The short answer
RFP win rate is wins divided by bids that reached a decision, times 100. Leave out bids still pending and bids the buyer cancelled, and always state the denominator, because “wins over submitted” and “wins over decided” can land far apart on the same bids, as the worked example below shows. Track it by count and by contract value, over a rolling 12 months. Pair it with bid rate, shortlist rate, hours per response and on-time rate so you can tell whether a change came from bidding better, bidding more selectively, or just bidding less.
Key takeaways
- The denominator is the metric: Wins over decided bids is the cleanest win rate. Wins over submitted bids understates it while decisions are pending.
- Count and value tell different stories: Winning many small deals and losing large ones looks fine by count and poor by value.
- Small samples lie: At 16 decided bids, one result swings the rate about six points. Use rolling 12-month windows.
- A published reference point exists: Loopio’s 2026 benchmark survey of 1,533 teams reports an average win rate of 39 percent for 2025, down from 45 percent in 2024, self-reported.
- Selectivity moves win rate: Declining poor-fit work tends to raise it. Watch bid rate alongside, or you’ll celebrate a number that came from saying no.
- Limitations: Proposal quality is one input among many. Price, relationships and incumbency drive outcomes too, so win rate alone can’t grade the proposal team.
The metrics that matter, with formulas
| Metric | Formula | What it tells you | What it doesn’t |
|---|---|---|---|
| Win rate (count) | Wins ÷ decided bids × 100 | How often you win once a buyer decides | Whether the wins were worth much |
| Win rate (value) | Value won ÷ value of decided bids × 100 | Whether you win the big ones | Profitability of what you won |
| Bid rate | Bids submitted ÷ opportunities received × 100 | How selective you are | Whether the selection was right |
| Shortlist rate | Shortlisted ÷ decided bids × 100 | Whether the written response gets you to the next round | What happened in orals or negotiation |
| Hours per response | Total logged hours ÷ responses, by type | Effort and cost per bid | Quality of the effort |
| SME hours per response | Non-proposal-team hours ÷ responses | Load on experts outside the team | Whether those hours were avoidable |
| Cycle time | Submission date − receipt date, in working days | How fast you turn work around | Whether you started late on purpose |
| On-time rate | Submitted by internal deadline ÷ submitted × 100 | Process discipline | External delays you couldn’t control |
| Content reuse | Answers from approved content ÷ total answers | Library usefulness | Whether reused answers were right |
| First-pass acceptance | AI or library drafts approved without edits ÷ drafts | Draft quality | Whether reviewers were actually checking |
Two of these deserve a warning. Shortlist rate is the closest thing you have to a pure measure of the written response, because it isolates the stage the document controls. Track it if your buyers run multi-stage processes. First-pass acceptance looks like a quality metric, but a high number can also mean rubber-stamping; read it next to the review signals in our human-in-the-loop guide.
Getting the denominator right
Every win-rate argument in a quarterly review comes down to what’s under the line. Define these outcome states once and stick to them:
- Won: contract awarded to you, even if not yet signed.
- Lost: contract awarded to someone else.
- No decision: buyer kept the incumbent, deferred, or did nothing. Decide once whether this counts as a loss (stricter) or is excluded (kinder), and write it down. Most teams treat it as a loss because the buyer did evaluate you.
- Cancelled: buyer withdrew the RFP. Exclude.
- Withdrawn: you pulled out after submitting. Exclude from win rate, but track the count, since late withdrawals usually signal a weak go/no-go call.
- Pending: no outcome yet. Exclude until resolved.
The cleanest standard metric is wins ÷ (wins + losses + no decisions). Report it with the window (“rolling 12 months to September 30”) and the count (“on 42 decided bids”). A number without its denominator invites the wrong comparison.
Worked example: One quarter, three win rates
Here’s an illustrative quarter for a mid-size team.
Opportunities: 40 RFPs received. 26 bid, 14 declined. Bid rate: 26 ÷ 40 = 65%.
Outcomes of the 26 submitted bids:
| Outcome | Count | Contract value |
|---|---|---|
| Won | 6 | $1.2M |
| Lost | 8 | $2.9M |
| No decision | 2 | $0.9M |
| Cancelled | 2 | $0.4M |
| Pending | 8 | $2.6M |
Three ways to state win rate:
- Wins over submitted: 6 ÷ 26 = 23%. Looks bad, but eight bids haven’t been decided and two were cancelled.
- Wins over decided (excluding no decision): 6 ÷ 14 = 43%.
- Wins over decided (counting no decision as a loss): 6 ÷ 16 = 37.5%. This is the version to report.
By value: $1.2M won ÷ ($1.2M + $2.9M + $0.9M) = $1.2M ÷ $5.0M = 24%. The team wins more than a third of decided bids but under a quarter of the value, which says the large bids are going elsewhere. That’s a more useful finding than any of the count-based rates.
Shortlist rate: say 9 of the 16 decided bids reached a shortlist or orals. 9 ÷ 16 = 56%. The written responses are getting the team into the room more often than they’re winning, so the gap may be in orals, pricing or relationships rather than the document.
Sample-size check: with 16 decided bids, one more win would make it 7 ÷ 16 = 44%, a jump of about six points from a single result. Don’t present a quarter-on-quarter change smaller than that as a trend.
Efficiency metrics: Hours, cycle time and on-time rate
Win rate tells you about outcomes. Efficiency metrics tell you about cost and capacity, which matter just as much when deciding how many RFPs you can take on.
Log hours by response type and role. A security questionnaire, a DDQ and a full narrative RFP are different jobs; averaging them hides everything. Loopio’s 2026 benchmark summary reports that teams spend 33 hours per RFP and that nine people contribute to each RFP on average. Use figures like those as context for your own logging, not as a target, since they mix response types and team sizes.
