To increase proposal acceptance rate, fix the follow-up before you touch the document. Measure it as accepted ÷ sent on a fixed send cohort. The largest public dataset — 742,137 proposals sent through Proposify in 2025 — reports a 34% average close rate, but no independent benchmark exists, so your own baseline is the only honest comparison.
- Formula: accepted proposals ÷ proposals sent, counted on the cohort that was sent in a period, not on the deals that closed in it.
- The only large public number: a 34% average close rate across 742,137 proposals sent through Proposify in 2025, spanning 30 industries. That population is self-selected — companies that bought proposal software and used it.
- The decision window is short: in that dataset the average time from send to first open is 34 minutes, and from open to closed-won is 2.5 days.
- The most-quoted follow-up statistic in this field is not real. “80% of sales need five follow-ups” traces to a 1942 survey of fewer than 40 members of one Long Island chapter.
- The floor for an old quote pile: across our own Australian database reactivation campaigns, a fully dormant CRM record converts to a booked qualified call at 4.4% average and 8.9% peak. A quoted prospect is warmer than that.
How is proposal acceptance rate actually calculated?
Proposal acceptance rate is proposals accepted divided by proposals sent. The mistake almost everyone makes is the denominator: counting the quotes accepted in September against the quotes sent in September. Those are two different populations. A quote sent on 28 September that lands in November is a loss in one month and appears from nowhere in another.
Measure proposal acceptance rate on the send cohort: take every quote sent in a fixed month, wait a fixed window, then count how many of that specific set were accepted. Ninety days suits domestic trades and most professional services; thirty suits transactional work where the buyer compares three prices the same day. The window matters as much as the number — 22% at 30 days and 22% at 180 days describe completely different businesses.
Keep it separate from close rate, which is normally measured from a wider denominator: every qualified opportunity, including those that never reached a written price. If your close rate and acceptance rate are identical, you are quoting everyone who asks, which is its own problem.
How it works
How to lift proposal acceptance rate in four steps
Cohort the quotes
Tag every quote by the month it was sent, not the month it closed. Acceptance rate measured on the send cohort is the only version that compares across periods.
Start the clock at open
Track when the buyer opens the document, and make first contact the same working day. You are following up the open, not the send.
Run a fixed ladder
A set number of contacts across a set window, each carrying new information rather than a check-in. Hold the ladder constant for a quarter so the result is readable.
Bank the loss reason
Record on every loss whether the prospect explicitly declined and how many contacts you made. That is what separates a price problem from a follow-up problem.
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What is a good proposal acceptance rate? The honest answer
There is no credible independent benchmark for proposal acceptance rate, and any page that gives you one without naming its dataset is guessing. This is the stage of the pipeline where public data is genuinely thinnest, and pretending otherwise would be more useful to us than to you.
What does exist is platform data. Proposify’s State of Proposals 2026 analysed 742,137 proposals sent through its own product during 2025 across 30 industries, totalling $3,065,076,804 in generated sales at an average deal value of $16,388, and reports a 34% average close rate. Real dataset, real n, and the best public figure available. It is also not an industry benchmark: the population is businesses that pay for proposal software, which is not the population of every builder quoting from a spreadsheet. The same page asserts that “the industry average close rate is only 20%” without attributing that figure to any source — treat it accordingly.
So do not chase 34%. Pull your last three complete send cohorts, apply a fixed window, write down the three numbers. That is your benchmark. In our experience a business that has never calculated it has no idea whether it sits at 15% or 45%.
