How to cut RFQ response time from 2 days to 2 hours
Most of a two-day RFQ response is waiting, not work: the request sits in an inbox, then waits on a price or lead-time lookup, then waits for someone to send it. You cut response time to about two hours by drafting the quote the moment the RFQ arrives, pricing it in code from your own price sheet, flagging only the unclear lines, and giving reps a short approval window. The rep still approves every quote; they just stop retyping it.
Why does a quote take two days when the work takes 30 minutes?
Ask an inside sales team how long a typical quote takes to build and you will often hear 15 to 30 minutes. Ask how long customers wait and the answer is closer to one or two business days. The gap is queue time: the RFQ lands while the rep is on the phone, gets read after lunch, waits for purchasing to confirm a lead time, and goes out the next morning.
That matters because buyers do not wait politely. In a 2020 survey of part buyers by Paperless Parts, 67% said they expect a quote within 24 hours. Harvard Business Review's research on online leads found that firms that contacted a lead within an hour were nearly 7 times as likely to qualify it as firms that waited longer. Speed is not a nice-to-have in quoting, it is often the deciding factor.
So the fix is not to make reps type faster. It is to remove the waiting between steps, which is exactly the part software is good at.
Where does the time actually go? A worked example
Here is an illustrative distributor. The numbers are an example, not data from a real customer, but the pattern is common. Assume an RFQ with eight lines arrives by email at 9:00 on a Tuesday.
| Step | Before (elapsed hours) | After (elapsed hours) |
|---|---|---|
| RFQ waits in the inbox until a rep opens it | 24 | 0.1 (draft is created automatically in about 5 minutes) |
| Price and lead-time lookup, waiting on purchasing | 20 | 0 (calculated in code from the price sheet) |
| Build the quote, check, send | 4 | 0 |
| Rep resolves flagged lines | Included above | 0.4 |
| Quote waits in the approval queue | Included above | 1.5 |
| Total | 48 | 2.0 |
Illustrative example. Before: 24 + 20 + 4 = 48 hours. After: 0.1 + 0 + 0 + 0.4 + 1.5 = 2.0 hours.
How much rep time does that free up?
Response time and rep time are different things, and both improve. Keep the same illustrative distributor: 60 RFQs a week, 25 minutes of hands-on work each when built manually. That is 60 × 25 = 1,500 minutes, or 25 hours of rep time every week.
If a pre-built draft takes about 5 minutes to review and approve, the same volume needs 60 × 5 = 300 minutes, or 5 hours. The difference is 25 − 5 = 20 hours a week, roughly half a full-time inside sales role, returned to calling customers and chasing open quotes. You can run your own numbers in the RFQ response time calculator.
What are the steps to get to two hours?
- Measure your baseline: pull the last 50 RFQs and note when each arrived and when the quote went out. Use the median, not the average, so one stuck quote does not skew it.
- Clean up your price sheet: one Excel or CSV export with part number, description, quantity breaks, customer-specific prices and lead times. This is the single source of truth for every quote.
- Route every RFQ to one shared mailbox, including web-form submissions your site emails to you.
- Draft automatically: have every incoming RFQ turned into a draft quote within minutes, at any hour, with prices and totals calculated from the sheet.
- Flag, do not guess: unclear, unmatched or missing-spec lines, large quotes, first-time customers and low-stock items go to a rep with the reason shown.
- Set an approval target, for example every draft reviewed within 90 minutes during business hours, and make one person accountable for the queue.
- Follow up on open quotes on fixed days, such as day 3 and day 7, and track the median response time weekly.
What should stay human?
The approval. A rep should see every quote before it reaches a customer, especially at the start. That is how you catch the unusual request, the customer who needs a call instead of a PDF, or the substitute part that only your senior rep knows about.
With Merlin's Quote Desk, the AI's only job is choosing which catalog item matches each request line. Prices, totals and lead times are calculated in code from your price sheet, and a human approves every quote. When a rep corrects a match, Merlin remembers it as a learned alias, so the next request using that customer's wording matches correctly.
How do you prove it works before committing?
Run in shadow mode first. For one to two weeks, the system drafts quotes on your real RFQs while nothing can be sent, and your team compares each draft with the quote they actually sent. You get evidence on your own data rather than a demo.
Then agree on one success metric in writing, such as median quote response time under one hour, and measure it during a 30-day pilot. See pricing for how the Merlin pilot ties the monthly fee to hitting that metric, and quote response time statistics for more context on buyer expectations.
What mistakes slow teams down even after automating?
The most common one is letting drafts pile up. A draft that is ready in five minutes but approved the next afternoon has saved typing, not time. Name one owner for the approval queue each day and check it at fixed times, for example every hour on the hour.
The second is an outdated price sheet. If lead times in the file are three months old, reps stop trusting the drafts and go back to rebuilding quotes by hand. Refresh the sheet on a schedule, or publish it from your ERP to a link that refreshes automatically.
The third is treating every flag as a failure. A flagged line is the system doing its job: telling you it is not sure. Over the first weeks, each correction becomes a learned alias, and the share of flagged lines usually drops as the system learns your customers' wording.
Frequently asked questions
Is two hours realistic for complex RFQs?
For RFQs where most lines are on your price sheet, yes, because the draft is ready in minutes and the rest is review time. Lines needing engineering input or a supplier quote will take longer; those get flagged so the rest of the quote is not held up behind them.
Should I measure average or median response time?
Median. One quote stuck for a week can double an average and hide real progress. Track the median weekly and also watch the share of quotes sent within 24 hours.
Does faster quoting mean less accurate quoting?
It should not. When prices and totals come from your price sheet in code and a rep approves each quote, accuracy usually improves because nobody is retyping numbers.
What about RFQs that arrive at night or on weekends?
An automated desk drafts them as they arrive, 24/7, so the quote is waiting for approval when your team starts the day instead of joining the back of the queue.
Do I need to change my ERP or quoting process to do this?
No. You keep your ERP and your approval habits. What changes is that the first draft is built for the rep instead of by the rep, from a price sheet exported as Excel or CSV.
