Average B2B Sales Cycle Length by Deal Size and Segment

The average business-to-business (B2B) sales cycle runs from within 14 days for deals under $2,000 in annual contract value (ACV) to 6 to 18 months or more for deals over $500,000, based on SaaStr's bands. Deal size, stakeholder count and budget timing decide where a given deal lands.

What is the average B2B sales cycle length?

The average B2B sales cycle depends mostly on deal size. Small deals close in weeks. Deals above half a million dollars in ACV can take well over a year. The bands below come from SaaStr, which describes them as averages for a company that already has a working sales motion.

The average time to close rises from 14 days for deals under $2,000 ACV to 90 days for deals under $25,000 ACV.

Average days to close by ACV band

Item	Value
Under $2,000 ACV	14 days
Under $5,000 ACV	30 days
Under $25,000 ACV	90 days

Source: SaaStr, Dear SaaStr: What's a Good Benchmark for B2B Sales Cycles, 2023

The same SaaStr post adds a cross-check from Gong: across its customer base the average deal size is $97k and the cycle at that size is 69 days. That sits at the fast end of the 90 to 180 day band for deals under $100,000.

Segment labels give a similar picture with different cut points. Domestique puts small and midsize business (SMB) deals under $10,000 in annual recurring revenue (ARR) at 30 to 60 days, mid-market deals of $10,000 to $100,000 ARR at 60 to 120 days, and enterprise deals above $100,000 ARR at 6 to 12 months.

Cycles got longer after mid-2022

Capchase surveyed 500 U.S.-based SaaS revenue leaders and compared January 2021 to June 2022 against June 2022 to June 2023. It found that sales cycles became 3.8 weeks longer across all companies.

Here is what that does to a plan, using our own arithmetic. 3.8 weeks is about 27 days. A team that planned around a 69-day cycle and absorbed the full lengthening would be closing in roughly 96 days. On a quarterly plan, a deal sourced in the first week of a quarter used to land inside it. At 96 days it lands in the next one.

We think the lengthening matters more for small deals in relative terms. 27 extra days nearly triples a 14-day cycle but adds little to an 18-month one. Check which band most of your pipeline sits in before you apply the average.

To see how a longer cycle changes revenue per day from the same pipeline, run your numbers through the pipeline velocity calculator. Cycle length is the denominator, so a 40% longer cycle cuts velocity by more than a quarter with everything else held constant.

Sales cycle length by deal size, with publisher, sample and definition

The band figures quoted earlier come from different kinds of evidence. Before you set one against your own CRM, check three things: how the publisher drew its bands, what sits behind the number, and what it says makes deals run long.

What the table changes in practice

The bands are not interchangeable. SaaStr cuts by ACV and Domestique by ARR, so a multi-year deal or one with services attached can land in different bands depending on which measure you use. In our reading, the two sets line up most closely at the enterprise boundary, where both treat deals above the $100,000 mark as a separate class.

Process weight moves the number more than price alone. SaaStr notes that if the business process change is large, "e.g., ERP) it will be far longer". Domestique points to research showing the average enterprise buying decision involves 6 to 10 stakeholders.

Neither band set states where the clock starts and stops. Fix that in your own data first. We measure from opportunity creation to closed-won, and we exclude closed-lost deals from the average.

Worked example: adding slippage to a band

Domestique reports that about 20% to 30% of forecasted deals slip in a given quarter. It also says the highest slippage rates are mid-market and enterprise.

Say you have 40 mid-market deals forecast to close this quarter. Applying that range, 8 to 12 of them push into next quarter. If your average mid-market deal is $40,000 ARR, that is $320,000 to $480,000 moving out of the quarter. Model that explicitly in your pipeline coverage target. Then run the adjusted cycle length through the pipeline velocity calculator.

For an opportunity-level dataset to compare against, Ebsta's 2025 GTM Benchmarks was produced in association with Pavilion and drew on 2,000+ CROs.

How long is the sales cycle for enterprise SaaS?

This section covers what moves an enterprise deal inside or outside those bands, and how to plan around it.

Budget timing sets the floor

Sometimes, a whole additional year if you don't."](https://www.saastr.com/dear-saastr-whats-a-good-benchmark-for-b2b-sales-cycles/) The same author took 1.5 years to close Google for a full enterprise deal in his company's early days.

Domestique adds that multi-product or multi-region deals can run longer and that a well-run deal can still slip two quarters because of an internal budget freeze. It also reports that legal and security reviews are now multi-week exercises.

A worked example

The buyer locks next year's software budget in month 4.

• If evaluation, security review and the business case finish by month 4, the deal is funded and can sign around month 7.

The work is the same in both cases, and the close dates sit a year apart. Plan stage dates backward from the budget lock, not forward from the first meeting.

Discovery template for enterprise deals

What 2023 showed

Ebsta's 2024 benchmark, across its full dataset rather than enterprise alone, found that sales cycles grew +16% in H1 (and +38% compared to 2021), before cycles reduced by -23% as the year closed. It reported that 44% of deals were pushed back. When deals slipped, win rates fell by -67%, particularly for deals delayed more than 8 weeks.

