A metric is its definition
Write a metric so that two people get the same number: events, exclusions, window and time zone.
Lesson 3 of 32~25 min of learningIncludes ~15 min for questions and tasks
Contents1 of 40 steps
1 of 40 steps
Maya
Quick one for Thursday's update: how many active users did Halves have on Wednesday, 3 June? My dashboard says 6.
Theo
Mine says 7. Same app, same Wednesday. I'd like to know which one goes to the board.
🦉
You
One of the queries must have a bug. Should I find out whose?
Alex Tutor
Probably neither. They answered two different questions that share a name. “Active users on Wednesday” sounds exact, but it's a label, not a rule.
Alex Tutor:
Maya's dashboard counts anyone with any event on the UTC date. Theo's sheet counts app opens on each user's local date. Neither removes staff accounts.
🦉
You
So before asking who's right, I ask what each of them counted.
Alex Tutor
Question 1
“Active users on Wednesday” leaves several questions open. Which of these must a written definition answer? Select all that apply.
Alex Tutor
A metric definition that nobody can misread has five parts. Each hides a trap we've met in the Halves data.
Alex Tutor:
1. Qualifying action: which event makes someone count. For “active”, take
app_opened, not any event. Recurring bills are server-side events, so three users look active without touching their phones.Alex Tutor:
2. Unit counted: distinct users, groups or settlements. Both sides log a settle-up, so settlements are distinct
settlement_id values, not rows.Alex Tutor:
3. Exclusions: staff, QA and test accounts. 4. Time window: a day, seven days or a month, with its exact start and end. 5. Reporting time zone: whose clock decides the date, UTC or the user's.
🦉
You
About staff: one QA account had an empty
is_internal, didn't it?Alex Tutor
Yes, u002. So the rule says how to find staff:
is_internal = true or acquisition_channel = staff. A rule that trusts a half-filled flag is still vague.Alex Tutor
Alex Tutor
Question 2
Match each part of a metric definition to its example from Halves.
Pick an answer for each row. Choosing an answer that is already used moves it to this row.
- Qualifying actionChoose an answer
- Unit countedChoose an answer
- ExclusionsChoose an answer
- Time windowChoose an answer
- Reporting time zoneChoose an answer
Maya
Fine, let's fix it. Real people, app opened, Wednesday 3 June. What's the number?
Alex Tutor
Two files. The users table has each person's time zone and staff markers. The event log runs from noon 2 June to noon 4 June UTC, which covers Wednesday in every time zone our users live in.
Alex Tutor
Dataset · 22 rows
Halves users, first week of June
All 22 accounts that have any event in the week of 1–7 June 2026.
- Columns
- user_id
- User id
- signup_date
- Sign-up date
- platform
- ios or android
- acquisition_channel
- Where the user came from; `staff` marks team accounts
- timezone
- The user's time zone
- is_internal
- Internal-account flag: true, false or empty (not always set)
Data
| user_id | signup_date | platform | acquisition_channel | timezone | is_internal |
|---|---|---|---|---|---|
| u001 | 2026-04-23 | ios | invite | Europe/Kyiv | false |
| u002 | 2026-02-10 | android | staff | Europe/Kyiv | |
| u003 | 2026-05-10 | ios | paid_social | Europe/Kyiv | false |
| u004 | 2026-05-22 | ios | paid_social | Europe/Kyiv | false |
| u005 | 2026-04-20 | android | app_store_search | Europe/Kyiv | false |
Show 17 more rows
| u006 | 2026-05-16 | ios | invite | Europe/Kyiv | false |
| u007 | 2026-05-08 | ios | referral_link | Asia/Bangkok | false |
| u008 | 2026-05-08 | ios | app_store_search | Asia/Bangkok | false |
| u009 | 2026-05-04 | ios | paid_social | Europe/Lisbon | false |
| u010 | 2026-05-29 | android | invite | America/New_York | false |
| u011 | 2026-05-28 | ios | invite | America/New_York | false |
| u012 | 2026-05-30 | android | invite | Asia/Bangkok | false |
| u013 | 2026-05-11 | ios | paid_social | Europe/Lisbon | false |
| u014 | 2026-05-30 | ios | referral_link | Europe/Kyiv | false |
| u015 | 2026-03-13 | android | invite | Europe/Kyiv | false |
| u016 | 2026-05-14 | ios | paid_social | Europe/Lisbon | false |
| u017 | 2026-06-01 | ios | paid_social | America/New_York | false |
| u018 | 2026-05-27 | ios | app_store_search | Asia/Bangkok | false |
| u019 | 2026-02-10 | ios | staff | Europe/Kyiv | true |
| u020 | 2026-05-06 | android | referral_link | Europe/Kyiv | false |
| u021 | 2026-05-12 | ios | app_store_search | Asia/Bangkok | false |
| u022 | 2026-05-11 | ios | app_store_search | Europe/Lisbon | false |
Alex Tutor
Dataset · 42 rows
Halves events, noon 2 June to noon 4 June (UTC)
42 events from the week's log. Times are UTC.
