Model your funnel from leads to MQLs, SQLs, and closed deals to project pipeline, revenue, ROI, and cost per stage — all from a single marketing spend figure.
Results update live as you type
Each stage multiplies by its conversion rate. All figures are estimates for planning purposes.
Cost / Lead
$40.00
Cost / SQL
$285.71
CAC
$1,142.86
MQLs, SQLs, and customers compound through each conversion rate you set.
Cost per SQL is often the cleanest efficiency signal in a long B2B cycle.
Match revenue to the period your spend actually influenced for accurate ROI.
B2B revenue is earned stage by stage. Understanding each one is how you find the leaks worth fixing.
Leads enter the top, become MQLs when they show real interest, convert to SQLs once sales accepts them, then close as customers. Each stage filters the last.
An MQL is marketing-qualified by behavior; an SQL is sales-accepted as a true opportunity. The handoff between them is where many B2B funnels leak value.
B2B deals can take months. Spend in one quarter may close in the next, so ROI must use matching time windows or it will look artificially low.
Average contract value (ACV) is the lever that justifies higher acquisition costs. A larger deal size lets you profitably spend more per lead and per SQL.
Cost per SQL = spend ÷ SQLs. Because SQLs are real opportunities, this metric predicts pipeline far better than cost per raw lead.
Target the weakest stage. Lifting a 25% MQL-to-SQL rate to 35% flows through every downstream stage, multiplying customers and ROI without more spend.
B2B funnels compound small errors: a wrong rate at the top distorts everything below it.
A B2B pipeline model multiplies four or five conversion rates in sequence. That structure is unforgiving — a stage rate that is optimistic by ten percentage points does not make the forecast ten percent wrong, it can make it wrong by a factor of two by the time it reaches closed revenue.
The discipline that fixes this is using observed rates from your own CRM rather than industry averages, and modelling a range rather than a single number. If the pessimistic case still justifies the spend, you have a decision you can act on.
| Stage transition | Typical rate | What moves it |
|---|---|---|
| Visitor → lead | 1% – 3% | Offer strength and form friction dominate. |
| Lead → MQL | 20% – 40% | Depends entirely on how strictly you define an MQL. |
| MQL → SQL | 30% – 50% | Sales acceptance. Low rates usually mean marketing is over-qualifying. |
| SQL → opportunity | 40% – 60% | Discovery quality and fit assessment. |
| Opportunity → closed won | 20% – 35% | Competitive position and pricing. |
| Visitor → customer (end to end) | 0.1% – 0.5% | The compound result of everything above. |
End-to-end conversion in B2B is usually a fraction of one percent. A model implying two or three percent is almost certainly using a definition of "lead" that excludes most of the funnel.
A six-month cycle means spend in January produces revenue in July. Model the lag or the plan will look like it is failing for two quarters.
B2B revenue is usually concentrated in a few large deals. An average deal value hides that, and a model built on the mean will miss badly in both directions.
If marketing and sales define an MQL differently, every rate in the model is measuring something ambiguous. Agree the definitions before the arithmetic.
Pipeline beyond what the sales team can work is not pipeline. Model capacity as a hard ceiling, not an afterthought.
In B2B, much of the value arrives through renewals and upsells. Excluding it understates return and leads to systematic underinvestment.
Long cycles with many touchpoints defeat last-click entirely. Self-reported attribution on the enquiry form is crude but often more accurate.
Benchmarks are for sanity-checking outputs, not for populating a model. Your own historical rates, however imperfect, are more predictive.
One set of point estimates gives false precision. Run pessimistic, expected and optimistic cases and make the decision on the pessimistic one.
In B2B, a large share of opportunities die without choosing anyone. Treating every loss as competitive misdiagnoses the problem.
With a handful of deals a quarter, stage rates are statistically meaningless. Widen the window or accept the model is directional only.
The most valuable output of a funnel model is usually not the revenue figure but the bottleneck it exposes. When you can see which single transition is costing you the most closed revenue, the investment decision stops being about budget size and becomes about where the next hour of work should go — which is a far more useful question.
We build demand-gen and pipeline programs for B2B teams across Canada. No long-term contracts.