---
title: "The Trial Cohort to Paid Customer report | Cometly Academy"
description: "Cohort reporting is what makes PLG attribution honest. The trials you ran in March turn into MRR in April, May, and June. This report groups every paying customer by the month, source, and campaign their trial started in, so you can compare 30, 60, 90-day, and 1-year ROAS side-by-side."
source: "https://www.cometly.com/academy/trial-cohort-to-paid-customer-report"
---

# The Trial Cohort to Paid Customer report

Group every paying customer back to the cohort of trials they started in.

- Module 03: Product-Led Growth Reports
- Lesson: 3.2
- Track: PLG
- Format: Report
- Read time: 10 min

Cohort reporting is what makes PLG attribution honest. The trials you ran in March turn into MRR in April, May, and June. This report groups every paying customer by the month, source, and campaign their trial started in, so you can compare 30, 60, 90-day, and 1-year ROAS side-by-side.

## Key takeaways

- Group rows by trial-start month and source
- Columns: Spend, Trials, New Customers, 30/60/90-day MRR, Year-1 LTV ROAS
- Filter to paid sources only when calculating channel-level ROAS
- Cohorts mature as time passes — the most recent cohort always looks worst
- Use this report to justify holding spend through the conversion lag

The cohort report is the single most important report for PLG growth teams. It’s the one that proves to your CFO that the trials you generated last month are turning into customers this month, even when the in-platform ROAS numbers look terrible.

## Why it matters

PLG conversion is laggy. Trials started in week 1 don’t become customers until week 2–4, and they don’t pay back the CAC until month 6–12. A cohort report is the only honest way to look at that economics — and the only way to keep your team from cutting paid budgets that haven’t had time to mature.

## Building the report

Use the Cometly Report Builder to create a Cohort report. Group rows by trial-start month and source. Add columns for Spend, Trials Started, New Customers, MRR at 30/60/90 days, and LTV ROAS.

Set the attribution model to Source-Specific (the model that gives a channel credit any time it appears in the journey) and the window to Lifetime. This is the combination that produces the most honest channel-level cohort numbers for PLG.

- Rows: trial-start month × source
- Columns: Spend, Trials, New Customers, MRR @30/60/90, LTV ROAS
- Model: Source-Specific
- Window: Lifetime
- Filter to paid sources for clean ROAS comparisons

## Reading cohorts

Cohorts mature over time. The trials you started in May won’t have a meaningful 90-day MRR until August. Always read the report top-down (oldest cohort first) and remember that the most recent cohort always looks worst because it hasn’t had time to convert.

Channel-level cohort patterns are stable. If LinkedIn’s 90-day cohort ROAS has been 4.2x for six months, you can budget against that. If Meta’s 90-day cohort ROAS has been bouncing between 0.8x and 3.5x for six months, that’s a creative or audience problem worth investigating.

## Common pitfalls

### Comparing cohorts that haven’t matured

Last month’s cohort is always lower than two months ago’s. Compare cohorts at the same age, not the same calendar date.

### Using last-touch attribution on cohorts

PLG journeys are multi-touch. Last-touch over-credits direct and branded search and hides the channels that actually drove the trial.

### Reporting cohort ROAS at 30 days

Most PLG cohorts have negative ROAS at 30 days because the LTV hasn’t happened yet. Use 90-day or longer for serious decisions.