How to Measure Average Deal Size in Competitive Deals

Before the average, the deal size. Ask four people in one company what a deal was worth and you will get four answers, all defensible, differing by a factor of three. None of them is a mistake; they are answers to different questions that happen to share a phrase.

For competitive work, annual recurring value is usually the right choice, because it is the one least affected by decisions a competitor had nothing to do with. A buyer signing three years instead of one is telling you about their procurement calendar.

Pick one, and never quietly switch

The damage is not in choosing wrongly, it is in choosing twice. A series where two quarters were measured on subscription value and the third on total bookings will show a jump that somebody will explain with a story about competition. Write the choice into the report header, in four words, and the whole failure mode disappears.

Closed-won only. Including lost deals produces the average size of what you pursued rather than of what you sold, which is a pipeline metric with a misleading name on it, and mixing the two is how a quarter with one enormous loss gets reported as a quarter of larger deals.

Contested pipelines in most B2B categories are not one population. They are a large group of departmental purchases and a small group of company-wide ones, and the two behave so differently that a single summary of both describes neither.

The mean is arithmetically correct and operationally useless: no deal in the set is anywhere near $89,750, so any decision sized against it is sized against a deal nobody sold. The two medians together take one extra row and describe both halves of the business.

Nearly every company that measures this finds the same thing: deals with a competitor in them are bigger than deals without one. It is tempting to read that as evidence that competitive work drives larger deals, and it is almost entirely the other way round.

Running an evaluation costs a buyer real money. Somebody has to convene a committee, write requirements, sit through demos and defend a recommendation. Below a certain spend nobody bothers, so they buy the obvious option and never tell you there was a shortlist. Above it, a comparison becomes worth the effort. The size is what caused the competition, not the reverse.

What the contested-to-uncontested gap really measures

It measures the spend threshold at which your buyers start shopping, which is a genuinely useful thing to know and has almost nothing to do with how you compete. It tells marketing where comparison content starts earning its keep, and it tells sales where a deal stops being a transaction. What it does not do is measure performance, and a programme that reports the gap rising as a result is claiming credit for a change in deal mix.

The version of this metric that does say something

Hold the competitor constant and watch the number over time. Your average contested deal against one named competitor, quarter by quarter, moves for reasons worth chasing: they repackaged and are now reaching buyers further up than before, or your own positioning has drifted into a smaller segment where they were already strong. That series is a competitive measurement. The cross-sectional gap is a market fact.

The single most useful thing to do with deal value in competitive work is not to average it. It is to use it as an axis. Cut the win rate against one competitor by deal size band and the picture usually changes completely.

Blended, this is 42 wins from 82 closed deals: 51%, unremarkable, nothing to investigate. Split by value it says something specific and urgent, which is that above roughly $75,000 you win barely a quarter of the time against this competitor. The average deal size on those same deals would have read comfortably high, because the large deals you lost are absent from a closed-won average while the large deals you won are in it.

That last point is worth sitting with. Average deal size is computed on wins, so it cannot see the shape of your losses at all. Competitive win rate by band is the cut that can, and the two together answer a question neither answers alone: are the deals we are winning the deals we wanted.

The export, and the two filters that matter

Closed-won deals for the window, with value, close date and the competitor field. Filter to won, split on whether a competitor is recorded, and compute mean and median for each side. The value column is the one to check before anything else: confirm it holds the measure you chose in the first section rather than whatever the opportunity amount field was originally set up to carry, because in most systems that field has been redefined at least once.

Where the competitor field is thin, the contested average is measuring the deals reps remembered to tag, and those skew large. Structuring the competitor field is the prerequisite here as it is for every pipeline-derived competitive number.

Comparing your figure to theirs

Your own average is half the question. What a competitor typically charges for a comparable deal is the other half, and it is a genuinely difficult research problem rather than an export. How to find a competitor’s average deal size works through the public disclosures that carry it and the error bars that come with each one. Treat any figure produced that way as an estimate with a range, and never put it beside your own exact number without saying which is which.

Every comparison this metric supports rests on something you did not write and cannot control: what the other side charges. A competitor adds a tier, moves a feature from the middle plan to the top one, or changes what a seat includes, and the deal you were benchmarking against last quarter is now a different product at a different price.

What that costs shows up in one predictable moment. Somebody argues value against a figure that was true six months ago, the buyer corrects them with today's, and the conversation stops being about value at all. That correction is almost never reported upwards, so the stale figure survives another quarter and gets used again.

Keeping that reference current is what competitive intelligence platforms are built for. Flares follows competitor pricing pages, packaging and what each tier includes, and records what changed and when, so a deal size comparison rests on this quarter’s price list rather than on a screenshot somebody took in January.

It will not give you their actual average deal size. Nothing will: the number is not published, the discounts are private, and any figure claiming otherwise is a model wearing a measurement’s clothes. What monitoring gives you is an accurate list price and an accurate packaging, which is the honest input to an estimate you then label as one.