Most B2B revenue plans fail before a single campaign launches — because the funnel math is internally inconsistent. The revenue goal, the budget, the conversion assumptions, and the sales cycle don’t reconcile. Nobody does the translation between “we want $3M in new ARR” and “here’s the volume, velocity, and conversion chain that has to hold for that to be reachable.” I built a free calculator to force that translation into the open.
Every new client engagement starts the same way. They hand me three numbers: a revenue goal, a time horizon, and a marketing budget. Sometimes there’s a fourth — a headcount assumption.
Then they look at me and ask: does this work?
I’ve been asked this question at least a dozen times across my career. And about nine of those times, before I said a word, I already knew the answer was no. Not because the goal was too ambitious. Not because the market wasn’t there. Because the math didn’t clear. The inputs were internally inconsistent in ways that no amount of good execution could fix.
The problem isn’t that founders and CROs set unrealistic goals. It’s that nobody has ever shown them the mechanism that connects their budget to their number — the actual chain of conversion, velocity, and volume that has to hold for the revenue target to be reachable. So they pick numbers that feel right and call it a plan.
I got tired of rebuilding that model from scratch every time. So I built a tool.
Why Is Bad Pipeline Math Invisible Until It’s Too Late?
Here’s how bad pipeline math usually plays out.
A company wants $3M in new ARR this year. They have $600K in marketing budget and a 90-day average sales cycle. Their close rate is around 20%, and their average contract is $75K.
Run the numbers and you need 40 closed deals. At 20% close, that’s 200 qualified opportunities. At a typical SQL-to-opportunity rate of 40%, that’s 500 SQLs. If marketing sources 60% of pipeline, you need 300 marketing-sourced SQLs — in a year, with a 90-day sales cycle, which means you’re really working with about three effective pipeline windows.
That’s 100 marketing-sourced SQLs per quarter. From a $600K budget. With no brand presence, no existing content engine, and a sales team that’s still figuring out the pitch.
Can it happen? Maybe, in a best-case scenario with everything going right. Is it the plan? No — the plan was “$3M ARR, $600K budget, let’s go.”
This is the gap I kept encountering: between the revenue goal that gets presented to the board and the funnel reality that has to exist for that goal to be reachable. Nobody was doing the translation. Or if they were, they were doing it in their head, trusting their gut, and moving on.
The problem with gut math is that it has no error bars. You can’t see where you’re optimistic. You can’t see what has to break for the model to fail. You can’t tell a founder “your budget isn’t wrong, your close rate assumption is” — because the close rate assumption was never made explicit.
What Does the B2B Funnel Velocity Calculator Actually Do?
The B2B Funnel Velocity Calculator forces all the assumptions into the open.
You put in your revenue goal. Your average selling price. Your time horizon. Your marketing budget. Then you work through the conversion chain: lead-to-MQL, MQL-to-SQL, SQL-to-opportunity, opportunity-to-close. You set your sales cycle length. You define what percentage of pipeline marketing owns versus sales development or inbound.
The model works both directions. You can start with the revenue goal and see what volume and conversion rates you need to hit it — what I call the demand-backward view. Or you can start with your actual funnel metrics and see what revenue they support — the capacity-forward view. Most planning processes only go one direction. The disconnect between the two is where bad plans live.
The calculator also surfaces unit economics: cost per lead, cost per SQL, cost per closed deal. These aren’t vanity metrics — they’re guardrails. And they connect directly to stage-appropriate metrics that shift as your company matures. If your model requires a cost-per-SQL of $180 but your industry benchmarks are $600–$900, that’s not a budget problem to solve later. That’s a plan that doesn’t work, identifiable before you spend a dollar.
The last piece is what I call the Trigger Model. Given your sales cycle and your planning horizon, when do activities in Q1 actually show up as closed revenue? If your sales cycle is 90 days and you want deals closed by December 31, your pipeline generation window closes in early October. Every campaign you launch after that is working for next year’s number, not this year’s. Most revenue plans ignore this entirely — they assume a uniform pipeline contribution across all 12 months and then wonder why Q4 falls short.
How Does the Physics of Growth™ Framework Explain Pipeline Failure?
