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Weekly breakdown and line charts

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Justin Fargione
2026-02-03 15:06:44 -05:00
parent dd83fa5ba1
commit f9b57307de
11 changed files with 26334 additions and 13313 deletions
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237
2026-01-02_query-01-booking-people.sql
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@ -1,9 +1,25 @@
-- First establish some base parameters for our users -- First establish some base parameters for our users
WITH config AS ( WITH config AS (
SELECT SELECT
-- For now we're using this as our free-trial filter. We'll treat users
-- that churn during this period differently since they likely haven't actually
-- tried the software very much. We'll also use this to isolate feature usage
-- from this period, as it's likely that user goals are different at this stage.
14 AS short_term, 14 AS short_term,
-- This is our ~day74 cutoff. For users, we want to see patterns of usage here
-- that might differ from those who either churn later or hang around for a while.
-- For feature aggregates, this should represent the "serious" efforts to use ZenMaid.
90 AS medium_term, 90 AS medium_term,
-- This is a (in theory) rarer group of churned users that lasted beyond our day 74
-- danger zone but still churned.
-- Beyond this we'll consider anyone remaining a "success" (even if they churned at some
-- point) since things like company closures, etc. start to come into play more
-- frequently.
-- For features, we'll look at this as features used in the company's day-to-day, and not
-- evaluation attempts.
360 AS long_term, 360 AS long_term,
-- We'll only look back a few years since we want to make fairish comparisons betwen users
-- exposed to "modern" ZenMaid tools.
CAST('2023-01-01' AS timestamp) AS date_cutoff CAST('2023-01-01' AS timestamp) AS date_cutoff
), ),
-- Then establish a standard core set of user data -- Then establish a standard core set of user data
@ -51,96 +67,213 @@ users_to_examine AS (
-- Finally, people who are still kicking. -- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older -- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around. -- dates since it only includes those who are STILL around.
SELECT 'active-user' AS category, uwcs.* SELECT 'long-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config FROM users_with_churn_stats uwcs, config
WHERE BILLING_STATE != 'CHURNED' -- We could also filter this by the following if we wanted to look at current users:
AND lifespan >= config.long_term -- billing_state NOT IN ('CHURNED', 'ONBOARDING', 'UNKNOWN')
-- ...but for a multi-year peek into history, we'll lose too many good users who left
-- for other reasons like closures, etc.
WHERE lifespan >= config.long_term
), ),
-- Now we'll start pulling counts from our various core features to help check usage.
-- We'll ALSO bucket these by the same eras we're using to split up our users.
-- For example, things created in the short_term are probably less serious integration
-- efforts - users are just poking around at the software to see if they want to give it a
-- serious try.
-- Medium-term (up to our day 74ish cutoff) is when users REALLY decide if they want to build
-- a business around ZenMaid.
-- Long-term should represent users who are actively using ZenMaid for their day-to-day operations.
-- Well add booking forms created by era -- Well add booking forms created by era
booking_form_counts AS ( booking_form_counts AS (
SELECT bf.user_id, -- Establish a summary of records with ages
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, WITH summary AS (
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, SELECT booking_forms.user_id, booking_forms.created_at::date - users.created_at::date AS created_age
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, FROM config, booking_forms
COUNT(*) AS total JOIN users ON booking_forms.user_id = users.id
FROM config, (
SELECT booking_forms.user_id, booking_forms.created_at::date - users.created_at::date AS created_age
FROM config, booking_forms JOIN users ON booking_forms.user_id = users.id
WHERE booking_forms.created_at >= config.date_cutoff WHERE booking_forms.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff AND users.created_at >= config.date_cutoff
) bf AND booking_forms.created_at <= users.created_at::date + config.long_term
GROUP BY bf.user_id ),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
), ),
-- Bookings made by era -- Bookings made by era
