Capital in. Disburse. Repay. Distribute.

Every lending platform runs the same loop, across a pile of accounts nobody sees at once. Investor capital in, loans out, repayments back, investors paid. Reconciled by hand, usually on a Monday.

TreasuryPath runs the whole loop through one account: one ledger across every bank you already use, idle capital working between draws, every move approval-gated.

Around 30 days to live, not the six to twelve months a legacy treasury system takes.

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Hey {{first_name}} 👋, I hope you’re having a great week! In the last issue, we discussed about LTV to CAC, and now we are moving with the next topic from Reporting content.

Let’s talk about ⬇️

A company came to me convinced they had a product-market fit problem.

Blended monthly churn: 3.2%. Bad enough that leadership had started debating whether the product itself was wrong.

So we built the cohort decay curve. And months 1 through 3? Retention was sitting at around 94%. Strong. Nothing wrong there at all.

The drop was at month 6. Sharp, and in exactly the same place every single time.

Turned out month 6 was when structured onboarding support ended and customers got moved to self-serve. The people who churned there had lower product adoption scores back at month 3 — the signal was in the data months before anyone left.

Fix was straightforward once we could see it. Extend structured onboarding through month 8 for low-adoption accounts, add a proactive check-in at month 5.

Blended churn went from 3.2% to 2.1% over the next two quarters. Zero product changes.

That's the whole case for cohort analysis in one story. A blended churn rate tells you something is wrong. Cohort tables tell you where.

Here's how I build them.

1️⃣ The shape is the signal

2️⃣ You need three tables, not one

3️⃣ Where's the drop?

4️⃣ NRR benchmarks — where you should be

5️⃣ CAC payback benchmarks

Group customers by when they started paying. Track what each group does month by month. That's it — that's cohort analysis.

What makes it useful is that different problems produce different shapes. A 4% blended churn rate can't tell you whether you have an onboarding problem, an ICP problem, or a pricing problem. The curve can.

Steep drop in months 0 to 2, then it flattens out? That's activation failure. Your onboarding isn't getting people to value fast enough.

Steady linear decline across 12 months? Value decay. The product works, just not enough to make switching costs stick. Usually correlates with a specific segment or channel.

Holds steady then falls off a cliff at month 12? Annual renewal friction. Price increases, procurement, or the customer's business changed.

Steep early drop, then strong flattening? That's actually healthy. Focus on the early drop.

Curve rises above 100%? Negative churn. Expansion is outrunning gross churn. Congratulations.

A number without a shape is just a data point.

Most implementations build a retention heatmap and stop. That's incomplete.

Table 1 — Customer retention. Who stays?

Rows are monthly cohorts. Columns are months since first payment. Cells show what percentage of that cohort is still active.

Use first-payment date as the anchor, not signup date. Signup cohorts pull in trial behavior and delay the signal you actually care about.

You need four fields: customer ID, first payment date, monthly status, monthly revenue. A Stripe export plus a pivot table gets you there. Done means two people asking the same question get the same number.

Table 2 — Revenue retention. Do they grow?

Logo retention says whether they stay. Revenue retention says whether they're worth keeping.

Same cohort definitions, but sum MRR from those customers over time and divide by month 0.

Look at February. Might have perfectly acceptable logo retention — but revenue is contracting. Downgrades and discount roll-off are eating any expansion.

You'd never see that in a blended NRR number.

Table 3 — CAC payback. When does the money come back?

Assign CAC to each cohort month. Calculate monthly gross profit (MRR × gross margin). Sum it cumulatively. Payback month is the first month cumulative gross profit passes cohort CAC.

If cohort CAC is $120K and you're clearing $33K–36K of gross profit a month, payback lands around month 3. If it's stretching past 18 months, the economics don't work at that spend level — and you know that before you commit to the next hiring cycle.

Once the table's built, plot the curves and ask one question: where does the drop happen?

Months 0–2 → onboarding. Customers arrived with an expectation and the product didn't deliver fast enough. The fixes live in implementation speed, feature discoverability, and how sales sets expectations.

Gradual across 6–12 months → the product delivers some value, just not enough. This is a PMF problem at the segment level, not usability. Check whether it tracks to a specific ICP or channel.

Cliff at month 12 → annual renewals failing. Three usual suspects: price increase at renewal, procurement friction, or the customer's business shifted. See if it correlates with plan type or account size.

One rule if you only slice the table one way: slice by segment or plan type. A blended table is the worst possible version, because it averages across groups that behave nothing alike and gives you a retention number describing no actual customer.

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Overall median sits at 101%. Barely above flat. Most SaaS companies are just covering gross churn with expansion and not actually growing the base.

ChartMogul looked at 2,500+ SaaS businesses and found companies at 100%+ NRR grew 48% YoY — more than double the sub-100% group. At $10M–$50M ARR that gap compounds fast, since expansion is up to 40% of total growth at that stage.

Below 100% NRR, do the math on what it actually costs you. At $10M ARR with 95% NRR, you're down $500K a year from the existing base before signing a single new customer. That's not a retention footnote — it changes your CAC budget, your runway model, and your hiring plan.

Above 115% at mid-market? Different question entirely: what's driving it? Because that's what belongs at the top of your next product cycle.

The 12-month rule everyone quotes is a fine starting point and a terrible universal standard. Kyle Poyar's framing is the right one — your payback target is inseparable from your NDR. Under 100% NDR, target sub-12 months. Between 100–120%, twelve to eighteen is defensible. Above 150%, longer payback can be justified because expansion compounds.

A $75K ACV enterprise deal with a nine-month sales cycle is never hitting 12-month payback without either mispricing or gutting sales costs.

What matters more than the benchmark: is payback improving or worsening across vintages? If it's extending quarter over quarter, it's one of three things — CAC rising, early churn increasing, or margin compression. The cohort tables isolate which.

Your churn rate tells you something went wrong. Cohort tables tell you when, in which segment, and at what point in the lifecycle.

Revenue cohorts tell you whether the customers who stay are growing or shrinking. Payback cohorts tell you whether the acquisition math holds at your current spend.

Three tables. That's enough to make specific, defensible calls on product investment, GTM budget, and runway — without a finance team.

Most companies at $5M–$50M ARR already have the data sitting in Stripe. What's missing is a structure for turning it into a monthly decision cadence.

That's all cohort analysis is.

Reply with "COHORT" and I'll send you the cohort analysis template — four tabs, the COUNTIFS formulas already built, retention and revenue tables, CAC payback calc, and the five questions to run through it every month.

Chat soon,

I'm opening a small group for SaaS finance operators. Every month: one finished financial model and one live session building it — ARR bridges, burn multiple, usage-based pricing, board packs.

Not a course. Just the models I build for clients, plus a room where you can ask why line 47 is doing that.

$490 for the first year, capped at 30 people. $990 after. Closes Aug 30th.

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