Parse RFQ Emails Into a Quote Request Spreadsheet

For distributors, fabricators, and anyone who sells configured products, the quote request is the top of the revenue funnel. It also arrives in the messiest possible form: a plain email to sales@, sometimes with a line-item table pasted in, sometimes as three sentences of prose, sometimes as a forwarded chain from a buyer who copied the specs out of their own system. Response speed decides who wins the order, and speed dies in a shared inbox where quotes are tracked by memory.

How do I track RFQ emails in a spreadsheet?

Connect the inbox that receives quote requests and map the fields you quote on: company, contact, part or SKU, quantity, requested delivery date, and any special terms. The parser writes one row per request. Your sales desk works a sorted list with deadlines instead of scrolling an inbox, and nothing quietly ages past the response window.

What is an RFQ email?

An RFQ, or request for quotation, is an email in which a buyer asks a supplier to price specific goods or services, usually naming quantities, specifications, and a date they need the answer by. It differs from a general inquiry because the buyer has already decided what they want and is comparing prices. That makes it the highest-intent message in a sales inbox and the one worth structuring first.

Can a parser read a line-item table pasted into the email?

Yes. When a buyer pastes an HTML table of parts and quantities into the message, a parser reads it as rows, so a ten-line RFQ becomes ten spreadsheet lines tied to the same request. That is the single biggest time saver in quoting, because retyping part numbers is exactly where transposition errors creep in. See extracting a table from an email for how table capture works.

What if the specs come as a PDF attachment?

Be clear on the boundary. MailParse reads the email message: body text and HTML tables become named fields, and each attachment is recorded by filename, type, and size so you keep the link to the original file. Reading the specifications inside an attached PDF or drawing is document extraction and needs a separate document tool. In practice most RFQ emails carry the commercial details (who, what, how many, by when) in the body, which is enough to log, prioritize, and route the request while the engineer opens the drawing.

How does this speed up quote response time?

Two ways. First, the request is triaged the minute it arrives instead of when someone reads down the inbox, so an urgent bid is visible immediately. Second, the person quoting starts from structured values rather than rereading the email, which removes the slowest part of the job. Teams that measure response time usually find the delay was never in the pricing, it was in the handling before pricing started.

How do I avoid missing a bid deadline?

Put the parsed due date in a real date column and sort by it. Once the request is a row rather than an email, an overdue filter is trivial, and you can flag anything unanswered within your target window. This is the same reason procurement teams keep purchase orders in a system rather than a mailbox; the structure is what makes the deadline visible. Related reading: extracting purchase order data from emails.

Should the RFQ go to a CRM or a spreadsheet?

Both work, and the choice depends on who needs it. A spreadsheet is faster to start with and easier to hand a sales desk that already works in Excel. A CRM is better when the quote needs to attach to an account record and roll into a pipeline forecast. The parsed record is the same either way; you are only choosing the destination. See email to CRM for the push option or email to Excel for the sheet.

What about the inquiries that never become RFQs?

A large share of inbound messages are questions rather than priced requests: lead times, compatibility, minimum order quantities. Those still deserve a fast answer, and answering them earlier in the buying process is often what earns the RFQ in the first place. Many suppliers put a chatbot trained on their own catalog and documentation on the site so routine product questions get answered instantly, leaving the sales desk to handle real quotes.

Getting started with RFQ email parsing

Pull thirty recent quote requests and look for the fields that appear in almost all of them. Those are your columns. Connect the sales inbox, map buyer, item, quantity, and deadline first, and add the nice-to-have fields once the core is reliable. Export to Excel or CSV for the quoting desk, and read extracting order data from confirmation emails for the downstream half of the same pipeline.