booking_counts AS ( booking_counts AS (
SELECT bk.user_id, -- Establish a summary of records with ages
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, WITH summary AS (
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, SELECT booking_forms.user_id, bookings.created_at::date - users.created_at::date AS created_age
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, FROM config,
COUNT(*) AS total booking_forms
FROM config, ( JOIN users ON booking_forms.user_id = users.id
SELECT booking_forms.user_id, bookings.created_at::date - users.created_at::date AS created_age JOIN bookings ON bookings.booking_form_id = booking_forms.id
FROM config, booking_forms WHERE booking_forms.created_at >= config.date_cutoff
JOIN users ON booking_forms.user_id = users.id AND bookings.created_at >= config.date_cutoff
JOIN bookings ON bookings.booking_form_id = booking_forms.id AND users.created_at >= config.date_cutoff
WHERE booking_forms.created_at >= config.date_cutoff ),
AND bookings.created_at >= config.date_cutoff -- Group by week for line charts
AND users.created_at >= config.date_cutoff weekly_counts AS (
) bk SELECT user_id,
GROUP BY bk.user_id jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (
SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts
JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
), ),
-- Employees added by era -- Employees added by era
employee_counts AS ( employee_counts AS (
SELECT empl.user_id, -- Establish a summary of records with ages
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, WITH summary AS (
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT employees.user_id, employees.created_at::date - users.created_at::date AS created_age SELECT employees.user_id, employees.created_at::date - users.created_at::date AS created_age
FROM config, employees JOIN users ON employees.user_id = users.id FROM config, employees
JOIN users ON employees.user_id = users.id
WHERE employees.created_at >= config.date_cutoff WHERE employees.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff AND users.created_at >= config.date_cutoff
) empl AND employees.created_at <= users.created_at::date + config.long_term
GROUP BY empl.user_id ),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
), ),
-- Contacts created by era -- Contacts created by era
contact_counts AS ( contact_counts AS (
SELECT cust.user_id, -- Establish a summary of records with ages
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, WITH summary AS (
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT customers.user_id, customers.created_at::date - users.created_at::date AS created_age SELECT customers.user_id, customers.created_at::date - users.created_at::date AS created_age
FROM config, customers JOIN users ON customers.user_id = users.id FROM config, customers
JOIN users ON customers.user_id = users.id
WHERE customers.created_at >= config.date_cutoff WHERE customers.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff AND users.created_at >= config.date_cutoff
) cust AND customers.created_at <= users.created_at::date + config.long_term
GROUP BY cust.user_id ),
), -- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table -- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id, SELECT users_to_examine.id,
users_to_examine.active,
users_to_examine.billing_state,
users_to_examine.free_trial_ends_at,
users_to_examine.category, users_to_examine.category,
users_to_examine.created_at,
users_to_examine.month_created,
users_to_examine.updated_at,
users_to_examine.lifespan,
COALESCE(booking_form_counts.short_term, 0) AS booking_forms_short_term, COALESCE(booking_form_counts.short_term, 0) AS booking_forms_short_term,
COALESCE(booking_form_counts.medium_term, 0) AS booking_forms_medium_term, COALESCE(booking_form_counts.medium_term, 0) AS booking_forms_medium_term,
COALESCE(booking_form_counts.long_term, 0) AS booking_forms_long_term, COALESCE(booking_form_counts.long_term, 0) AS booking_forms_long_term,
COALESCE(booking_form_counts.total, 0) AS booking_forms, COALESCE(booking_form_counts.total, 0) AS booking_forms,
COALESCE(booking_form_counts.weekly_counts, '{}'::jsonb) AS booking_forms_weekly_counts,
COALESCE(booking_counts.short_term, 0) AS bookings_short_term, COALESCE(booking_counts.short_term, 0) AS bookings_short_term,
COALESCE(booking_counts.medium_term, 0) AS bookings_medium_term, COALESCE(booking_counts.medium_term, 0) AS bookings_medium_term,
COALESCE(booking_counts.long_term, 0) AS bookings_long_term, COALESCE(booking_counts.long_term, 0) AS bookings_long_term,