In the Physics of Growth™ framework I use to think about GTM systems, this is a problem of distance and velocity.
Distance is how far you are from your revenue goal. Velocity is the rate at which your funnel moves deals toward close. The uncomfortable truth is that most early-stage companies have no empirical read on their velocity. They’re estimating. And estimates compound: a 10% error in your close rate assumption, a 15% error in your cycle time, a 20% error in your SQL conversion — stack those up and your model is off by half before you’ve run a single campaign. It’s the friction that kills deals before they start — compounding resistance that no amount of top-of-funnel volume can overcome.
Mass matters here too. A company with strong brand presence, a reference customer base, and an active content program generates pipeline at a different efficiency than a company with none of those things. Same budget, same tactics, different physics. The calculator lets you dial in a demand generation efficiency assumption — essentially a forcing function to ask: are we modeling this like a category leader or like a startup? Those aren’t the same equation.
The insight I kept coming back to — the one that made me build the tool — is that most B2B revenue plans aren’t plans. They’re wishes with spreadsheets attached. The goal is set top-down, the budget is allocated, and somewhere in between, everyone agrees to believe the funnel will work out. The calculator breaks that agreement. It makes the mechanism visible. And once the mechanism is visible, the real conversation can start.
If this kind of systems thinking about GTM resonates, you might also find value in how I think about fractional CMO engagements and GTM architecture for growth-stage companies.
What Should Founders, CROs, and Marketing Leaders Do With This?
If you’re a founder or CRO: Run your current revenue plan through the calculator before your next board meeting. Not to validate it — to pressure-test it. Specifically: what does your model assume about your close rate? Your SQL volume? Your marketing-sourced percentage? If those assumptions have never been stated explicitly, they’ve never been defended. The board will ask eventually. Better to find the gaps now.
If you’re a marketing leader: Use the demand-backward view to reframe budget conversations. Instead of asking for more budget in the abstract, show what your current budget can produce in terms of closed revenue — and what additional budget would buy in incremental pipeline. That’s a capital allocation argument, not a marketing argument. It lands differently.
In both cases: Pay attention to the unit economics output. If the model requires a cost-per-closed-deal that’s lower than what your industry typically supports, you have three options — increase budget, increase conversion rates, or reduce the revenue target. Those are the only levers. The calculator makes that choice explicit rather than invisible.
The tool is free. The math has always been there. Most plans just haven’t been willing to look at it.
Frequently Asked Questions
How accurate is a funnel velocity model for early-stage companies? It’s as accurate as your inputs. For a company with 12+ months of funnel data, the model is highly predictive. For a pre-revenue startup estimating conversion rates, it’s a scenario-planning tool — useful for bracketing outcomes and identifying which assumptions matter most, not for producing a single “right” number. The value isn’t precision; it’s making the assumptions explicit.
What conversion rates should a Series A B2B SaaS company use as benchmarks? Industry averages for B2B SaaS: lead-to-MQL conversion around 15–25%, MQL-to-SQL around 30–40%, SQL-to-opportunity around 40–60%, and opportunity-to-close around 15–25%. Your mileage will vary by deal size, sales cycle, and market maturity. Use these as starting points, then replace with your actual data as you collect it.
How does sales cycle length affect pipeline planning? Dramatically. A 90-day sales cycle means any pipeline generated in Q4 won’t close until Q1 of the following year. Most annual revenue plans ignore this — they assume pipeline contribution is uniform across all 12 months. The Trigger Model in the calculator makes this time lag visible so you can plan pipeline generation windows accurately.
Should marketing own 100% of pipeline generation? No. In most B2B SaaS companies, marketing sources 40–60% of pipeline, with the remainder coming from sales development, partnerships, and inbound referral. The calculator lets you set this ratio explicitly. If your model assumes marketing sources 80% of pipeline but your SDR team generates half your SQLs, the model is wrong before you start.
What’s the most common mistake founders make in revenue planning? Setting the revenue target and the marketing budget independently, without reconciling the funnel math between them. A $5M ARR target with a $200K marketing budget isn’t ambitious — it’s impossible in most B2B contexts. The calculator makes this gap visible in under five minutes. Better to know now than to discover it in Q3.