DAU/MAU vs WAU/MAU: measuring stickiness the right way

Theo

An investor sent us a benchmark: good consumer apps have DAU/MAU around 50%. Ours for June is 17%. Are we a failing app?

Alex Tutor

Let's unpack it. DAU, WAU, MAU are distinct users who did the qualifying action in a day, a week and a month. For Halves that action is opening the app.

Stickiness is DAU divided by MAU: the share of the month's users who show up on a typical day. It only makes sense at the product's natural usage rhythm. You need a messenger every day. Nobody splits a dinner bill every day.

You

So what is our rhythm?

Alex Tutor

Let the data say. Here's June, 1 to 28: four whole weeks, which we'll treat as the month. One row per user with the days they opened the app in each week, plus DAU for each day.

Alex Tutor

Dataset · 140 rows

Halves users and their active days, 1–28 June 2026

Everyone with any event in June, 140 rows. Staff accounts already removed.

Download CSV

Columns
user_id: User id
group_type: Type of the user's main group: couple, flat, trip or other
days_w1: Days with `app_opened` in week 1 (1–7 June)
days_w2: Same for week 2 (8–14 June)
days_w3: Same for week 3 (15–21 June)
days_w4: Same for week 4 (22–28 June)
server_only: true if the user's only June events were recurring bills created by the server

Data

user_id	group_type	days_w1	days_w2	days_w3	days_w4	server_only
m2u001	trip	0	4	0	0	false
m2u002	flat	0	2	1	2	false
m2u003	flat	2	2	1	1	false
m2u004	trip	0	5	0	0	false
m2u005	other	0	1	0	0	false

