The unit economics of this coffee subscription business are polarised by channel in a way the blended figures conceal. Contribution margin is S$15.50 per subscriber per month on S$38 MRR — a genuine 40.8% after all variable costs are applied, versus the 62% gross margin figure in the current deck which excludes S$8.00 per unit of fully variable costs. The blended CAC of S$58 fully loaded (versus S$44 media-only in the deck) produces a 3.7-month blended payback that looks reasonable in isolation. The problem is the channel composition underneath it: referral at S$18 CAC and 68% month-6 retention produces an observed LTV:CAC of approximately 7.6:1, while Meta at S$68 CAC and 44% month-6 retention produces approximately 1.3:1. The blended 1.8:1 observed LTV:CAC is an average of a genuinely strong channel and a dominant channel with weak economics — and the dominant channel is the one being considered for a budget double.
The 4% monthly churn assumption in the investor deck implies 62% retention at month 12. Observed cohort data shows 36% — a gap that more than halves LTV from a projected S$388 to an observed S$107, and drops LTV:CAC from 6.7:1 to 1.8:1. This is not a presentation choice; it is a factual discrepancy that changes the fundamental conclusion about growth-readiness. Doubling Meta spend from S$30,000 to S$60,000 per month cannot be justified by the observed economics: Meta CAC has risen 39% in six months, Meta cohort retention runs 8 percentage points below blended, and the channel's observed LTV:CAC of 1.3:1 sits well below the 3:1 growth-readiness threshold. The stronger capital allocation is to hold Meta, cut influencer, and redirect budget toward the referral programme — the channel with the best unit economics and the least investment to date.
| Metric | Classification | Reason |
|---|---|---|
| Referral CAC (S$18) | Reliable | Simple denominator, clean payout structure that only triggers on the second order, stable trend across the observed period. No allocation complexity. |
| Contribution margin (S$15.50 per subscriber per month) | Reliable | Every cost line is named, variable, and scales directly with subscriber volume. Coffee and packaging, shipping, payment processing, support, and refunds are all accounted for. This is the most trustworthy number in the model. |
| Channel-level retention at month 6 (Meta 44%, Referral 68%) | Reliable | Both cohorts carry sufficient volume to be statistically meaningful. The 24-percentage-point gap between them is real signal — it is directionally reliable and large enough that measurement error does not explain it. |
| Payback period by channel | Reliable | Arithmetic output of two reliable inputs — fully-loaded CAC and contribution margin per unit. The outputs are as trustworthy as the inputs that drive them. |
| Blended CAC (S$58) | Directional | Correct methodology and fully loaded, but the blended figure masks a spread from S$18 (referral) to S$95 (influencer). Useful as a summary figure; misleading as a basis for channel-level decisions. |
| Blended retention curve (36% at month 12) | Directional | The month-12 figure is real, but the blended cohort is dominated by Meta at 55% of acquisition volume. It simultaneously understates the referral cohort experience and overstates it for Meta subscribers. Channel-level retention is more useful than the blend. |
| Google Search retention | Directional | No channel-specific cohort data available — assumed at blended average. Probably in the right ballpark given conversion intent from search, but unverified and should not be treated as confirmed. |
| 62% gross margin | Misleading | Excludes S$7.00 per subscriber per month in fully variable costs — shipping (S$5.20), payment processing (S$1.30), support (S$0.90), and refunds (S$0.60) are all omitted. Overstates the economics by 21 percentage points relative to the true contribution margin of 40.8%. This figure should not appear in investor materials without a clear footnote defining what it excludes. |
| 4% monthly churn assumption | Misleading | Implies approximately 62% retention at month 12. Observed cohort data shows 36% — a 26-percentage-point gap that more than halves the LTV figure and drops LTV:CAC from a projected 6.7:1 to an observed 1.8:1. This is the single assumption most likely to mislead investors into believing the model is growth-ready when the evidence does not yet support that conclusion. |
| S$44 blended CAC (deck figure) | Misleading | Media spend only — excludes the fully-loaded growth hire and tools allocation. Understates true blended CAC by S$14, a 32% understatement. Already flagged for correction in the updated deck. |
| Influencer LTV:CAC | Misleading | Cohort size is too small to produce statistically reliable retention data. The S$95 CAC is real and verifiable; the retention signal from influencer cohorts is not. Any LTV:CAC ratio computed for this channel should not be presented as evidence of channel performance. |
Biggest distortion: The 4% monthly churn assumption. It is the lever that makes the model look like a 6.7:1 LTV:CAC business when observed data produces 1.8:1. Every other metric distortion in the deck is a presentation choice — this one changes the fundamental conclusion about whether the business is ready to accelerate spend.
The observed unit economics do not support doubling Meta spend ahead of the seed extension. Meta's observed LTV:CAC of approximately 1.3:1 is well below the 3:1 growth-readiness threshold, CAC on the channel has risen 39% in six months, and Meta cohorts churn materially faster than referral cohorts. Blended LTV:CAC on observed data is 1.8:1 — below the benchmark. The business has a genuinely strong unit economics story in referral (7.6:1 observed LTV:CAC, S$18 CAC, 1.2-month payback) that is structurally underinvested. The correct move before the raise is to demonstrate referral programme investment, hold Meta at current spend, cut influencer, and use the next 60–90 days to produce cohort data that shows whether referral can absorb meaningful volume growth. That is a stronger story for investors than doubling spend on the channel with the weakest observed economics.
What this is. This Unit Economics Review was built through a guided conversation between Demo Account and Ren.
How it was built. All analysis reflects your own thinking — structured using established frameworks, sharpened, and presented clearly.
This report was produced by Ren, an AI advisor built by Renatus. It is based on information you provided during the conversation and established frameworks. It is intended to support — not replace — your own judgement. All conclusions should be reviewed before acting on them.
Renatus applies the underlying principles of established methods and credits their origin where relevant. Named frameworks, methods, and instruments are the property of their respective owners. Reference to them does not imply endorsement or affiliation.
3 frameworks were used to structure your thinking:
