Messy scraper exports when you sell to cafes
Does your scraper export have columns called field1, field2 and field3? Here is how people who sell to cafes fix it.
Does your scraper export have columns called field1, field2 and field3?
Browser scrapers and directory exports produce files with odd column names, several URL columns and a lot of noise. Turning that into a usable list takes work.
It hits harder when you sell to cafes. Cafes are often owner operated with a small team, and the owner is usually behind the counter or the coffee machine. They buy coffee, milk, cakes and equipment regularly, and switching supplier depends on taste, service and a good personal relationship.
Raw exports stay unused on your desktop. The time you saved by scraping is lost again on cleaning.
From raw export to clean records in one import.
Riftjaw reads CSV with comma or semicolon and Excel .xlsx files. You map each column to a field and, when there are several URL columns, you pick which is the website and which is something else. Records start as concepts, so you can review before publishing, and an import can be deleted.
- Fields that fit cafes, such as coffee brand
- Email prefixes that fit, like hello@, info@, coffee@ and orders@
- Status, tags and a segment on every record
How to fix it in Riftjaw
- Upload the export. Drop in the CSV or Excel file straight from your scraper.
- Name the columns. Map each column and choose which URL column is the website.
- Review the concepts. Check the records, then publish or delete the import.
Other walls when prospecting cafes
Cafes and restaurants blur together in scraped listings.
Use a field for business type and a saved filter so your cafe campaign only reaches cafes.
Owners reply by phone or in person, so email history is incomplete.
Add notes to the contact after every call or visit and update the status by hand.
You pitch the same cafe twice because two reps kept their own spreadsheet.
Everyone works in one database with duplicate checks on import and unlimited users on an account.
Fields worth recording for cafes
Add these as fields in your lead file. Any column in your import can become a field.
| Field | Why it helps |
|---|---|
| Company name | The key field. Duplicates are checked on it or on the website. |
| Website | Needed to derive an email when the export has none. |
| Coffee brand | The roaster they use tells a roaster or machine supplier where they stand. |
| Food offer | Cafes that serve food buy from bakeries and food wholesalers. |
| Seats | Size shows likely volume of coffee and cups. |
| Machine | The espresso machine they run matters for service and replacement offers. |
| Takeaway share | High takeaway volume means more cups, lids and packaging. |
Questions people ask
Import a list of cafes from public listings, add fields for coffee brand and machine, and put the cafes not using your beans yet in their own segment. Then send a short campaign offering a tasting.
Yes. Add a note on the contact after each visit and change the status. Email history is logged automatically next to your notes.
If your file has an email column, that wins. If it only has websites, tick the prefixes you want, such as hello@, info@, coffee@ and orders@, and Riftjaw derives addresses from each domain. Derived addresses are good guesses, not verified, so start with small batches.
Yes. The Free plan runs the whole pipeline on one list with small campaigns, and you do not need a credit card. Move to Pro when you want more lists, segments and bigger campaigns.
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