Show 135 more rows

m2u006	flat	0	0	0	0	true
m2u007	flat	3	1	1	0	false
m2u008	flat	1	2	0	1	false
m2u009	other	0	0	0	1	false
m2u010	other	0	0	0	1	false
m2u011	trip	0	0	3	1	false
m2u012	trip	5	0	0	0	false
m2u013	couple	3	2	3	1	false
m2u014	trip	0	0	0	3	false
m2u015	flat	2	1	2	0	false
m2u016	couple	2	1	2	2	false
m2u017	flat	0	2	0	1	false
m2u018	flat	1	2	2	0	false
m2u019	flat	2	0	2	1	false
m2u020	flat	1	1	2	2	false
m2u021	flat	0	0	0	0	true
m2u022	couple	1	0	3	2	false
m2u023	flat	0	0	0	0	true
m2u024	flat	1	2	1	1	false
m2u025	couple	2	2	2	0	false
m2u026	trip	0	0	4	1	false
m2u027	flat	1	1	0	0	false
m2u028	other	0	0	0	1	false
m2u029	flat	2	0	0	1	false
m2u030	couple	3	1	1	3	false
m2u031	other	0	0	0	1	false
m2u032	couple	2	1	3	0	false
m2u033	couple	1	0	0	0	false
m2u034	couple	1	3	0	1	false
m2u035	couple	1	3	1	3	false
m2u036	other	0	0	0	1	false
m2u037	flat	1	2	1	0	false
m2u038	other	1	0	0	0	false
m2u039	couple	1	1	0	1	false
m2u040	couple	1	0	1	3	false
m2u041	trip	5	0	0	0	false
m2u042	other	0	0	1	0	false
m2u043	flat	0	1	0	0	false
m2u044	flat	0	0	0	0	true
m2u045	trip	0	3	0	0	false
m2u046	couple	3	2	1	0	false
m2u047	flat	3	0	2	1	false
m2u048	flat	0	2	2	1	false
m2u049	flat	2	0	2	2	false
m2u050	flat	2	1	2	0	false
m2u051	couple	3	1	2	3	false
m2u052	flat	1	0	2	0	false
m2u053	flat	0	0	0	0	true
m2u054	flat	2	2	0	2	false
m2u055	trip	0	3	0	0	false
m2u056	flat	2	2	0	0	false
m2u057	flat	0	2	1	0	false
m2u058	trip	0	0	0	3	false
m2u059	flat	0	0	0	0	true
m2u060	trip	0	0	4	0	false
m2u061	trip	4	1	0	0	false
m2u062	flat	1	1	1	1	false
m2u063	flat	0	0	0	0	true
m2u064	trip	3	0	0	0	false
m2u065	couple	3	2	1	1	false
m2u066	couple	3	2	3	1	false
m2u067	flat	2	2	0	1	false
m2u068	trip	0	3	0	0	false
m2u069	couple	2	3	0	2	false
m2u070	trip	5	1	0	0	false
m2u071	other	1	0	0	0	false
m2u072	couple	3	1	3	1	false
m2u073	couple	1	1	2	3	false
m2u074	flat	0	0	0	0	true
m2u075	couple	2	2	2	2	false
m2u076	trip	0	0	0	5	false
m2u077	trip	0	0	5	0	false
m2u078	flat	1	2	0	0	false
m2u079	trip	0	0	0	5	false
m2u080	flat	2	0	2	2	false
m2u081	trip	0	0	3	0	false
m2u082	trip	0	4	0	0	false
m2u083	flat	0	0	0	0	true
m2u084	couple	0	2	3	3	false
m2u085	trip	0	3	0	0	false
m2u086	flat	2	1	0	2	false
m2u087	couple	3	1	1	3	false
m2u088	trip	0	4	0	0	false
m2u089	couple	1	1	3	3	false
m2u090	flat	2	1	1	1	false
m2u091	couple	0	2	2	1	false
m2u092	trip	0	4	0	0	false
m2u093	couple	3	1	2	1	false
m2u094	trip	0	0	0	5	false
m2u095	trip	0	0	0	4	false
m2u096	flat	0	0	0	0	true
m2u097	flat	0	0	0	0	true
m2u098	other	0	0	1	0	false
m2u099	trip	5	0	0	0	false
m2u100	couple	1	2	1	2	false
m2u101	flat	3	1	0	2	false
m2u102	couple	2	0	1	3	false
m2u103	couple	3	3	3	0	false
m2u104	flat	1	1	0	1	false
m2u105	flat	0	2	2	1	false
m2u106	flat	2	2	2	2	false
m2u107	flat	1	1	1	1	false
m2u108	flat	1	0	1	0	false
m2u109	couple	1	1	3	0	false
m2u110	flat	2	2	2	0	false
m2u111	trip	0	4	1	0	false
m2u112	trip	4	0	0	0	false
m2u113	flat	1	1	0	0	false
m2u114	couple	0	3	2	1	false
m2u115	couple	2	1	1	2	false
m2u116	flat	2	1	1	0	false
m2u117	flat	2	2	0	2	false
m2u118	trip	0	0	4	1	false
m2u119	couple	3	0	3	3	false
m2u120	trip	4	0	0	0	false
m2u121	flat	0	2	2	0	false
m2u122	couple	2	3	2	3	false
m2u123	flat	1	0	2	1	false
m2u124	couple	2	3	3	2	false
m2u125	flat	2	0	0	2	false
m2u126	flat	2	2	0	1	false
m2u127	flat	2	1	2	1	false
m2u128	flat	0	0	0	0	true
m2u129	couple	2	2	1	2	false
m2u130	other	0	0	0	1	false
m2u131	other	0	0	1	0	false
m2u132	couple	2	1	3	1	false
m2u133	couple	2	1	2	2	false
m2u134	other	0	0	0	1	false
m2u135	flat	1	2	2	2	false
m2u136	other	0	0	0	1	false
m2u137	flat	2	2	1	0	false
m2u138	flat	2	1	1	1	false
m2u139	flat	1	2	0	2	false
m2u140	flat	2	2	1	1	false

Alex Tutor

Dataset · 28 rows

Halves daily active users, 1–28 June 2026

Distinct users with `app_opened` each day (UTC), same users as above.

Download CSV

Columns
date: Day (UTC)
weekday: Day of the week
week: Week number, 1–4
dau: Daily active users

Data

date	weekday	week	dau
2026-06-01	Mon	1	40
2026-06-02	Tue	1	24
2026-06-03	Wed	1	24
2026-06-04	Thu	1	18
2026-06-05	Fri	1	25

Show 23 more rows

2026-06-06	Sat	1	25
2026-06-07	Sun	1	17
2026-06-08	Mon	2	25
2026-06-09	Tue	2	18
2026-06-10	Wed	2	21
2026-06-11	Thu	2	23
2026-06-12	Fri	2	24
2026-06-13	Sat	2	23
2026-06-14	Sun	2	23
2026-06-15	Mon	3	22
2026-06-16	Tue	3	23
2026-06-17	Wed	3	20
2026-06-18	Thu	3	19
2026-06-19	Fri	3	16
2026-06-20	Sat	3	21
2026-06-21	Sun	3	20
2026-06-22	Mon	4	20
2026-06-23	Tue	4	23
2026-06-24	Wed	4	20
2026-06-25	Thu	4	17
2026-06-26	Fri	4	20
2026-06-27	Sat	4	27
2026-06-28	Sun	4	13

You

MAU first, then. That's just the number of rows?

Alex Tutor

Look at the last column before you answer.

Alex Tutor

Question 1

Using the user file, what is Halves MAU for 1–28 June: distinct users who opened the app at least once?

