How to forecast MAU from cohorts

Maya

The board wants a user forecast for the rest of 2026. MAU jumped from 25,704 in May to 34,365 in June. Is that our new level?

Alex Tutor

That depends on who joined in June. Last year's cohorts can tell us: here is how summer cohorts stayed compared with the rest.

Alex Tutor

Dataset · 12 rows

Monthly retention curves from the 2025 cohorts

Share of a cohort that opens the app in each month of its life.

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Columns
month_since_signup: Age of a cohort in months; 0 is the sign-up month
active_share_regular: Share of a cohort active at that age: cohorts from September to May
active_share_summer: The same for June–August cohorts (many trip groups)

Data

month_since_signup	active_share_regular	active_share_summer
0	1.0000	1.0000
1	0.5003	0.5002
2	0.4158	0.3829
3	0.3610	0.3067
4	0.3253	0.2573

Show 7 more rows

5	0.3024	0.2250
6	0.2874	0.2041
7	0.2775	0.1905
8	0.2711	0.1818
9	0.2670	0.1760
10	0.2643	0.1723
11	0.2627	0.1699

You

At month 1 both curves are at 50%. By month 3 summer is at 31% against 36%, and by month 11 at 17% against 26%.

Alex Tutor

Trip groups settle the bill and leave. Two-thirds of June's 12,904 new users came through invites, many into trip groups, so part of June's MAU is on loan.

Alex Tutor

Question 1

How many users of the June 2026 cohort do you expect to be active in September 2026? Use the right curve and round to whole users.

Your answer

Units: users

Alex Tutor

So build the forecast the way users arrive and leave: each month's new users times the share of a cohort still active at its age, plus everyone who joined in 2025 as one line.

Alex Tutor

\text{MAU}_t = \text{active from 2025 cohorts}_t + \sum_{c \le t} N_c \times r_{\,t-c}

N: new users in cohort month c; r: share of a cohort active at age t − c.

Alex Tutor

Dataset · 12 rows

Halves new users and MAU, 2026: actual and plan

January–June are actual; July–December are the growth plan and the "paid halved from September" scenario.

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Columns
month: Calendar month, 2026
status: actual (January–June) or plan (July–December)
new_invite: New users from invites
new_organic: New users from store search, not paid
new_paid: New users from paid social and store search ads
new_users: All new users (the cohort size)
new_users_paid_halved: New users if paid acquisition is halved from September (and, from October, minus the invites the missing paid users would have brought)
active_from_2025_cohorts: Active users who signed up in 2025 (projected after June)
mau_actual: Actual monthly active users (empty for plan months)

Data

month	status	new_invite	new_organic	new_paid	new_users	new_users_paid_halved	active_from_2025_cohorts	mau_actual
2026-01	actual	1966	998	1682	4646	4646	13998	18644
2026-02	actual	2211	1106	1789	5106	5106	12875	20304
2026-03	actual	2300	1176	1919	5395	5395	12146	22025
2026-04	actual	2267	1241	1996	5504	5504	11676	23680
2026-05	actual	2555	1263	2169	5987	5987	11363	25704

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2026-06	actual	8453	1325	3126	12904	12904	11164	34365
2026-07	plan	9359	1442	3303	14104	14104	11033
2026-08	plan	9873	1521	3485	14879	14879	10951
2026-09	plan	3064	1605	2626	7295	5982	10894
2026-10	plan	3232	1693	2770	7695	5903	10858
2026-11	plan	3410	1786	2923	8119	6229	10836
2026-12	plan	3597	1884	3084	8565	6570	10822

You

In September, January will be 8 months old, June 3, and September itself 0.

Alex Tutor

Right. Month 0 is 1.0: everyone who signs up opens the app that month. Summer curve for June, July and August, the regular one for the rest.

Alex Tutor

Question 2

Forecast Halves MAU for September 2026 under the plan: the 2025 line plus every 2026 cohort from January to September at its age, with the summer curve for June–August.