- Columns
- event_time
- When it happened, UTC
- user_id
- Who did it
- event
- Event name
- platform
- ios, android or server
- entry_method
- How an expense was entered
- amount_usd
- Amount in USD
- settlement_id
- Which settlement a settle-up belongs to
Data
| event_time | user_id | event | platform | entry_method | amount_usd | settlement_id |
|---|---|---|---|---|---|---|
| 2026-06-02T15:05:00Z | u018 | app_opened | ios | |||
| 2026-06-02T16:07:00Z | u014 | app_opened | ios | |||
| 2026-06-02T16:09:00Z | u014 | expense_added | ios | manual | 31 | |
| 2026-06-02T16:49:00Z | u004 | app_opened | ios | |||
| 2026-06-02T16:51:00Z | u004 | expense_added | ios | manual | 34 |
Show 37 more rows
| 2026-06-02T18:49:00Z | u014 | app_opened | ios | |||
| 2026-06-02T19:11:00Z | u004 | app_opened | ios | |||
| 2026-06-02T19:13:00Z | u004 | expense_added | ios | manual | 69 | |
| 2026-06-02T20:50:00Z | u020 | app_opened | android | |||
| 2026-06-02T23:10:00Z | u012 | app_opened | android | |||
| 2026-06-02T23:13:00Z | u012 | expense_added | android | manual | 19 | |
| 2026-06-02T23:14:00Z | u010 | app_opened | android | |||
| 2026-06-02T23:16:00Z | u010 | expense_added | android | receipt_scan | 8 | |
| 2026-06-03T02:30:00Z | u011 | app_opened | ios | |||
| 2026-06-03T02:33:00Z | u011 | expense_added | ios | manual | 89 | |
| 2026-06-03T07:14:00Z | u002 | app_opened | android | |||
| 2026-06-03T07:15:00Z | u002 | expense_added | android | manual | 1 | |
| 2026-06-03T07:53:00Z | u019 | app_opened | ios | |||
| 2026-06-03T07:54:00Z | u019 | expense_added | ios | manual | 1 | |
| 2026-06-03T09:19:00Z | u002 | app_opened | android | |||
| 2026-06-03T09:20:00Z | u002 | expense_added | android | manual | 1 | |
| 2026-06-03T09:37:00Z | u019 | app_opened | ios | |||
| 2026-06-03T09:38:00Z | u019 | expense_added | ios | manual | 1 | |
| 2026-06-03T10:02:00Z | u004 | app_opened | ios | |||
| 2026-06-03T10:04:00Z | u004 | expense_added | ios | manual | 105 | |
| 2026-06-03T10:07:00Z | u004 | settle_up_recorded | ios | 152 | s002 | |
| 2026-06-03T15:46:00Z | u018 | app_opened | ios | |||
| 2026-06-03T15:48:00Z | u018 | expense_added | ios | manual | 66 | |
| 2026-06-03T16:39:00Z | u021 | app_opened | ios | |||
| 2026-06-04T01:40:00Z | u017 | app_opened | ios | |||
| 2026-06-04T01:43:00Z | u017 | expense_added | ios | manual | 59 | |
| 2026-06-04T02:31:00Z | u008 | app_opened | ios | |||
| 2026-06-04T02:33:00Z | u008 | expense_added | ios | manual | 49 | |
| 2026-06-04T07:08:00Z | u019 | app_opened | ios | |||
| 2026-06-04T07:09:00Z | u019 | expense_added | ios | manual | 1 | |
| 2026-06-04T07:35:00Z | u002 | app_opened | android | |||
| 2026-06-04T07:36:00Z | u002 | expense_added | android | receipt_scan | 1 | |
| 2026-06-04T09:03:00Z | u002 | app_opened | android | |||
| 2026-06-04T09:04:00Z | u002 | expense_added | android | receipt_scan | 1 | |
| 2026-06-04T09:24:00Z | u019 | app_opened | ios | |||
| 2026-06-04T09:25:00Z | u019 | expense_added | ios | manual | 1 | |
| 2026-06-04T10:35:00Z | u020 | app_opened | android |
Alex Tutor:
Start with UTC, the date exactly as written in
event_time. Keep only app_opened, drop the two staff accounts and count distinct user_id on 2026-06-03.🦉
You
Distinct users, so someone who opened the app twice still counts once.
Alex Tutor
Question 3
Use the two files above. How many real users (both staff accounts excluded) opened the app on 2026-06-03 by UTC date?
Units: users
Theo
My ads run per country, so I read days the way people live them. A New Yorker opening the app at 10 p.m. on Tuesday is a Tuesday user to me.
Alex Tutor
A fair choice. In June, New York is UTC−4, Lisbon UTC+1, Kyiv UTC+3 and Bangkok UTC+7. Add the offset to
event_time and take the date from the result.🦉
You
So a late-evening UTC open in Bangkok is already the next morning there?
Alex Tutor
Yes, and an early-morning UTC open in New York is still the evening before. That's where this week's travelers slip from one day to another.
Alex Tutor
Question 4
Same files, same exclusions, but now the day is the user's local date (New York UTC−4, Lisbon UTC+1, Kyiv UTC+3, Bangkok UTC+7). How many real users opened the app on 3 June?
Units: users
Maya
So it's 4 or 5. Which one goes in the update?
Alex Tutor
Either, if the rule is written next to the number and never changes quietly. Most teams report in UTC so every chart splits days the same way; local days suit questions about daily habits.
Alex Tutor:
What breaks trust is 6 on one slide and 7 on the next. And both of those also counted the QA team.
Theo
Same story for the week, then. I've seen 22, 19, 18 and 17 weekly active users for 1–7 June.
Alex Tutor
Four definitions, four numbers. Only one is our DAU rule stretched to a week.
Alex Tutor
Question 5
Theo has four weekly numbers for 1–7 June (UTC): 22, 19, 18 and 17. Which definition gives 17, the agreed weekly active users?
🦉
You
Where should the definition live, so Maya doesn't have to ask us every Thursday?
Alex Tutor
Under the chart as a caption, and in the tracking plan, the document that lists what we log and why. That's the next lesson. The rule for today: no number without its written definition.
Maya
Then I want one for settling up. How many people actually settle their debts each week?
Alex Tutor
Good test. Write it with all five parts, and decide what happens when both sides log the same settle-up.
Alex Tutor
Question 6Short answer · AI-checked task
Write a complete definition of weekly settling users for Halves, in 3–6 lines. Cover the qualifying action, the unit counted, exclusions, the time window and the reporting time zone, and say what happens when both sides log the same settle-up.
Write your answer and get a score with feedback from our AI reviewer.
Log in to get AI feedbackAlex Tutor:
So, Wednesday: 4 real users opened Halves by UTC date, 5 by local date. Both are correct. Only a number without a definition is useless.
Alex Tutor
That’s the lesson. You answered every task — nicely done.