Slow quotes do not lose deals loudly. Nobody calls to say they went with the other supplier because you took three days. The RFQ just goes quiet, and three weeks later somebody closes it in your CRM as no decision. The deal that was lost on turnaround looks exactly like the deal that was never real.
Three suppliers, one RFQ
A buyer at a contract manufacturer needs fourteen line items. Some are stocked commodity parts, two are fabricated, one has been obsolete since last year and needs a substitution. She writes one email, attaches one spreadsheet, and sends it to three suppliers at 10:20 on a Tuesday.
One of them replies the same afternoon. The quote is not the cheapest. It is complete, it has lead times against every line, and it says plainly that line nine is obsolete and here is what it would be replaced with.
Think about what that quote has just done. It has given her something to compare the other two against. It has told her which lines are the risky ones. It has set the format of her comparison — she now has a spreadsheet with columns that match its layout. When your quote arrives on Thursday, it is not being read fresh. It is being checked against somebody else’s answer.
That is the real cost of being third. Not that she has already decided; that she has already framed the decision, and you did not get to do the framing.
Why your quotes are slow
Almost never because the people are slow. Time your own process twice — once for the steps, once for the gaps between them — and it is the gaps that are worth arguing about. There are four usual suspects, and effort is not one of them.
Pricing lives in three places. There is the price list, which is a file. There is contract pricing, which is in the ERP, mostly. And there is the deal somebody did last quarter, which exists in an email thread and in one person’s memory. To quote fourteen lines confidently, a rep needs all three, and the third one cannot be looked up.
One person knows the odd items. The fabricated parts, the obsolete substitutions, the item where the catalogue is wrong and everybody has agreed to work around it. That knowledge is real and valuable and sitting in one head, and that head is in a plant walkthrough until Thursday.
You cannot quote a lead time you do not have. So an email goes to a supplier, and then you wait, and the clock is now being run by somebody who does not work for you and has no idea a deal is attached to it.
And the queue is invisible. RFQs arrive faster than quotes get built, so they stack up. Nobody can see the stack. There is no screen anywhere in the business that says how old the oldest unquoted RFQ is, which means nothing gets triaged and the small easy ones wait behind the big hard one.
Measure your own decay
You will find plenty of published figures for what a day of delay costs a win rate. Do not use them. They come from other people’s industries, other people’s deal sizes, and other people’s definitions of a quote. Your own quote history is sitting in your ERP or your CRM right now and it is a better source than any of them.
Here is a method an operations person can finish in an afternoon.
- Export twelve months of quotes. Three fields matter: the date the RFQ arrived, the date you sent the quote, and whether you won it. Pull value and product family too — you will need them in step four.
- Work out business days to quote. Not calendar days. An RFQ that arrives Friday afternoon and is quoted Monday morning is one business day late, not three, and if you use calendar days every Friday RFQ will pollute your slow buckets.
- Bucket by turnaround and compare win rates. Same day, one day, two or three days, four days and over. Count the quotes in each bucket and count the wins. The win rate per bucket is the whole output.
- Segment before you believe any of it. This is the step everyone skips and it is the one that matters. Big complex jobs take longer to quote and are harder to win. If your slow bucket is mostly your hardest work, the curve you are looking at is measuring difficulty, not speed. Split the data by value band, or by product family, and look at the slope inside each band separately. If it survives inside a band of similar jobs, you have found something real.
- Read the slope off it. Take the win rate in your fastest bucket and the win rate one day slower. The drop between them, expressed as a share of the faster rate, is your decay per day. That relative figure is what the calculator below asks for.
Two things will distort it, and you should know they are in there. Quotes you were asked for as a third bid, to satisfy somebody’s procurement policy, were never winnable and will flatten every bucket they sit in. And no-decision RFQs — where the buyer’s project died — are not losses to you at all. If you can tag either group, exclude them. If you cannot, at least know the curve you are reading is conservative.
| Quote | RFQ received | Quote sent | Business days | Value | Product family | Won |
|---|---|---|---|---|---|---|
| — | — | — | — | — | — | — |
| — | — | — | — | — | — | — |
| — | — | — | — | — | — | — |
| Business days to quote | Quotes sent | Won | Win rate |
|---|---|---|---|
| Same day | — | — | — |
| One day | — | — | — |
| Two or three days | — | — | — |
| Four days and over | — | — | — |
If the slope is flat inside every band, that is a genuinely useful answer: turnaround is not your problem and you should go and work on something else. A measurement that tells you to stop worrying has earned its afternoon.
