Model the growth equation

Write your key metric as a formula, put today’s values in it, and you can see which input has to move for the goal to be reachable. It takes a spreadsheet and a day of data work, and it usually settles arguments that have been running for months.

The deliverable is a one-tab model: the equation, the current value of each input, and a scenario table. As an illustration, take a B2B SaaS at 2M ARR with two motions, self-serve trials and a sales team for larger accounts. The goal is 3M ARR in 12 months. All figures in this chapter are invented for the example.

Choose one key metric that means money

The metric at the top has to move when the business really gets better, and to be hard to inflate by accident. Signups and traffic fail that test, because you can grow both while earning less.

Business model	Key metric	Why
B2B SaaS	Net new ARR	Captures acquisition, expansion and churn in one number
Consumer subscription	Net new subscribers, or MRR	Same logic, with shorter cycles
E-commerce	Contribution margin after marketing	Revenue can grow while margin disappears
Marketplace	Transaction volume on the side that constrains supply	Growing the wrong side adds nothing

Use the metric leadership will accept in the framing note. If it reports ARR and you model signups, your conclusions will have no owner.

Write the equation down to inputs a team can move

Break the metric down until each term is something a named team can change. Keep separate branches for motions that behave differently, because a blended conversion rate between self-serve and sales describes nobody.

net_new_arr         = new_arr + expansion_arr - lost_arr
new_arr             = self_serve_new_arr + sales_led_new_arr
self_serve_new_arr  = signups * activation_rate * activated_to_paid * self_serve_acv
sales_led_new_arr   = qualified_opportunities * win_rate * sales_acv
expansion_arr       = opening_arr * expansion_rate
lost_arr            = opening_arr * gross_arr_churn_rate

Now the current year for our example:

Input	Value	Result
Signups	6,000
Activation rate	30%	1,800 activated
Activated to paid	20%	360 customers
Self-serve ACV	€800	€288,000
Qualified opportunities	120
Win rate	25%	30 deals
Sales ACV	€15,000	€450,000
Expansion rate on opening ARR	6%	€120,000
Gross ARR churn on opening ARR	18%	€360,000 lost
Net new ARR		€498,000

At this pace the company ends the year at about 2.5M. The goal requires €1,000,000 of net new ARR, so the gap is roughly €500,000. Everything else in the audit is now measured against that figure.

Collect each input with a definition

Most of the work is making these numbers mean one thing. Write the definition next to the value.

Input	Definition to agree on	Source	Common trap
Signups	New accounts, excluding invited teammates	Product database	Counting users instead of accounts
Activation rate	Share of accounts reaching the value event within a fixed window	Product analytics	A value event too easy to reach
Activated to paid	By signup cohort	Product and billing	Dividing this month’s customers by this month’s signups
Qualified opportunities	Using the sales team’s written criteria	CRM	Criteria that change by quarter
Win rate	Won over opportunities closed in the period	CRM	Leaving open deals in the denominator
ACV	First-year contract value, after discounts	Billing	List price
Churn and expansion	On the ARR at the start of the period	Billing	Mixing logos and revenue

Use cohorts wherever there is a delay between two steps. If the sales cycle lasts four months, this quarter’s deals come from opportunities created earlier. Before you trust the rates, check them against the data reliability sheet.

Run scenarios on plausible changes

In a chain of multiplications, 20% more on any input gives the same result as 20% more on any other. The percentage tells you nothing. What separates the levers is how far each one can realistically go and what that costs. So give each input a range you can defend from the data or from what the team has already achieved.

Lever	Today	Hypothesis	Effect on net new ARR
Activation rate	30%	40%	+€96,000
Signups	6,000	7,200	+€57,600
Qualified opportunities	120	150	+€112,500
Win rate	25%	30%	+€90,000
Gross ARR churn	18%	14%	+€80,000
Expansion rate	6%	10%	+€80,000

Each line is computed alone, holding the others constant. Apply activation, opportunities, win rate, churn and expansion together, and new ARR reaches about €1,059,000, expansion €200,000 and lost ARR €280,000, for a net new ARR of about €979,000. So in this example, the goal needs five levers to land in the same year and still falls just short. That is what the model is for. The founder can now choose between concentrating on fewer levers with a later date, or adding one that isn’t modeled, such as pricing.

Two effects are invisible in a one-year table, so note them. Lost ARR scales with the base: 18% costs €360,000 at 2M and €900,000 at 5M, which makes retention weigh more each year. And some inputs compound. Derrick’s programmatic pages were still bringing 88 clicks a day two years after the last SEO action, as told in the Derrick case. A channel like that behaves like a stock, unlike ads that stop when the budget stops, so model it separately. The same goes for seasonality: on Grosbill’s Black Friday, ad revenue over the period was multiplied by 7, and an annual average would have hidden the moment that mattered.

Check that growth pays for itself

Add the unit economics per segment and per channel, using fully loaded costs.

cac            = (marketing + sales costs, salaries and tools included) / new customers
monthly_margin = monthly_revenue_per_account * gross_margin
payback_months = cac / monthly_margin
ltv            = monthly_margin / monthly_revenue_churn_rate

Say a sales-led account brings €1,250 a month at 80% gross margin and costs €12,000 to win: payback is 12 months. Judge that number against how long this segment actually stays and how much cash the company has, rather than against a generic benchmark. A 12-month payback is comfortable on customers who stay for years, and a serious problem on a segment that churns at month 10. Treat LTV with suspicion on a young base, where it extrapolates a lifetime nobody has observed yet.

What to deliver

The model tab, plus findings written in this form: “Reaching 3M requires €1,000,000 of net new ARR against €498,000 at the current pace. Qualified opportunities and activation carry the largest plausible gains, and retention matters more each year as the base grows. The audit will therefore go deepest on those.” Those hypotheses go straight into the funnel analysis, which tests where the rates actually break.

Your next step: write the equation for your business on one line per branch, fill in the last twelve months, and mark in red every input for which two people gave you different numbers.