B2B SaaS · publishing platform 2024–2026 Sole marketing leader

Building Paperturn's SaaS unit economics from zero

From no unit economics to fiscal discipline in two quarters

Executive summary

What existed

When I joined, Paperturn had no SaaS unit economics: no way to measure how much a customer was worth over their lifetime, how fast they cancelled, or how long it took to earn back acquisition cost. Acquisition decisions were grounded in first-payment amounts rather than recurring-revenue economics, which is the framing a SaaS unit-economics layer is built to replace.

What I built

The measurement layer Paperturn lacked: customer-lifetime-value formula, cancellation definitions, retention tracking, and a CAC payback model with closed-form formula and 5-tier country framework. I defined, shipped, and carried marketing decisions on it.

What changed

19 → 11 mo 42% reduction in CAC payback between Q3 2025 and Q1 2026

37% → 55% annual subscriber mix shift

The framework to achieve efficiency is still running. Acquisition targets it sets are the operational benchmarks today.

Why measurement had to come first

Without a measurement layer, no benchmark could apply and no target would be fair.

SaaS targets revolve around customer and trial acquisition (both volume and cost), and they only mean something compared against benchmarks fair to your business. Measurement is the infrastructure that makes both possible. With it, you can build decision frameworks that greenlight or kill campaigns and segments.

We entertained two “true north” KPIs (CAC payback and a 3:1 LTV:CAC ratio) and settled on a 9–12 month CAC payback target, country-dependent.

A strategic consultant flagged the reasoning: a product-led-growth company (where customers sign themselves up rather than going through a sales team) typically has higher cancellation rates than B2B SaaS. That uncertainty makes CAC payback the more fiscally responsible metric: it ensures the cash actually comes back. LTV:CAC targets were reserved for time-tested winners: campaigns and country segments where we could afford a higher acquisition cost.

The methodology

Simple vs empirical payback.

I shipped both because the contrast itself mattered. The simple equation is good for speed and quick comparisons; the empirical method allows you to understand cohort behavior across time.

When you run these equations separately for monthly vs annual cohorts (groups of customers acquired in the same period, paying monthly or yearly), by country, by acquisition window, the segmentation reveals where efficiency actually lives — and where it doesn’t. Same standards applied, just with different inputs.

A formula that gives you a target.
The most useful question for a marketing team is also the simplest: at our cancellation rate, what’s the worst acquisition cost we can afford and still hit a 12-month payback? Once the empirical model is set up, that question collapses into a single equation:

required ARR/CAC=\text{required ARR/CAC} =12cg×(1(1c)N)\frac{12c}{g \times \left(1 - (1-c)^{N}\right)}

where c = monthly cancellation rate, g = gross margin, N = payback target in months.

At Paperturn’s gross margin and a 12-month target, this simplifies to a rule of thumb that fits on a sticky note:

required ratio1+kc\text{required ratio} \approx 1 + k \, c

where k is a constant derived from gross margin and the payback target.

Plug in your cancellation rate, get your target efficiency ratio. The same formula run backwards tells you the maximum cancellation rate any observed efficiency ratio can sustain.

Segregating monthly vs annual.
Blending monthly and annual cohorts was the default. Subscription-business thinking, segregating the two, was not yet the lens in reporting. Same acquisition cost per account, same gross margin, different cancellation patterns over time: the annual cohort earned its acquisition cost back roughly twice as fast as the monthly cohort. Same dollars in, meaningfully different dollars out. The reallocation toward annual-producing geographies followed downstream.

The 5-tier country framework

Across the top markets, each had a different efficiency, payback, and trial-to-paid conversion profile. Naming them one-by-one was unwieldy. Meetings got stuck on edge cases. Tiering forces the action: the label dictates the decision. Country labels below are anonymized; ratios and payback months are preserved so the methodology stands on its own.

#MarketEfficiencyEmpirical PaybackTier · Action
1A2.03×8 moA: Compounder · scale
2B1.43×13 moB: Workhorse · maintain
3C1.36×14 moB: Workhorse · maintain
4D1.29×15 moB: Workhorse · maintain
5E1.16×18 moC: Bifurcated · annual-only
6F1.09×19 moC: Bifurcated · annual-only
7G0.97×20 moD: Marginal · pull paid
8H0.95×24 moE: Broken
9I0.86×25 moE: Broken
10J0.55×>36 moE: Broken

Bifurcated = annual customers earn payback, monthly customers don’t, in the same market.

The reallocation

By late 2024, blended acquisition cost had inflated significantly. Google’s AI Overviews had pushed organic traffic into paid search and bid prices with it. Cancellations and revenue per customer were stable; the problem was acquisition. The framework named which markets to act on.

Adding up the cuts across underperforming geographies and monthly-heavy campaigns gave a reallocatable five-figure DKK monthly ad budget: a portfolio shift, not a budget cut, trading unprofitable volume for durable quality. The founder approved.

The Q1 2026 recovery, and country-level honesty about it

Portfolio recovery was real and material: empirical payback 19 → 11 mo (−42%).

Empirical CAC payback · portfolio −42%
Q3 2025
19 mo
Q1 2026
11 mo

But it was country-uneven. Denmark posted its best-ever quarter and recovered fastest in the portfolio. Australia and France also recovered. The UK had been declining since its Q3 2025 peak. The US’s Q1 2026 was the second-worst US quarter on record. Competitors had begun aggressively bidding on our branded search terms and running comparison SEO campaigns, and click prices in our highest-value market reflected that.

A blended-portfolio recovery number alone would have hidden the country-level reality: Denmark recovering, the US deteriorating. The methodology was built to surface exactly that distinction.

What persists

All four artifacts I built are still running: the customer-lifetime-value formula, cancellation definitions and dashboards, the methodology for tracking whether customers spend more over time, and the CAC payback model with its 5-tier framework. The trial-conversion and acquisition-cost targets I set are the paid agency’s operational benchmarks today.