Here’s an exercise worth doing before your 2027 revenue plan goes anywhere near a board. Take the headcount number at the bottom of it, and work it backwards.
How much pipeline does that many reps need? How many accounts does that pipeline have to come from? And how many qualified accounts are actually out there to give it to them?
For a lot of revenue leaders, and the investors backing them, that last question is where things start to wobble, because the one number everything else depends on - the size and value of the qualified market - was never really measured in the first place.
Adam Kay, CRO at Goodfit, has built and pressure-tested a lot of these plans. Here are the five places he looks for the maths to break, and what to check if you’re the one being asked to sign it off.
How do most revenue plans get built?
Most of them follow the same waterfall. The board agrees a growth number - 2x, 3x, whatever the next round needs to see. The revenue leader works out how much of that will come from renewals and expansion, and how much has to come from net new. Then the net new figure gets divided by average rep quota, and out comes a headcount number.
There’s nothing wrong with any of those steps on their own. The problem is that the whole thing runs top-down, which ultimately is back to front.
"The top down works, but the bottom up doesn't. We don't know how big our market is. We don't know which accounts are likely to come into the market, and we don't really have a repeatable process by which we can confidently say we know how to generate enough pipeline for a rep to get the coverage they need to hit their quota."
1. Can the market actually support the headcount number?
Headcount gets more scrutiny than almost anything else in a plan, but it’s an output - so the most useful thing you can do with it is turn it back into pipeline.
Say the board wants 3x, and once renewals and expansion are accounted for, the plan needs $8M of new business. If an AE carries a $1M quota, that’s eight fully ramped reps, or around eleven once you allow for ramp time and people leaving.
At a 20% win rate and a $60K average deal size, each rep needs roughly 17 wins a year, which means around 83 opportunities. Across eleven reps, that’s about 900 opportunities - and if half your initial conversations turn into opportunities, you need around 1,800 of those.
Now put that against the market. If there are 10,000 qualified accounts out there, the plan needs to start a sales conversation with nearly a fifth of them in a single year.
"That doesn't feel realistic. To me it’s clear there’s a problem with that plan."
When the numbers land somewhere like that, adding more reps won’t fix it. The levers that actually move the maths are win rate, deal size, or a more accurate view of the market itself.
If you’re on the board, ask what share of the qualified market the plan needs to convert in a year - and whether anyone who has actually sold into that market thinks it’s achievable. |
2. What is the market number actually made of?
In that example, the 10,000 qualified accounts were taken as a given. In a real plan, that number is doing a lot of heavy lifting. If the qualified market is actually closer to 6,000 accounts, the same plan needs a conversation with nearly a third of it. If it’s 15,000, the maths starts to look a little more comfortable.
So it’s worth asking where that number came from - because in most plans, the market figure is the one that gets examined the least.
Usually it’s a TAM, built by filtering a database on industry, company size and geography. Those filters were the best option available for a long time, but they’re proxies - and the gap between the proxy and who actually buys is often bigger than people expect.
Goodfit analysed its customer base and discovered that on average, 70% of the accounts in a B2B company’s CRM don’t meet the criteria of a genuinely qualified account. At the same time, around 60% of the accounts a company could sell to aren’t in their CRM because the filters missed them. The criteria that made them a good fit - the size of a specific team, whether they offer integrations, or whether they handle sensitive data - can’t be searched for with standard filters.
So the market number in a plan can be wrong both by inflated account numbers that were never going to buy, and missing a lot of the ones that would.
To make matters worse, on average, 7% of a qualified market changes every month, with accounts both dropping out and qualifying in. Start the year with 10,000 accurate accounts, and by December only around 6,700 still fit, while thousands of new ones have qualified that the original list never captured. Yet most plans set that number once and never go back to it.
For investors, the question is how the qualified market was built and when it was last checked. If the answer is a set of database filters pulled at the start of the year, every other number in the plan is resting on something shaky. |
3. Why do averages make a plan look safer than it is?
Most plans run on one average win rate and one average deal size, applied to every account in the market. This helps make a plan easier to explain, but real markets don’t behave like that.
A more useful way to look at a market is through expected value:
“Expected value is the approach we’ve built at Goodfit to put a figure on every account before anyone has spoken to them. It’s made up of two predictions, both built from a company’s own history of won and lost deals - how likely an account is to close, and how much it’s likely to be worth if it does. Multiply the two together and you get that account’s expected value. It won’t tell you whether one specific deal is going to close - any single account will either sign or it won’t. But across a whole market, those figures add up to something you can actually plan against, and they show you how much more one account is worth than another.”
Take two accounts that would both sit in the top tier of a traditional model. One might sign at $30,000 with a 25% chance of winning, which gives it an expected value of $7,500. The other might sign at $180,000 with a 15% chance, an expected value of $27,000. An average treats them as the same kind of account. In reality, one is worth more than three times the other.
Scale that up and averages can hide a lot. Two markets of 8,000 accounts can support completely different plans depending on how much of their value sits in the best-fit accounts.
Grading every account by expected value gives you a bottom-up view of what the market can realistically produce, which is a much more honest thing to hold a growth target up against. Harrison Rose, Goodfit Co-founder and CEO went into how Goodfit builds those predictions at this year’s EUVC Summit.