Put a number on the gap
Once you have a decay figure, pricing the gap is arithmetic. The calculator assumes each extra business day of delay multiplies your win rate by one minus your decay — so the win rate you would have at your target turnaround is your win rate today, divided by one minus decay, raised to the number of days you save.
It is a deliberately simple curve. Your real one is probably steeper at the front and flatter at the back, because the difference between the same day and the next day matters more than the difference between the fourth day and the fifth. And because that single assumption does nearly all the work in the output, the calculator always shows the answer at two, four and eight percent a day side by side. Look at the spread before you look at the headline.
Read the extra-wins line before the revenue line. Revenue at this scale is abstract; a change in wins per month is a number you can hold against specific quotes from last quarter and ask whether it looks plausible. If it does not, your decay assumption is wrong, and that is the input to go and fix.
What actually fixes it
Three things, in this order, and the order matters.
Write the pricing rules down. Not into a system yet. Into sentences. How is a price arrived at for a stocked item, for a contract customer, for a quantity above a break, for an item you have to buy in. If one person has to be asked, the rule is not written down and nothing downstream can be automated. This is unglamorous and it is the whole job.
Automate the checks, not the judgement. Stock position and lead time are lookups. They do not need a person, they need a connection — and they are responsible for two of the four waiting steps in the diagram above. Pulling those into the quote as it is built removes the gaps where the clock is being run by someone else.
Flip the rep from building to approving. This is the real change. A rep who receives a drafted quote with every line priced, every lead time attached, and the two uncertain lines flagged with a reason is doing a different job from a rep who starts with a blank quote and a spreadsheet. The drafted quote can be wrong — it will sometimes be wrong — and reviewing a wrong draft is still faster than building a right one from nothing.
That is the shape of the Inbound Sales Agent, which picks up the RFQ and drafts the response, and the Quote-to-Cash Agent, which carries the same thread through to the order and the invoice. Both run with the approval limits you set, and both log what they saw and why they did it, so an odd quote can be read back rather than guessed at.
Checklist: what has to be true before you can quote faster
These are the things that make the difference between a quoting project that lands and one that quietly stops.
- You can export quotes with both dates and the outcome. RFQ received, quote sent, won or lost. If the received date is not captured anywhere, start capturing it this week — everything else depends on it.
- Your price list is a file, not folklore. One current version, with an owner, in a format something other than a human can read.
- Contract pricing lives in a system. With a review date on it. The deals that exist only in email threads are the ones that make reps slow and quotes wrong.
- Lead times for your top items come from somewhere readable. A supplier portal, a feed, even a maintained spreadsheet. Anything except an email to a person.
- Somebody owns the quote queue and can see its age. If no screen in the business shows the oldest unquoted RFQ, nobody is triaging, and the easy ones are stuck behind the hard one.
- A rep can approve a drafted quote without rebuilding it. If your process makes a rep retype a draft to take ownership of it, the draft saves nothing.
- You can name the handful of product families behind most of your RFQs. That is where the pricing rules get written first, and where a first build should start.
Where to go next
If the method in the middle of this piece gave you a decay figure you trust, the next question is where the gap actually closes — at the inbound end, where the RFQ gets read and drafted, or across the whole thread from quote to cash. The RevOps Agents page covers the first, the Supply Chain Agents page covers the second, and both say what gets measured and where the approval line sits.
If the honest answer is that you cannot get the quote history out of your systems to run the method at all, that is its own finding, and AI Reporting and Dashboards is the page about it.
And if you would rather have the question answered than read another page: the Agent Opportunity Assessment takes about three minutes, asks about a dozen questions about how your operation actually runs, and comes back with the three agents that would pay off first in your business — worked out from your own answers, the same way the calculator above works.