user_id stringlengths 36 36 | date stringdate 2020-10-01 00:00:00 2023-10-31 00:00:00 | registration_date stringdate 2020-04-20 00:00:00 2023-10-31 00:00:00 |
|---|---|---|
d8c465ab-e9fd-5edd-9e4e-c77094700cb5 | 2020-10-01 | 2020-08-25 |
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d32fcac5-122c-5463-8aea-01b39b9ad0bb | 2020-10-01 | 2020-09-30 |
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ba6750c9-9b60-58dc-8d40-504e7375249e | 2020-10-01 | 2020-09-29 |
48e7a195-dbdf-55d6-9d46-f550da95a9a9 | 2020-10-01 | 2020-08-25 |
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d49270af-16a1-5668-ba22-06650a8a0a70 | 2020-10-01 | 2020-08-18 |
f7aa12da-06b8-5db0-adcb-a892f81a4c90 | 2020-10-01 | 2020-06-09 |
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78a71416-202c-5111-b59b-a06264b411b6 | 2020-10-01 | 2020-07-22 |
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007282bd-f5a7-5521-aea2-8dedb16850fb | 2020-10-01 | 2020-08-25 |
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b858e4aa-0334-5ae9-b2a3-2c370930525f | 2020-10-01 | 2020-07-06 |
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309adf95-db96-5bab-aab4-b1ba4298dbf9 | 2020-10-01 | 2020-09-27 |
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60bb0a53-ee50-579b-8258-d9c9e990d01b | 2020-10-01 | 2020-05-31 |
21f00e63-94f4-5f41-8c64-34f14fd7496f | 2020-10-01 | 2020-09-28 |
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17fc6e83-c83b-5220-af4c-2aed850a07fe | 2020-10-01 | 2020-08-26 |
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a32db9f6-4815-5616-83de-ffce4e69d100 | 2020-10-01 | 2020-09-29 |
6e9cabda-fa38-5c9a-8169-985c003b94c0 | 2020-10-01 | 2020-05-31 |
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c321c897-c5ab-53a5-a664-4234c5f1e3f0 | 2020-10-01 | 2020-05-31 |
d74f5574-c2c7-5569-a45c-872390f805e9 | 2020-10-01 | 2020-09-29 |
2ccdf8b0-391e-584a-aefa-93d6843d8079 | 2020-10-01 | 2020-08-25 |
d68f5f70-b7ef-5856-a5d7-f263f05e5e79 | 2020-10-01 | 2020-08-26 |
a3e4c005-b2d3-5527-96b3-816be0db8066 | 2020-10-01 | 2020-05-31 |
2f85f17d-0f6f-5fb3-b7d0-094233e0cb70 | 2020-10-01 | 2020-08-19 |
5e80f597-9c92-5b7a-9c0c-0aeb449eed6c | 2020-10-01 | 2020-08-25 |
cc96ffbd-9aed-51c2-8c56-37d0fe24bc70 | 2020-10-01 | 2020-08-25 |
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356bb4ec-fbd3-5597-b45e-d596e15e8e56 | 2020-10-01 | 2020-06-14 |
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acda7ad7-964a-5f4b-be55-521db7ade1b3 | 2020-10-01 | 2020-07-07 |
2c47828b-1650-52c7-9fc2-311b53379bca | 2020-10-01 | 2020-06-05 |
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1eaeca2a-b962-5359-aa15-033d601c44bb | 2020-10-01 | 2020-09-28 |
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1dab9200-a119-5c82-93a0-2098abcb267e | 2020-10-01 | 2020-09-27 |
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4b2cbba5-9aac-55f2-b4a0-391a2d4a5b6b | 2020-10-01 | 2020-09-16 |
fe4eec0d-675d-58d9-9ad8-cae327f88163 | 2020-10-01 | 2020-05-31 |
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234c3035-145f-5694-bd7f-bec58a5f0c5c | 2020-10-01 | 2020-05-31 |
310c15a2-fe92-5703-be1f-80270c1b83bd | 2020-10-01 | 2020-08-27 |
41fb29c7-8122-59db-a690-e9d9502f3c38 | 2020-10-01 | 2020-09-02 |
44edc106-d349-5737-a62f-bf6c2b03316a | 2020-10-01 | 2020-05-31 |
ca6cc863-7721-5deb-8c6f-21e71e166009 | 2020-10-01 | 2020-08-25 |
bc185a3c-5d17-512d-adc4-2f9b699b4aa8 | 2020-10-01 | 2020-08-25 |
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7b33008c-ef84-5036-96d4-d61555685078 | 2020-10-01 | 2020-09-16 |