In our reading, a missed budget date is a win-rate risk as well as a timing risk. We think enterprise pipeline should carry extra coverage for deals whose budget lock has passed. Model that in the pipeline coverage calculator and watch its effect on pipeline velocity.

How to measure sales cycle length in your CRM

Kixie defines sales cycle length as the time it takes a potential customer to move through the sales process, from initial contact to the final purchase decision. That definition leaves the start point open. Pick one and write it down before you pull a single report.

Gong separates two terms that teams often blur. The sales cycle is the steps a team goes through to close a deal, and the sales process is how salespeople carry out those steps. Gong's shorthand: the cycle is the noun, the process is the verb. You measure the noun. You coach the verb.

Three start and stop points

Salesforce gives the base formula: the total number of days it takes for a deal to close, divided by the total number of closed deals. Salesforce also suggests matching the measurement window to your revenue reporting: if you close the books quarterly, measure quarterly.

Why it has to come from the CRM

Salesforce notes that many companies never calculate the metric because the information about their reps' pipeline is spread out among employees and their files. A customer relationship management (CRM) system puts the dates in one place. Without timestamped fields, the number depends on each rep's memory.

Field and report template

Add or confirm these fields on the opportunity object:

• Close Date (date): locked once the stage is Closed Won.
• Days in Stage (formula or stage history): current stage date minus previous stage date.

Group by segment and deal size band.

Worked example

The total is 400 days across 5 deals, so the average is 80 days. The median is 70.

Kixie suggests comparing how long closed-won opportunities spend in each stage to find the slowdown. Feed the average into the pipeline velocity calculator and track it on your demand gen dashboard.

Why the median cycle matters more than the mean

A mean sales cycle is easy to compute and easy to distort. A few long deals pull it upward, and it then describes no deal you actually closed. The median, the middle deal when you sort by days to close, holds steady when one or two outliers land in the quarter.

A worked set of 10 closed deals

Here is one quarter of closed-won deals, sorted by days from opportunity creation to close.

The total is 925 days, so the mean is 92.5 days. The median is the average of deals 5 and 6: (50 + 55) / 2 = 52.5 days. The mean sits about 76% above the median, and eight of the ten deals closed faster than the mean.

Remove the two enterprise deals and the mean of the remaining eight falls to 48.1 days (385 / 8). Two deals moved the headline number by more than 44 days. A forecast built on 92.5 days would push expected close dates for typical mid-market deals out by more than a month.

Published averages carry the same risk. Norwest found that companies selling to the mid-market at $50-100K ACV were reporting that it takes on average 9 months to close a deal. In our reading, a figure labeled as an average should be treated as a mean unless the publisher says otherwise, and compared with your own median only after you check for skew.

What to report instead

1. Split by ACV band first. Use the same bands you set up when measuring in your CRM. Never pool enterprise and mid-market deals in one distribution.
2. Count closed-lost deals separately. Report their count and their own median days to close. Mixing them into closed-won cycles blends two different events: a buyer who signed and a deal that died or was disqualified.
3. Show the count next to every figure. A median from four deals is a data point, not a benchmark.

A reporting row per band then reads: band, closed-won count, median days, 75th percentile days, closed-lost count, closed-lost median days.

Feed the median, not the mean, into the pipeline velocity calculator when you model pipeline velocity. We think the median gives a forecast that matches how most of your deals actually move.

Worked example of when this quarter's pipeline can close

A quarter has about 91 days. If pipeline is created evenly across those days and every deal takes exactly the median cycle to close, the share of this quarter's new pipeline that can also close this quarter is (91 minus cycle) divided by 91. A deal created on day 1 with a 30-day cycle closes on day 31. A deal created on day 70 with the same cycle closes next quarter.

Assuming even pipeline creation over a 91-day quarter, the share of new pipeline that can close in the same quarter falls from 67% at a 30-day cycle to 0% at 120 days.

Worked example: share of a quarter's new pipeline that can close in the same quarter

Item	Value
30-day cycle	67 % of pipeline created
60-day cycle	34 % of pipeline created
90-day cycle	1 % of pipeline created
120-day cycle	0 % of pipeline created

Eleven example deals closing in 35, 42, 50, 58, 63, 70, 77, 90, 120, 180, 365 days: mean 104.5 days, median 70 days. Source: Take the Market calculation: (91 days minus cycle) / 91, pipeline created evenly, every deal at the cycle length

The drop is linear and steep. At a 60-day cycle, two thirds of what marketing sources this quarter is next quarter's revenue. At 90 days, almost none of it closes in the quarter it was created.

Plug in your median, not your mean

Take eleven example deals that closed in 35, 42, 50, 58, 63, 70, 77, 90, 120, 180 and 365 days. The mean is 104.5 days, past the 90-day row, where the closable share is already about zero, so it suggests nothing created this quarter can close this quarter. The median is 70 days, between the 60-day and 90-day rows: by the same formula, (91 − 70) / 91, about 23% of this quarter's new pipeline can still close in-quarter. The two outlier deals at 180 and 365 days decide which answer you get, which is why the earlier section argued for the median.