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The seven levers that move proposal acceptance rate, ranked
Ranked by the effect size we observe in the campaigns we run — our own operator judgement, not a published study, and the table says so rather than dressing it up. Fix them top down; the top two are worth more than the bottom five combined.
| # | Lever | What it changes | Evidence, and whose it is |
|---|---|---|---|
| 1 | Follow-up that actually happens | The share of sent quotes receiving any second contact at all | Ours, attributed: in our own client work, doubling contact rate on a cohort is worth roughly 2x downstream. No public benchmark exists for quote-specific follow-up. |
| 2 | Speed of first contact after sending | Whether you are in the conversation while the buyer is still reading | Proposify 2025: send-to-open 34 minutes; open-to-closed-won 2.5 days. The famous response-time research is about inbound enquiries, not quotes — see below. |
| 3 | A booked next step written into the quote | Replaces “let me know what you think” with a diarised time | No public figure. Measurable in your own data: acceptance of quotes with a booked walkthrough versus those without. |
| 4 | Reaching the actual decision-maker | Whether the second partner or finance approver ever sees the document | No public figure. Record, per quote, whether every decision-maker was on a call. |
| 5 | Pricing as options rather than one number | Turns a yes/no into a which-one | No verified public figure. We looked — the circulated numbers on pricing tables trace back to vendor marketing pages, so we have not published one. |
| 6 | Recording the real loss reason | Turns anecdote into a countable list | No public figure. The test in the “someone cheaper” section below separates price losses from silence losses. |
| 7 | Reviving the 90-day-plus cohort | Converts quotes your CRM has already written off | Ours: 4.4% average, 8.9% peak, on fully dormant records to a booked qualified call, across our database reactivation campaigns including Colliers. |
A note on our own numbers, because they do not reconcile if you multiply them. In our own client work we typically see something in the order of a 300% conversion lift for a business still running 2020 operations rather than 2026 AI-driven operations, and separately we describe doubling contact rate as worth roughly 2x. Those do not multiply out to the headline, because the levers overlap heavily: contacting people faster is part of how contact rate rises, and contact rate is part of how acceptance rises. They are the same improvement counted twice. We would rather say that plainly than publish arithmetic that does not add up.
How long after you send a quote does it stay winnable?
A quote does not have an expiry date; it has a handover point, and that point is roughly 90 days. Before it, the quote is a live opportunity belonging to whoever priced it. After it, it is a reactivation cohort belonging to a scheduled process. Most businesses run neither, which is why the pile grows.
| Days since quote sent | Acceptance probability — what is actually known | Correct action |
|---|---|---|
| Day 0 | Proposify 2025: average time from send to first open is 34 minutes | Send inside business hours and make contact the same day. You are following up the open, not the send. |
| Days 1–3 | Same dataset: average time from open to closed-won is 2.5 days | This is the decision window. Get a walkthrough call in the diary here, or spend the next month chasing. |
| Days 4–14 | No public dataset publishes per-day acceptance for this range. We could not find one and do not have a first-party figure either. | Two contacts on different channels, each carrying new information — a start date, a materials change, a like-for-like job. Never “just checking in”. |
| Days 15–30 | No public dataset. Note that most CRMs auto-close opportunities around here, which is a reporting default rather than a buyer decision. | Ask the closing question and accept a no. An explicit no is worth more than an open record because it frees follow-up capacity. |
| Days 31–90 | No public dataset. Cold, not dead — the scope you priced is usually still a job the buyer intends to do. | Move to monthly contact, and stop counting it in the current cohort or you corrupt the measurement. |
| Day 90+ | Our own record on fully dormant CRM records: 4.4% average, 8.9% peak, to a booked qualified call. A quoted prospect is warmer, so treat 4.4% as a floor rather than a forecast. | Stop calling it an open quote. Run it as a reactivation cohort with a fresh reason to reopen the conversation. |
One caution about the row everybody wants to fill in with the famous lead-response numbers. The best-documented research here is Oldroyd, McElheran and Elkington in Harvard Business Review, March 2011, which audited 2,241 US companies and separately examined 1.25 million leads at 29 B2C and 13 B2B firms, finding that firms contacting a prospect within an hour were nearly seven times as likely to qualify the lead — defined as a meaningful conversation with a key decision maker — as those waiting an hour longer, and more than 60 times as likely as those waiting a day. That is genuinely well documented, and it is about the first response to an inbound web enquiry. It is not about quote follow-up, and applying its multipliers to proposals is an extrapolation nobody has tested. We treat it as the mechanism, not the number, which is why the five-minute speed-to-lead rule stays on its own page rather than lending its figures here.