Your answer

Units: users

Alex Tutor

DAU moves around: 40 on Monday 1 June, when flats log the rent, and 13 on Sunday the 28th. So stickiness uses the average DAU over the month.

Alex Tutor

Halves DAU, 1–28 June 2026Rent day on Monday 1 June stands out; the rest of the month is flat.

Chart data

Day of June	Daily active users
1	40
2	24
3	24
4	18
5	25

Show 23 more rows

6	25
7	17
8	25
9	18
10	21
11	23
12	24
13	23
14	23
15	22
16	23
17	20
18	19
19	16
20	21
21	20
22	20
23	23
24	20
25	17
26	20
27	27
28	13

You

Average of the dau column. Or total active days in the user file divided by 28, which should be the same thing.

Alex Tutor

Exactly the same: every active user-day is one unit of some day's DAU. Two routes to one number is a free check on your arithmetic.

Theo

I copied that 17% straight from the dashboard. Let's see if it survives a recount.

Alex Tutor

Question 2

Compute June's DAU/MAU stickiness: average DAU over the 28 days divided by MAU. Give it in %, one decimal.

Your answer

Units: % (e.g. 12.3)

Alex Tutor

Now the weekly view. WAU for a week is the users with at least one day in that week's column. Average the four weeks and divide by MAU.

Theo

And that's the ratio I send back to the investor?

Alex Tutor

For our rhythm, yes, with its name on it. A daily benchmark from messengers and games doesn't compare with a weekly product.

Alex Tutor

Question 3

Now the weekly rhythm. Count WAU for each of the four weeks (users with at least one day in that week), average them and divide by MAU. What is WAU/MAU, in %, one decimal?

Your answer

Units: % (e.g. 45.6)

62% of the month's users are there in a typical week. And 71 of the 128, 55.5%, show up in at least three of the four weeks. That's a habit, just not a daily one.

Alex Tutor

Split WAU/MAU by group type and the rhythms show: couples 87.5%, flats 74%, trips 30%.

Maya

Trips at 30%? That sounds like a problem.

Alex Tutor

It's the design. A trip is one intense week, then maybe a settle-up day. A trip user active one week in four is doing exactly what the product is for. Judge trips per trip, not per week.

You

So one ratio for everyone mixes three rhythms. That's the mix problem again.

Alex Tutor

Question 4

Match each product to the activity measure that fits its natural usage rhythm.

Pick an answer for each row. Choosing an answer that is already used moves it to this row.

A team chat app
Choose an answer
Halves for couples and flatmates
Choose an answer
Halves for trip groups
Choose an answer
An app for filing yearly tax returns
Choose an answer

Alex Tutor

One more trap sits in the definition. Suppose a dashboard takes MAU from any event but WAU from app opens. The 12 server-only users join MAU, although they never opened the app in June.

Theo

A bigger MAU would look better in the deck, though.

Alex Tutor

It would, until someone divides by it. Work out the WAU/MAU that dashboard would show.

Alex Tutor

Question 5

A dashboard counts MAU from any event, so the 12 server-only users are included, but counts WAU from app_opened. What WAU/MAU does it show for June?

Choose one answer62.1%: the extra users don't change anythingAbout 67.9%: more active users means a higher ratioIt can't be computed from these filesAbout 56.8%: the denominator grows and the numerator doesn't

Maya

So what do we send back? I don't want to sound like we're hiding a bad number.

Alex Tutor

Don't hide it. Show DAU/MAU, say why it's the wrong rhythm for bill splitting, and give the weekly ratio with its definition next to it.

You

So the reply has three parts: the 17% itself, why it's the wrong ruler for us, and the weekly number with its definition.

Alex Tutor

Question 6Short answer · AI-checked task

Write the 3–5 sentence reply to the investor who asked why Halves's DAU/MAU is only 17% against a 50% benchmark. Use June's numbers.

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

Log in to get AI feedback

Alex Tutor

17% was never a failing grade. It was a daily ruler held against a weekly product.

Alex Tutor

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

Next lesson: Case: a north star for Halves

6	25
7	17
8	25
9	18
10	21
11	23
12	24
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14	23
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16	23
17	20
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19	16
20	21
21	20
22	20
23	23
24	20
25	17
26	20
27	27
28	13

6	25
7	17
8	25
9	18
10	21
11	23
12	24
13	23
14	23
15	22
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17	20
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19	16
20	21
21	20
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23	23
24	20
25	17
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27	27
28	13

6	25
7	17
8	25
9	18
10	21
11	23
12	24
13	23
14	23
15	22
16	23
17	20
18	19
19	16
20	21
21	20
22	20
23	23
24	20
25	17
26	20
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28	13