If you’re backing the plan, it’s worth asking whether the growth target has been checked against the total value of the qualified market, or just against an average deal size multiplied by a TAM. Only one of those tells you whether the number can actually be reached. |
4. Where does the spend leak?
Once the pipeline target is set, it gets turned into marketing budget, tooling and outreach - usually costed at a blended CAC across the whole market. That’s where two other problems tend to creep in.
The first is spending the same amount on accounts that are worth very different amounts. One Goodfit customer, a spend management company, graded a market of around 240,000 accounts and worked out what an account needed to be worth to cover the cost of an SDR’s time - in their case, around $2,150 in expected value. Accounts above that line got human outreach, accounts below it went into a lower-cost, automated motion. Without a value on each account, you can’t draw that line, so the most expensive motion ends up spread across everyone.
The second is reaching for more signals to fill the gap. Funding rounds, job changes and website visits - in theory, they tell you when to reach out. In practice, almost every team now has access to the same ones.
"Everyone's got the same signals. When somebody raises funding or somebody moves jobs or somebody's on your website, everyone gets that. So suddenly signals become very commoditised and generic."
Point those signals at a CRM where most of the accounts don’t qualify, and you get a lot more activity on the wrong accounts. A signal can tell you something happened at an account, but it can’t tell you whether that account was worth acting on in the first place.
Once your market is mapped and graded, signals become a lot more useful - because you already know what each account is worth before the signal comes, so you can decide whether it deserves any action, and with what level of effort.
Adam explains it with a simple scenario. Imagine two accounts in your market visit your website on the same morning. One is a tier A account, with a high predicted ACV and a strong chance of winning. The other is a tier D account, with a much lower ACV and a lower win rate.
“If you don’t have that information beforehand, you treat them both the same. Whereas if you know these are two different value companies, your approach is probably going to be quite different, despite the signal being identical.”
With a graded market behind it, the tier A visit gets your best people and your fastest response, while the tier D visit can go into an automated programme. The same applies to every other signal - a funding round or a new hire at a top-tier account is a reason to move quickly, while the same event at a low-value account might not warrant a rep’s time at all.
That’s also where the spend comes back into line. Instead of every alert pulling on the same budget, the response is matched to what the account is actually worth.
A useful question for any board - does spend change depending on what an account is worth, or does every account get the same motion? A single blended CAC can make a plan look efficient, while a large share of the budget goes on accounts that were never going to pay it back. |
5. Which assumptions are being treated as fixed?
Every plan relies on a handful of ratios that get set at the start of the year and rarely touched again - accounts per rep, opportunities per BDR and customers per account manager.
Those ratios depend a lot on the quality of the accounts being worked. A rep with a well-defined book of best-fit accounts can get through it far faster than one sifting through a CRM full of poor fits.
AI is moving those ratios too, and nobody really knows by how much yet. Bake big productivity gains into the plan from day one and you risk being under-resourced. Ignore them and you risk hiring people you didn’t need.
Adam’s way round this is to plan headcount as a range rather than a fixed number - three to five reps rather than four, for example - and agree upfront what would move it. A revenue trigger protects runway - if targets start to slip, hiring and spend slow down early. A capacity trigger checks whether the existing team can carry more before the next hire goes in.
For investors, a headcount range with agreed triggers is often a healthier sign than a fixed number. It shows the team has thought about what would make them slow down, as well as what would make them speed up - and it protects runway if the assumptions don’t hold. |
Why does every crack lead back to the market?
Go back through each of those and you’ll notice the same thing keeps coming up. The headcount test needs a real market size. The market size needs real qualification criteria. The averages need a value on every account. The spend needs to know where that value sits. And the ratios depend on how good the accounts in the book actually are.
In other words, almost every weak point in the plan traces back to one input. That’s why plans that look fine from the top down so often start to unravel by Q3.
Mapping your market is how you fix that input. It means defining what a qualified account looks like based on what your won customers genuinely have in common, finding every account that matches - including the ones standard filters miss - grading each of them by expected value, and keeping that view up to date as the market moves.
The impact of this work can be seen in results faster than you’d think. When Chili Piper moved from reps choosing which accounts to work each morning to distributing accounts by predicted expected value, average contract value rose 30% within 30 days - before anything else about how the team sold had changed.
And at NatWest Cushon, working a mapped and graded market halved the time it took new SDRs to book their first five meetings, from two months to one - a month of salary and ramp that no longer has to be earned back.
Where should you start?
Look backwards before you look forwards. Take the accounts your team has worked over the last few months, grade them retrospectively, and compare the revenue they produced with what the same effort would have produced in your best-fit accounts.
That gap will tell you a lot about your 2027 plan - including whether it needs more people, or a better map of the market it’s built on. It’s also a far more convincing thing to bring into a board meeting than a headcount number on its own.
“The winners will be those that maintain focus on the core principles - building great teams, investing in your people and truly knowing your market as a foundational layer from which all of the innovation and modern capabilities can be deployed.”
About the author
Goodfit is the GTM intelligence platform that helps revenue leaders define, source and grade every account in their market, so they know what accounts to target and how to allocate resources against them.