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The 4.4% floor: what your pile of dead quotes is actually worth
The 4.4% floor rule: price your quote follow-up against 4.4%, not against zero. Across our own Australian database reactivation campaigns — buyer’s agents including Colliers, brokers, planners and consultants — a fully dormant CRM record converts to a booked qualified discovery call at 4.4% on average, with 8.9% as our peak campaign on record. Those people never received a site visit, a measure-up or a priced scope.
Your quoted-but-never-closed pile received all three. Every one of those prospects invited you onto their property, spent an hour with you, and asked for a number. That is a strictly warmer cohort than a dormant record, which is why 4.4% is a floor and not a forecast: we can honestly give you the dormant-record figure because we measured it, and we cannot honestly give you a quote-cohort figure because measuring that properly requires your data, not ours. The rule is what survives — if the coldest possible cohort returns 4.4%, a quote pile you have been treating as worthless is not worthless.
Two things the rule does not say. It does not say old quotes convert at 4.4%; that number is ours, from a different cohort, and we will not launder it into a benchmark. And it does not say every pile is worth working — below roughly 200 dormant records the arithmetic rarely justifies building a process. The mechanics of reopening those conversations, including the Spam Act and Do Not Call Register conditions that apply in Australia, are in our follow-up playbook for reactivating dead trade quotes.
Worked example: what six percentage points is worth on 40 quotes a month
Substitute your own numbers; the structure is the point. Say a renovation business sends 40 quotes a month at an average job value of $18,000, and its last three send cohorts came back at 22% acceptance on a 90-day window.
- Baseline: 40 × 22% = 8.8 jobs a month × $18,000 = $158,400 a month.
- Add six percentage points — not a claim, an input you are testing — taking acceptance to 28%: 40 × 28% = 11.2 jobs × $18,000 = $201,600 a month.
- Difference: 2.4 jobs a month, $43,200 a month, $518,400 a year, on quotes you were already producing. No extra leads, no extra ad spend, no extra site visits.
- What it costs to run: a four-contact ladder over 30 days on 40 quotes is 160 contacts a month. At four minutes each — find the record, read the scope, make the call, log the outcome — that is about 10.7 hours a month, roughly half a day a week.
Half a day a week is affordable, and a business this size should do it in-house. Now run the same arithmetic at 400 quotes a month: 1,600 contacts, about 107 hours, two-thirds of a full-time person doing nothing else, forever, without skipping the week they are busy on site. That is the honest shape of the cost, and it is where quote follow-up systems die — not in the design, in the fourteenth consecutive month of execution.
“They went with someone cheaper” — how to tell whether that is true
Price is the reason buyers give and silence is usually the reason they leave, and one test separates the two. On your last 30 lost quotes record two fields: whether the prospect ever explicitly declined, and how many contacts you made after sending. Then sort them.
If most losses have an explicit decline and a named competitor, you have a pricing or positioning problem and follow-up will not fix it — go and look at what the winning quote contained. If most losses are silent and received one contact or none, you do not have a price problem; you have a follow-up problem wearing a price costume. The second pattern is far more common in trades and domestic services, because an unanswered quote is indistinguishable from a beaten one, and the human explanation is always the flattering one. A price problem costs margin to fix and a follow-up problem costs hours — businesses routinely discount their way out of a problem discounting cannot touch.