e5f4165a-c06a-5418-ac62-93ca5a52e940 | 2020-10-01 | 2020-05-30 |
ee577267-67fc-53e3-9c78-89d1f1eff51d | 2020-10-01 | 2020-05-30 |
44664337-ed63-54fb-b6d5-9b0bedb7c5d2 | 2020-10-01 | 2020-09-21 |
a9f3a26d-b221-527c-b4ee-bf3da56d6ea0 | 2020-10-01 | 2020-05-31 |
3239da9a-d84a-56e7-96e8-1713c08060e0 | 2020-10-01 | 2020-06-18 |
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39e68a1f-333c-56ad-a43c-a3047f684740 | 2020-10-01 | 2020-06-04 |
f3933f49-b1d9-5931-82ab-f9d284f49a10 | 2020-10-01 | 2020-09-22 |
189e5393-502b-54ed-9bbc-f02a6a852135 | 2020-10-01 | 2020-07-10 |
242cd77d-2325-5bb9-8e15-70f4c7b599b1 | 2020-10-01 | 2020-09-30 |
462174b6-1a4d-55cc-8e76-2013287b61d7 | 2020-10-01 | 2020-06-23 |
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e3a54af1-d2d6-532f-9a00-f6050a36a0be | 2020-10-01 | 2020-08-26 |
4f73f345-ab66-5de1-b266-5709543e1318 | 2020-10-01 | 2020-06-04 |
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f66c426d-5ab6-56b9-beb6-0280ab8330e5 | 2020-10-01 | 2020-05-31 |
c3e4a72f-9acd-510c-8fd6-b5443238c71d | 2020-10-01 | 2020-06-01 |
0bb178c2-dd86-5f22-bf1c-063a724a9e48 | 2020-10-01 | 2020-09-03 |
Simulated daily active users of an app
667,236 records of daily user activity in an app: 51,480 users active between 1 October 2020 and 31 October 2023. Each row is one day on which one user was active. The data is simulated, based on historical activity data of a SaaS app; it contains no real users.
It is the data behind the blog post How Well Does a Markov Chain Predict Active Users? on Hugging Face, which places users in seven states each day (new, current, reactivated, resurrected, at-risk weekly active, at-risk monthly active, dormant) following Duolingo's growth model, estimates a Markov chain of transitions between them, and tests how well it predicts daily, weekly and monthly active users over six held-out months. Code: github.com/prteek/probabilistic-model-of-user-journey.
Activity records (dau_data.csv)
| column | content |
|---|---|
user_id |
anonymous user id (UUID) |
date |
a day on which the user was active (2020-10-01 to 2023-10-31) |
registration_date |
the day the user registered (2020-04-20 to 2023-10-31) |
Days without a row for a user are days on which the user was inactive.
User states, derived
The states used in the blog post (one row per user per day, 32,111,109 rows) are not stored here: a single DuckDB
query builds them from the activity records in a few seconds. It is user_journey/states.py in the code repository:
import duckdb
from user_journey.states import states
states(duckdb.connect(), "dau_data.csv").write_parquet("states.parquet")
| column | content |
|---|---|
user_id |
anonymous user id (UUID) |
date |
the day (2020-10-01 to 2023-10-31) |
is_active |
whether the user was active that day (true on the 667,236 days in the activity records) |
state |
the user's state that day (below) |
registration_date |
the day the user registered |
| state | meaning | rows |
|---|---|---|
new |
active for the first time | 50,175 |
current |
active today and in the past week | 585,129 |
reactivated |
active today, last active in the past month but not the past week | 16,847 |
resurrected |
active today, last active more than 30 days ago | 15,085 |
at_risk_wau |
not active today, active in the past week | 604,449 |
at_risk_mau |
active in the past month but not the past week | 1,683,057 |
dormant |
inactive for at least 30 days | 29,156,367 |
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