Your answer

Units: users

Alex Tutor

Halves MAU forecast, 2026: two curves vs oneForecast from cohorts with the summer curve for June–August (plan), and the same plan with the regular curve for every cohort.

Chart data

month	Plan, two curves	One curve for all
2026-01	18,644	18,644
2026-02	20,305	20,305
2026-03	22,027	22,027
2026-04	23,679	23,679
2026-05	25,702	25,702

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2026-06	34,365	34,365
2026-07	40,702	40,704
2026-08	46,166	46,592
2026-09	42,829	43,995
2026-10	43,060	45,193
2026-11	44,236	47,001
2026-12	46,081	49,260

Maya

Wait. 46,166 in August and 42,829 in September? We lose users while still adding 7,295 new ones?

Alex Tutor

The summer cohorts are huge and fall off fast. In September, July's 14,104 users are down to 38% and August's 14,879 to 50%.

Theo

And if we'd used one curve for everyone?

Alex Tutor

Then September would read 43,995, 1,166 too high, and December 3,179 too high. The error grows as the summer cohorts age.

Alex Tutor

Question 3

Which of these choices would make the September forecast too high? Select all that apply.

Choose all that applyUsing the regular curve for the June–August cohortsExtending a straight trend line through January–June MAUCounting September's own new users at 100%Leaving out the users who signed up in 2025

Theo

Finance has seen the M7 numbers: paid-social CAC up to $1.18 in June, and winter paid cohorts needing four or five months to pay back. They want to halve paid acquisition from September.

Alex Tutor

The forecast can price that in users; the cohort ROI says whether those users would have paid back. See new_users_paid_halved: half the paid users from September, and from October also the people the missing users would have invited, 0.31 each.

You

September's cohort is 1,313 smaller. October's is 1,792 smaller: 1,385 fewer paid users plus 407 lost invites.

Alex Tutor

Now find what the cut costs in December. Only the cohorts from September on differ, so take each one's missing users at its December age.

Alex Tutor

Question 4

By how many users is December 2026 MAU lower in the paid halved scenario than in the plan? Use new_users minus new_users_paid_halved for the September–December cohorts and the regular curve at each cohort's December age.

Your answer

Units: users

Alex Tutor

Halves MAU forecast, 2026: plan vs paid halvedForecast from cohorts: the plan and the scenario with paid acquisition halved from September.

Chart data

month	Plan	Paid halved
2026-01	18,644	18,644
2026-02	20,305	20,305
2026-03	22,027	22,027
2026-04	23,679	23,679
2026-05	25,702	25,702

Show 7 more rows

2026-06	34,365	34,365
2026-07	40,702	40,702
2026-08	46,166	46,166
2026-09	42,829	41,516
2026-10	43,060	40,611
2026-11	44,236	40,903
2026-12	46,081	41,922

September loses 1,313 users, December 4,159, 9% of the plan. The gap keeps widening after the cut: every smaller cohort stays smaller, and the lost invites add up.

Maya

So is halving the wrong call?

Alex Tutor

Not necessarily. It saves half the paid budget. Whether that's worth 4,159 users in December depends on what those users would earn against their CAC, and that's unit economics, not the forecast.

Alex Tutor

Question 5

If Halves halves paid acquisition from September, the MAU gap against the plan is largest in September and shrinks after that.

TrueFalse

Alex Tutor

A forecast is a set of assumptions with a number at the end. Write the assumptions down, so the board argues with the inputs, not with the result.

Maya

Short, please. December, both scenarios, and what could make it wrong.

Theo

And keep the halved scenario next to the plan. I'll need both for the budget talk in October.

Alex Tutor

Question 6Short answer · AI-checked task

Write Maya 3–5 sentences for the board: the December 2026 MAU forecast under the plan and with paid acquisition halved from September, how it was built, and the two assumptions most likely to make it wrong.

Write your answer and get a score with feedback from our AI reviewer.

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The rule: forecast users the way they arrive and leave, cohort by cohort, with the right curve for each kind of cohort. A trend line only extends the past.

Alex Tutor

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

Next lesson: Invites and virality