Cohort retention table: how to read and average it

Maya

The board asks for our month-1 retention. Theo averaged the month-1 column and got 43%. Can I send that?

Alex Tutor

Let's look at the table first. A cohort table has one row per sign-up cohort, here each month from January to June, and one column per month since sign-up.

Each cell is how many of that cohort opened the app at least once in month k after signing up: monthly bracket retention. The data runs to the end of June, so newer cohorts have fewer columns.

Alex Tutor

Dataset · 6 rows

Halves monthly cohorts, January–June 2026

Counts of users who opened the app in month k after their sign-up month. Empty: not observable yet.

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Columns
cohort: Sign-up month
users: Users who signed up that month (staff excluded)
m1: Active in the 1st month after the sign-up month
m2: Active in the 2nd month
m3: Active in the 3rd month
m4: Active in the 4th month
m5: Active in the 5th month

Data

cohort	users	m1	m2	m3	m4	m5
2026-01	800	353	281	256	244	229
2026-02	700	324	259	238	222
2026-03	2400	620	397	329
2026-04	900	448	356
2026-05	1000	502

Show 1 more row

2026-06

1100

Read it three ways. A row is one cohort's life. A column compares cohorts at the same age: is onboarding getting better? A diagonal is one calendar month: a release or a holiday hits every cohort there at once.

You

So a cell is a count; to get retention I divide by the cohort's users.

Alex Tutor

Question 1

What is the month-2 retention of the February cohort? In %, one decimal.

Your answer

Units: % (e.g. 45.5)

Theo

Here's how I got 43%: month-1 rates for January to May are 44.1, 46.3, 25.8, 49.8 and 50.2. Their average is 43.2%.

Maya

Isn't an average of the cohorts exactly what the board asked for?

Alex Tutor

That treats March's 2,400 users and January's 800 as equals. To answer “what share of everyone who signed up came back in month 1?”, weight by cohort size: add up the active users, add up the cohort sizes, divide.

Alex Tutor

Question 2

Compute month-1 retention for January–May weighted by cohort size (sum of m1 ÷ sum of users for the cohorts that have m1). In %, one decimal.

Your answer

Units: % (e.g. 45.5)

38.7% weighted against 43.2% simple. The gap is March: the biggest cohort by far, with the weakest month 1, 25.8%.

Theo

March is when a bank paid its student-card holders $5 to sign up to Halves. Sign-ups tripled.

Alex Tutor

That explains it. Incentivized sign-ups come for the reward; many never had a bill to split. Without March, the weighted month 1 is 47.9%. Both numbers are true; the board should see both, with March named.

Alex Tutor

Question 3

What should Maya send the board as month-1 retention?

Choose one answer43.2%, the average of the five cohorts, since every cohort counts equally47.9% only, since March was a one-off38.7% for all users, plus 47.9% without the March promo cohort, with the reason stated50.2%, the latest cohort, since it reflects the current product

You

For the average retention curve I weighted every column the same way: 38.7, 26.9, 21.1, then 31.1 and 28.6. Retention goes up in month 4?

Alex Tutor

Nobody came back. The cohorts changed. Month 3 includes March; month 4 has only January and February, which were stronger all along. Each column averages a different set of cohorts.

Fix: compare months over the same cohorts. For the drop from month 1 to month 2, use only January to April, the cohorts that have both columns.

Alex Tutor

Question 4

Month-1 retention, weighted, for January–April only (the cohorts that also have m2). In %, one decimal.

Your answer

Units: % (e.g. 45.5)

36.4% in month 1 to 26.9% in month 2 for the same four cohorts. That's a real step down; 38.7% to 26.9% mixed May into one side only.

You

So a curve for the report should only use cohorts old enough to have every column in it.

Alex Tutor

Yes. Or draw each cohort's row as its own line and compare the shapes. Averages come after you've looked at the rows.

Alex Tutor

Question 5

True or false: the weighted column averages go from 21.1% in month 3 to 31.1% in month 4, which shows that some users came back in their fourth month.

TrueFalse

Maya

Last question. We reworked onboarding in April. Does the month-1 column say it helped?

Alex Tutor

Read the column, not the average: 44.1, 46.3, then March, then 49.8 and 50.2. Keep in mind that a cohort of 800 has about ±3.5 points of noise on its own, and that nothing else was held constant.

You

So a careful “probably a bit better”, not a victory lap.

Alex Tutor

Question 6Short answer · AI-checked task

Onboarding was reworked in April. In 3–4 sentences, tell Maya what the month-1 column says about it, with numbers and with the limits of this reading.

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Alex Tutor

So Maya's board gets 38.7%, 47.9% without the promo wave, and a sentence explaining the difference.

Alex Tutor

That’s the lesson. You answered every task — nicely done.

Next lesson: The plateau: retention and product-market fit