What this means for coverage

Coverage for this quarter's number has to come mostly from pipeline already open on day 1. In our reading, a team with a 90-day median that sets a single blended pipeline coverage target is counting next quarter's pipeline against this quarter's quota.

A working template:

1. Pull the median cycle for closed-won deals in each segment from your CRM.
2. Read the closable share from the table, or compute (91 minus median) / 91.
3. Split the coverage target in two: pipeline open at quarter start, and pipeline to be created in-quarter, weighting the second by the closable share.
4. Run each segment separately. Mid-market and enterprise cycles, covered earlier, sit near or past the bottom row.
5. Re-check the split each quarter in the pipeline coverage calculator.

Cycle length can move. Ebsta and Pavilion's 2025 report found a 9% shorter sales cycle against its prior edition. A shorter median moves a segment up the table, so we think the in-quarter share of the coverage target should be reset whenever the median shifts, not set once a year.

Why are B2B sales cycles getting longer?

Three surveys from 2023 and 2024 point the same way: deals took longer, contracts got smaller and budgets shrank. They differ in sample and in who answered, so it helps to read them side by side.

The downturn comparison

Capchase worked with OnePoll to survey 500 U.S.-based SaaS revenue leaders. It checked their answers against financial data from over 1,200 SaaS companies that its underwriting team had analyzed. The before and after periods ran from January 2021 to June 2022 and June 2022 to June 2023.

The result: sales cycles became 3.8 weeks longer across all companies. Customer acquisition cost (CAC) and CAC payback rose between the two periods as well.

Self-reports and hard data diverged on contract value. Only 8.2% of leaders reported a decrease in average contract value and 23.2% reported stagnation. The internal data was harsher: 42% of companies saw average revenue per unit decline. In our reading, revenue leaders asked about their own deals tend to report less decline than their financials show.

Ebsta's 2024 report described a combination of declining win rates, dropping deal values, and increasing sales cycles.

Budget cuts and buyer urgency

Norwest surveyed 195 sales and marketing leaders at venture capital and private equity backed companies. At companies with $25-$100M in revenue, marketers reported operating with as little as half of the previous year's spend. Growth plans fell too: 38 percent planned 50 percent growth in 2023, against 9 percent in 2024.

Its sales leaders reported that cycles have continued to lengthen. Norwest framed the core problem as urgency: without it, deals stall. We think buyer budget cuts and seller growth cuts compound each other. Fewer buyers need to act this quarter, and fewer sellers push them to.

Test the trend on your own books

Before you plan around these numbers, run the comparison yourself.

1. Pull closed-won deals for two windows of equal length, for example the same quarters a year apart.
2. Take the median days from opportunity creation to close in each window, split by segment.
3. Convert the gap to weeks so it matches the 3.8-week figure.

Worked example: a median of 84 days in the earlier window and 111 days in the later one is a 27-day gap, about 3.9 weeks. Enter the new median in the pipeline velocity calculator to see what the slower cycle does to monthly pipeline output.

What to do when your sales cycles lengthen

When the median cycle stretches, find where it stretched before you change the forecast. These five steps run in order, from diagnosis to plan.

1. Split the cycle by ACV band and by stage. Domestique's advice is to "Start by segmenting your closed-won deals by size and source." Then compute median days per stage inside each band. Watch your own trend line: their example is an SMB cycle that went from 45 days to 70 days over the past three quarters. A band where one stage doubled needs a different fix from a band where every stage grew a little.

2. Involve decision makers in the first two stages. Ebsta and Pavilion found that when decision makers took part early, win rates rose by 55%, and they report an average of 8 stakeholders for new opportunities. That finding is about win rate. In our reading, early access also cuts late-stage restarts. Make it an exit criterion: no deal leaves stage two without a logged decision-maker meeting.

Pick the row that matches your last four quarters of actual slippage, then check whether next quarter's number already counts the rolled amount twice.

1. Check the change against buyer readiness data. In HubSpot's 2025 survey, 74% of sales pros believe AI is making it easier for buyers to research products, and one founder said "The buying cycle is a bit slower … buyers are spending more time exploring options." Compare stage timing with intent data. We think long early stages alongside heavy account research point to buyer exploration. Long late stages with flat research point to internal process: approvals, reviews or a stalled champion.

2. Rebuild pipeline timing. Cycle length is a lagging signal, so Domestique suggests watching velocity, which combines the number of opportunities in your pipeline, average deal value, win rate, and sales cycle length. If your median moved from 75 to 95 days, as in their example, pipeline for a given quarter must be created 20 days earlier. Rerun the numbers in the pipeline velocity calculator and reset targets in the pipeline coverage calculator.

What this page does not cover

This page does not break sales cycles down by industry or region, and it does not cover sales compensation, hiring or tool selection. The benchmarks and worked examples support planning and forecasting only. Nothing here is financial or legal advice.

Related on Take the Market: MQL to SQL conversion rate benchmarks and B2B funnel conversion rates by stage; B2B Win Rate Benchmarks by Deal Size and Segment; Demand generation calculator: reverse funnel math from a revenue target to visitors, leads, MQLs, SQLs and opportuniti.