Why Pipeline Velocity Trumps Deal Size Every Time

Lucas: So there's this number that keeps showing up in sales meetings I've been sitting in on lately. Everyone talks about average deal size. 'We need to increase our average deal size.' It sounds smart, right?

Larger deals, more revenue per close. Luna: I hear that all the time. And I get the instinct - bigger is better on paper. Lucas: Right.

But when you actually look at the data, average deal size is often a vanity metric. It can hide what's really going on in your pipeline. A team that closes a few giant deals late in the quarter looks great on that one number, but their forecast is a mess. Meanwhile, a team that moves smaller deals fast - I mean consistently fast - often produces more predictable revenue over twelve months.

Luna: So you're saying velocity beats size? That feels counterintuitive for a lot of VPs of Sales. Lucas: It does. But let me give you a concrete example.

Gong published a study a couple years back looking at over a million sales calls. They found that high-velocity teams - those that moved deals from first contact to close in under 60 days - had win rates nearly twenty percent higher than teams that dragged deals past ninety days. And their average deal size? Actually slightly lower.

But their annual revenue per rep was fifteen to twenty percent higher because they were closing more deals, more often. Luna: So the math is really about throughput. More cycles per year. Lucas: Exactly.

If your average sales cycle is ninety days, you can close four deals per rep per year. If you shorten that to seventy-two days - that's a twenty percent improvement - you get five deals. That's twenty-five percent more revenue from the same headcount, assuming deal size stays constant. And if deal size actually drops a little because you're going after faster-moving opportunities?

You still come out ahead in total revenue. Luna: Okay but - and I have to push here - doesn't this break down for enterprise sales? Some of those deals just take nine months because of procurement and legal reviews. You can't speed that up just by wanting to.

Lucas: You're right that the extreme long tail is different. A ten million dollar deal with a Fortune 100 company has its own rhythm. But even there, the principle holds. The question isn't 'how fast can we close this one deal?'

It's 'within the deals we're already pursuing, where is time being wasted?' Most enterprise sales teams have massive variability in stage duration. They might have a demo stage that takes thirty days for some deals and ten for others, with no good reason for the difference. Luna: So the real leverage is identifying the bottlenecks stage by stage.

Lucas: Yes. And that's where pipeline velocity as a framework is more useful than just tracking total cycle time. Salesforce actually has a built-in velocity metric: pipeline value multiplied by win rate, divided by sales cycle length. But I think that formula misses something.

What you really need is stage-level velocity. How long are deals sitting in the 'discovery' stage? How long in 'demo'? How long in 'negotiation'?

Luna: And then you compare that to your historical benchmarks for won deals versus lost deals. Lucas: Exactly. One of the most powerful things I've seen a company do - a mid-market SaaS firm I worked with last year - they started tracking time-in-stage by rep. They discovered that their top performers spent no more than five days in the 'demo' stage.

Their bottom performers averaged eighteen days. Same product, same pricing, same ICP. The difference was that top performers were qualifying out faster. They didn't waste time giving demos to deals that were never going to close.

Luna: So they were using velocity as a qualification signal, not just a measure of speed. Lucas: Right. They actually built a simple rule: if a deal stays in 'discovery' for more than fourteen days with no next step booked, it gets automatically flagged for review. That alone cut their average sales cycle by twenty-two percent in six months.

And their win rate went up, not down, because they were spending more time on deals that actually moved. Luna: That's a really concrete takeaway. I like that it's not about pushing reps to rush - it's about creating visibility into where deals stall. Lucas: Exactly.

And the reason I'm so focused on this right now is that we're halfway through 2026, and a lot of B2B teams are feeling pressure to make their numbers. The easy reaction is to chase bigger deals, offer discounts, extend terms. But that actually slows you down. Bigger deals take longer to approve, longer to negotiate, longer to implement.

They increase your sales cycle and decrease your predictability. Luna: So what's the first step for a team that wants to pivot to velocity? They probably don't have the Gong data sitting around. Lucas: They don't need it.

Start with your CRM. Export every deal closed in the last twelve months. For each stage, calculate the median number of days. Not the average - median, because outliers will skew it.

Then look at your won deals versus lost deals. Where do the lost deals spend more time? That's almost always your bottleneck. In my experience, for most B2B companies, it's either the demo stage or the proposal stage.

Luna: And the fix is usually process change, not rep coaching. Like automating proposal generation or standardizing demo criteria. Lucas: Right. One company found that their reps were spending four days on average customizing proposals.

They implemented a proposal tool with templates and clause libraries. Dropped to one day. Their cycle shortened, their win rate actually increased because proposals were more consistent, and they closed more deals in the quarter. That's the kind of leverage you don't get from just demanding 'sell bigger.'

Luna: I want to circle back to something you said earlier about this being a 2026 pressure moment. Are you seeing more teams adopt velocity metrics this year compared to, say, 2023? Lucas: Definitely. I think a lot of the noise around AI in sales over the past two years has actually pushed teams to get better at process before layering on tools.

The companies that are succeeding with AI sales assistants are the ones that already have clean data on their pipeline stages. Velocity metrics become the foundation. Without that, AI is just automating a messy process faster. Luna: That's a really good point.

Clean process first, then automation. Lucas: Exactly. And I'll tell you - if today's conversation made you think about your own pipeline in a new way, or gave you one framework to try, honestly, that's the whole point of this show. If it was worth a coffee to you, you can find us at buy me a coffee dot com slash fexingo.

No pressure, but listener support is what keeps this ad-free. And we love hearing that these episodes are actually useful. Luna: Yeah, it really does help. And we read every message.

Lucas: So back to velocity - one more number I think is worth knowing. For a typical B2B SaaS company with a sixty-day sales cycle and a twenty percent win rate on opportunities, a ten percent reduction in cycle time yields about a seven percent increase in annual bookings. That's real money, and it doesn't require hiring more reps or raising prices. It's just discipline on stage duration.

Luna: That's the kind of math that gets a CFO's attention. Lucas: It should. Because the alternative - chasing bigger deals - usually leads to lower win rates, longer cycles, and less predictable revenue. So next time someone in your pipeline review says 'we need bigger deals,' ask them how fast the current deals are moving first.

Luna: And if they don't know, you've found your real problem. Lucas: Exactly. That's the conversation.