Ltd lost three times across 2024). Thawly de-duplicates on lower-cased company name on import — only one company entry is created. The deal-history rows feed into the AI's context, but the "company" appears once.
• Currency mismatches. Spreadsheets don't enforce a currency. If your team has worked in USD for some deals and GBP for others, either convert all values to GBP in Excel before uploading, or accept that Thawly's prioritisation will be skewed by the mixed numbers.
• Multi-tab contamination. The single most common upload error is uploading a CSV that includes won deals or open deals because the source spreadsheet had them on the same tab. Filter aggressively in Excel/Sheets first — drop the won rows, drop the open rows, then export.
• Date format quirks. Excel sometimes encodes dates as numbers (45739 is March 14 2025 in Excel's date system). Format the date column as Short Date before exporting, or convert to text via the TEXT(A2, "yyyy-mm-dd") formula. Thawly's importer handles most date formats but rejects raw Excel numeric dates because they're ambiguous.
• Company names with quote marks or commas. CSVs use commas as separators and quotes as field delimiters. Names like Brackenfield Estates, Ltd or Marsden "Industries" Ltd need to be wrapped in quotes (Excel does this automatically when saving as CSV UTF-8). If you've hand-edited the CSV in a text editor, double-check the escaping.
• Stale "won" or "open" deals. Sanity-check that every row in your CSV is genuinely a lost deal. Mixing in a few open deals doesn't break anything technically, but Thawly will start monitoring them as if they were lost — which produces noise in your digest.

What happens next#

Drop the CSV at thawly.co.uk/upload. We auto-map the columns, run a Companies House lookup on every company name and show you a per-row preview before importing. The preview is where you fix any bad matches, drop rows you don't want, or remap columns if the auto-mapping picked something unexpected.

After import, monitoring runs on the next signal-source pass. Your first digest only lands when there's a real signal — see Reading your digest for what to expect.

What if my spreadsheet is messy?#

It's almost always messier than you think it is, and that's fine. Thawly's preview screen is built for this — bad matches, missing dates, inconsistent currency, blank lost reasons all show up flagged on the preview, and you fix them in one click. Don't spend two hours tidying the spreadsheet before uploading; spend ten minutes uploading and use the preview to clean up the issues that actually matter.

If your spreadsheet is genuinely too messy to be useful (e.g. company names in free-text alongside other content, no consistent column structure), the fastest fix is usually to copy the company-name column to a fresh spreadsheet, export that as a one-column CSV, and upload it. Thawly will accept a CSV with just a name column and start monitoring — you can fill in the deal value, lost date and notes later via the companies manager.

Coming from a CRM after all?#

If your spreadsheet was exported from a CRM, the per-CRM guides have specific tips that beat the generic advice here:

• Migrate from HubSpot
• Migrate from Salesforce
• Migrate from Pipedrive
• Migrate from Zoho CRM
• Migrate from Microsoft Dynamics 365 Sales

For the bigger picture on why monitoring lost deals works, read Dead deal recovery and Buying signals in B2B sales.