Do it yourself or hand it over: the honest crossover
This section exists because for a lot of readers the answer is genuinely “do it yourself”, and saying so is more useful than pretending otherwise.
| Your volume | Monthly follow-up load at four contacts per quote | What we would actually do |
|---|---|---|
| Under 20 quotes a month | Under 80 contacts, roughly 5 hours | Do it by hand. A calendar reminder and some discipline beat any system at this size, and no external cost is justified. |
| 20–80 quotes a month | 80–320 contacts, 5–21 hours | In-house, but templated and scheduled inside the quoting tool you already run. The failure mode is the busy month, not the method. |
| 80–250 quotes a month | 320–1,000 contacts, 21–67 hours | Crossover. Someone owns this as a named job or it stops happening. Hire or outside system is an economics question, not a technology one. |
| 250+ a month, or a backlog over 200 dormant quotes | 1,000+ contacts, 67+ hours | The manual version does not survive. This is where businesses come to us, and it is also where a dedicated internal hire genuinely works — both are real answers. |
Whichever way that goes, the measurement discipline belongs to you rather than to whoever runs the outreach: cohort the quotes, fix the window, record the loss reason, review the trend quarterly. We run this work on a pay-per-result basis — booked qualified appointments rather than retainers or seats — for trade and construction businesses and home renovation companies, where the quoted-but-never-closed pile is usually the largest single asset nobody has valued. Our published 7x average sales lift methodology defines what that figure measures and discloses that the median across the same cohort is closer to 4x, which is the number we would want you to read. Acceptance rate sits downstream of set rate and contact rate and upstream of close rate, so improving it in isolation will surface whichever of those is weakest — the pipeline-stages hub covers each stage and what it costs you when it leaks.
Frequently asked questions
How is proposal acceptance rate different from close rate?
Proposal acceptance rate uses a narrower denominator: only opportunities that received a written price. Close rate is normally measured across every qualified opportunity, including the ones that never reached a quote. A business can have a strong acceptance rate and a weak close rate if it only quotes prospects who were always going to buy, which flatters the metric and hides a qualification problem earlier in the pipeline.
What is a good proposal acceptance rate?
No credible independent benchmark exists. The largest public dataset is Proposify’s State of Proposals 2026, covering 742,137 proposals sent through its platform during 2025 across 30 industries, which reports a 34% average close rate. That population is self-selected — businesses that bought proposal software — so it is not an industry average, and the same page’s claim of a 20% industry average carries no source. Measure your own last three send cohorts instead.
How many follow-ups does a quote actually need?
Nobody credibly knows, and the statistic usually quoted here is not evidence. “80% of sales are made on the fifth to twelfth contact” is attributed across the web to the National Sales Executive Association. Sales & Marketing Executives International — the organisation formerly named the National Sales Executives Association — searched its own archives in 2021 and traced the figure to a 1942 survey of its Long Island chapter with a sample size of fewer than 40. VentureBeat’s Stewart Rogers reached a similar conclusion in 2014. Choose a fixed number of contacts, hold it constant for a quarter, and measure what your own cohorts do.
How quickly do people open a quote after you send it?
In Proposify’s 2025 data, the average time from sending a proposal to the prospect opening it is 34 minutes, and the average time from that open to a closed-won deal is 2.5 days. The practical implication is that your follow-up clock should start when the document is opened, not when it is sent, and that most of the decision happens in the first working week.
Should I stop measuring acceptance rate by calendar month?
Yes. Counting quotes accepted in a month against quotes sent in the same month mixes two different populations and produces a number that moves for reasons unrelated to your sales performance. Measure the send cohort: take every quote sent in a given month, wait a fixed window — 90 days suits most trades and professional services — then count acceptances within that specific set.
Is an old quote worth following up at all?
Across our own Australian database reactivation campaigns, fully dormant CRM records — people who never received a site visit or a price — convert to a booked qualified call at 4.4% on average and 8.9% at peak. A prospect who took a site visit and asked you for a number is a warmer cohort than that, so 4.4% is a reasonable floor to price your effort against. It is our figure from a different cohort, not a benchmark for quote acceptance, and we are not going to present it as one.
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