Measure cycle time in working days from receipt to submission. Then split it: days waiting for a go/no-go decision, days drafting, days in review, days in approvals. The longest segment is your bottleneck. Our deadline bottlenecks guide covers the usual culprits.
Track on-time rate against your internal deadline, not the buyer’s. Every response should be submitted before the buyer’s deadline, or it’s disqualified. The useful signal is how often you hit your own buffer, because missing it is how late-night portal failures happen.
Logging outcomes: The CRM fields that make this work
Metrics are only as good as the data entry. Put these fields on the CRM opportunity record (or a linked response record) so sales and proposal data match:
- Response type (RFP, RFI, RFQ, security questionnaire, DDQ, other)
- Date received, buyer deadline, internal deadline
- Go/no-go decision, date, decision owner and reason code
- Question count or page count (rough size)
- Date submitted, on time against internal deadline (yes/no)
- Hours logged, by role (proposal team, SMEs, legal, pricing)
- Shortlisted (yes/no)
- Outcome (won, lost, no decision, cancelled, withdrawn, pending) and outcome date
- Outcome reason, as stated by the buyer where available
- Contract value (estimated at bid, actual at award)
- Debrief requested and received (yes/no)
Two habits keep the data honest. First, close out pending bids monthly: chase the buyer or the account owner for an outcome. Second, use the buyer’s stated reason where you have it, and log your internal opinion separately. Loss reasons written by the person who lost are rarely neutral.
Reading the metrics together
Single metrics mislead. Combinations tell you what’s going on:
| Pattern | Likely meaning | What to check |
|---|---|---|
| Win rate up, bid rate down | You’re saying no more often to poor-fit work | Whether declined bids were genuinely poor fit |
| Win rate flat, shortlist rate up | The document is working; later stages aren’t | Orals preparation, pricing, negotiation |
| Win rate by value well below count | Losing the big deals | Large-bid pricing, references, compliance depth |
| Hours per response up, win rate flat | More effort, no return | Whether extra hours went to tailoring or to rework |
| On-time rate falling | Process strain | Volume versus capacity, late go/no-go decisions |
| Cycle time long, drafting short | Waiting, not writing | Go/no-go speed, approvals, SME availability |
The first row is the one to watch in a review. A bid/no-bid framework should raise win rate, and that’s a good thing, but only if the bids you dropped were genuinely poor bets.
Setting targets without gaming them
Every metric you put a target on will move, and not always for the reason you wanted. A few guardrails keep targets honest:
- Target win rate and bid rate together. A win-rate target alone rewards declining anything hard. Pair it with a floor on qualified bids submitted, or a value target, so the team can’t hit the number by bidding on half as much.
- Target hours per response by type, not overall. An overall average falls whenever the mix shifts toward short questionnaires, with no real efficiency gain.
- Don’t target first-pass acceptance. Once reviewers know approvals without edits are rewarded, they’ll edit less. Use it as a diagnostic only.
- Set on-time targets on internal deadlines. Buyer deadlines are pass or fail; a target of 100% says nothing useful. Hitting the internal buffer 90% of the time is a real goal.
- Review targets annually. If your market, offer or bid mix changed, last year’s win-rate target may be measuring something else now.
A useful format for leadership is a one-page scorecard each quarter: rolling 12-month win rate by count and value (with the decided-bid count), bid rate, shortlist rate, median hours per response for your two biggest response types, and on-time rate. Five numbers, each with its denominator and a one-line comment. More than that, and nobody reads past the first row.
About published benchmarks
The most-cited public source on RFP win rates is Loopio’s annual RFP Response Trends and Benchmarks Report. Its seventh edition, summarized in a March 26, 2026 post, draws on 1,533 teams worldwide and reports that teams win 39 percent of their RFPs on average, down from 45 percent the year before, that organizations submit an average of 166 RFPs a year, and that 75 percent of teams use a go/no-go process.
Use it with three caveats. It’s a survey, so the figures are self-reported. It’s published by a vendor that appears in this directory (Loopio), which doesn’t make it wrong but is worth knowing. And it doesn’t publish a single denominator definition you can match, so comparing your rate to the average only makes sense loosely. Your own trend over 12 months, on a stable definition, is worth more than any external average.
Where software helps (and where it doesn’t)
Where it helps: capturing the data without extra typing. Response tools that sync with the CRM can carry the opportunity ID, dates and outcome back and forth, and some log time or activity per project. Loopio lists analytics on response progress and content health among its features. Responsive covers intake through submission in one workflow, which gives you consistent timestamps for cycle time. AutoRFP.ai and Tribble both connect to Salesforce and HubSpot, and Tribble describes connecting proposal history and outcomes in one layer. On the other hand, G2 reviewers quoted on our Inventive AI profile (June to August 2026) call its analytics and management reporting light, so test reporting specifically if it matters to you.
Where it doesn’t: defining your denominators, getting outcomes from buyers, or deciding what a loss reason means. Those are process and discipline. Plenty of teams run good metrics from a CRM report and a shared spreadsheet. If your proposal tool’s dashboards disagree with the CRM, the CRM should win, because that’s where revenue is counted.
For tool shortlists, start at the RFP response software hub, and if you’re building a case for a purchase, the ROI guide shows how to turn hours per response into a business case.
Sources
- Average RFP Win Rates and More: 38 Proposal Statistics to Know in 2026, Loopio, published March 26, 2026 (summary of the 2026 RFP Response Trends and Benchmarks Report), accessed October 2026
- Vendor feature and review notes from the linked profile pages, accessed October 2026
- All other figures in this guide are illustrative inputs to the worked example, not benchmarks