What is the average deal size formula?
Divide total closed-won revenue for a period by the number of closed-won deals in that same period.``` Average deal size = Total closed-won revenue / Number of closed-won deals ```
A team that closed 34 deals worth $1,530,000 has an average deal size of $45,000. The formula is straightforward. The decisions that matter happen before the division: which revenue basis you count, which deals you include, and whether you use closed-won or open pipeline values.
Pick one revenue basis and hold it. Annual contract value, total contract value, and first-year billings all produce different averages from the same set of deals. A three-year $300,000 contract is a $100,000 deal on an ACV basis and a $300,000 deal on a TCV basis. Mixing the two inside one calculation makes the output meaningless, and it happens more often than most teams realize.
Should you calculate it from closed deals or open pipeline?
Use closed-won deals. Open pipeline values describe intent, not outcome.Open opportunities carry the amount a seller entered when the record was created, usually before scope is settled, before procurement applies pressure, and before any discount is negotiated. Most deals close for less than the value sitting in CRM. For example, a pipeline that shows an average deal size of $80,000 while closed-won deals average $40,000. A gap like that has direct consequences.
Every downstream number built on the inflated figure inherits the error. Coverage ratios double. Weighted pipeline doubles. A forecast built from open pipeline amounts predicts twice the revenue the same deals will actually produce. Calculating both versions and tracking the ratio between them gives you a correction factor you can apply, which is more useful than assuming the CRM amounts are right.
| Measure | Value | What it describes |
|---|---|---|
| Average open pipeline deal size | $80,000 | Amount entered at creation |
| Average closed-won deal size | $40,000 | Amount that actually arrived |
| Realization ratio | 50% | Correction factor for pipeline math |
Should you use the mean or the median?
Report both, because they answer different questions and the gap between them is itself a signal.The mean gives you revenue contribution per deal, which is what capacity models and coverage targets require. The median gives you the typical deal, which is what a rep actually works and what a manager should coach to.
Consider a quarter with 12 deals: ten between $28,000 and $52,000, one at $310,000, and one at $415,000. The mean lands near $94,000. The median lands near $40,000. Setting a rep quota using the mean implies a deal count that the median motion cannot deliver, and the plan will be short before the quarter starts.
When the mean runs well above the median, split the analysis. Model the large-deal motion separately, with its own win rate and its own cycle length. Those deals behave differently enough that averaging them with the core motion corrupts both.
How do you calculate average deal size by segment?
Filter first, then divide, and never plan off the blended number.| Segment | Closed-won deals | Closed-won revenue | Mean deal size | Median deal size |
|---|---|---|---|---|
| Enterprise | 9 | $1,260,000 | $140,000 | $118,000 |
| Mid-market | 27 | $1,296,000 | $48,000 | $45,000 |
| SMB | 82 | $984,000 | $12,000 | $11,000 |
| Blended | 118 | $3,540,000 | $30,000 | $11,000 |
Run the same split by lead source and by product line. Deals sourced from partner referrals and deals sourced from paid search often carry different values, and knowing which is which changes where you spend.
How does average deal size affect the forecast?
It scales everything, so an error here multiplies through every pipeline metric you produce.Average deal size appears in the sales velocity formula, in coverage math, in capacity planning, and in every pipeline generation target. An overstatement of 2x does not stay contained. It flows into the revenue expectation and produces a quarter that looked covered on day one and lands short.
The correction is mechanical. Compare closed-won averages against open pipeline averages for the same segment, calculate the realization ratio, and apply it to open pipeline before running any forecast math. Then compare the closed-won average against what your model produced last quarter. If deals are closing for less than they did a year ago, that is worth understanding on its own, because falling deal size is one of the clearest signals of competitive pricing pressure. The weighting mechanics that sit on top of this are covered in weighted pipeline.
What causes average deal size to change?
Competitive pricing pressure, segment mix shifts, and packaging changes, and only one of them is under your control.A new competitor entering your market creates pricing pressure and the outcome shows up as smaller average deals. Nothing about your execution changed. The market did. Discounting rises quietly, deal by deal, and the aggregate becomes visible a quarter later.
Segment mix is the second cause and it is frequently misread. If SMB deal volume grows faster than enterprise, blended average deal size falls even when every individual segment held steady. Check segment-level averages before concluding that pricing eroded. The blended number moved and the underlying motion did not.
Packaging and pricing changes are the third. New tiers, seat minimums, and platform fees all reset the distribution. Any model still running on the prior average will be wrong in a predictable direction, so recalculate on the day the change goes live rather than waiting for the quarter to close.
How often should you recalculate?
Quarterly on a trailing window, with an immediate recalculation after any pricing or packaging change.Use a trailing window wide enough to contain enough closed deals for the number to be stable. For most B2B SaaS teams that is two to four quarters, which smooths the effect of a single unusually large deal without hiding a real trend.
Track the trend line rather than the point value. A deal size drifting down 5% per quarter for three quarters is a pattern, and the sooner you attribute it to competitive pressure, mix shift, or discounting, the sooner you can respond. Deal size feeds directly into the revenue model described in how to forecast revenue.
Frequently Asked Questions
What is the average deal size formula?
Divide total closed-won revenue for a period by the number of closed-won deals in that period. Use closed-won values rather than open pipeline values, and hold the revenue basis constant. Mixing annual contract value with total contract value across deals produces a number that describes nothing.
Why is average deal size higher in open pipeline than in closed-won deals?
Open deals carry the value a seller entered at creation, before scope negotiation, discounting, and multi-year terms are settled. Most deals close for less than the amount recorded in CRM. A pipeline averaging $80,000 per deal alongside closed-won deals averaging $40,000 is one example, and a gap like that inflates every coverage and forecast number built on it.
Should average deal size use the mean or the median?
Report both. The mean tells you the revenue contribution per deal, which is what capacity and coverage models need. The median tells you what a typical deal looks like, which is what reps and managers experience. A large gap between them means a small number of large deals are carrying the period.
How do you calculate average deal size by segment?
Filter closed-won deals by segment, then run the same division inside each filter. Segment-level averages are the only version usable for planning, because a blended figure across enterprise and SMB describes neither motion and will misprice both territory quotas and pipeline targets.
How often should average deal size be recalculated?
Quarterly, using a trailing window long enough to include enough closed deals to be stable. Recalculate immediately after a pricing change or a packaging change, since both reset the underlying distribution and any model still using the old figure will be wrong in a direction you can predict.
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