COALESCE(booking_counts.total, 0) AS bookings, COALESCE(booking_counts.total, 0) AS bookings,
COALESCE(booking_counts.weekly_counts, '{}'::jsonb) AS bookings_weekly_counts,
COALESCE(employee_counts.short_term, 0) AS employees_short_term, COALESCE(employee_counts.short_term, 0) AS employees_short_term,
COALESCE(employee_counts.medium_term, 0) AS employees_medium_term, COALESCE(employee_counts.medium_term, 0) AS employees_medium_term,
COALESCE(employee_counts.long_term, 0) AS employees_long_term, COALESCE(employee_counts.long_term, 0) AS employees_long_term,
COALESCE(employee_counts.total, 0) AS employees, COALESCE(employee_counts.total, 0) AS employees,
COALESCE(employee_counts.weekly_counts, '{}'::jsonb) AS employee_weekly_counts,
COALESCE(contact_counts.short_term, 0) AS contacts_short_term, COALESCE(contact_counts.short_term, 0) AS contacts_short_term,
COALESCE(contact_counts.medium_term, 0) AS contacts_medium_term, COALESCE(contact_counts.medium_term, 0) AS contacts_medium_term,
COALESCE(contact_counts.long_term, 0) AS contacts_long_term, COALESCE(contact_counts.long_term, 0) AS contacts_long_term,
COALESCE(contact_counts.total, 0) AS contacts COALESCE(contact_counts.total, 0) AS contacts,
COALESCE(contact_counts.weekly_counts, '{}'::jsonb) AS contacts_weekly_counts
FROM users_to_examine FROM users_to_examine
LEFT OUTER JOIN booking_form_counts ON booking_form_counts.user_id = users_to_examine.id LEFT JOIN booking_form_counts ON booking_form_counts.user_id = users_to_examine.id
LEFT OUTER JOIN booking_counts ON booking_counts.user_id = users_to_examine.id LEFT JOIN booking_counts ON booking_counts.user_id = users_to_examine.id
LEFT OUTER JOIN employee_counts ON employee_counts.user_id = users_to_examine.id LEFT JOIN employee_counts ON employee_counts.user_id = users_to_examine.id
LEFT OUTER JOIN contact_counts ON contact_counts.user_id = users_to_examine.id LEFT JOIN contact_counts ON contact_counts.user_id = users_to_examine.id
; ;

78
2026-01-02_query-02-email.sql
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@ -1,9 +1,25 @@
-- First establish some base parameters for our users -- First establish some base parameters for our users
WITH config AS ( WITH config AS (
SELECT SELECT
-- For now we're using this as our free-trial filter. We'll treat users
-- that churn during this period differently since they likely haven't actually
-- tried the software very much. We'll also use this to isolate feature usage
-- from this period, as it's likely that user goals are different at this stage.
14 AS short_term, 14 AS short_term,
-- This is our ~day74 cutoff. For users, we want to see patterns of usage here
-- that might differ from those who either churn later or hang around for a while.
-- For feature aggregates, this should represent the "serious" efforts to use ZenMaid.
90 AS medium_term, 90 AS medium_term,
-- This is a (in theory) rarer group of churned users that lasted beyond our day 74
-- danger zone but still churned.
-- Beyond this we'll consider anyone remaining a "success" (even if they churned at some
-- point) since things like company closures, etc. start to come into play more
-- frequently.
-- For features, we'll look at this as features used in the company's day-to-day, and not
-- evaluation attempts.
360 AS long_term, 360 AS long_term,
-- We'll only look back a few years since we want to make fairish comparisons betwen users
-- exposed to "modern" ZenMaid tools.
CAST('2023-01-01' AS timestamp) AS date_cutoff CAST('2023-01-01' AS timestamp) AS date_cutoff
), ),
-- Then establish a standard core set of user data -- Then establish a standard core set of user data
@ -51,33 +67,67 @@ users_to_examine AS (
-- Finally, people who are still kicking. -- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older -- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around. -- dates since it only includes those who are STILL around.
SELECT 'active-user' AS category, uwcs.* SELECT 'long-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config FROM users_with_churn_stats uwcs, config
WHERE BILLING_STATE != 'CHURNED' -- We could also filter this by the following if we wanted to look at current users:
AND lifespan >= config.long_term -- billing_state NOT IN ('CHURNED', 'ONBOARDING', 'UNKNOWN')
-- ...but for a multi-year peek into history, we'll lose too many good users who left
-- for other reasons like closures, etc.
WHERE lifespan >= config.long_term
), ),
-- Emails sent by era -- Emails sent by era
email_counts AS ( email_counts AS (
SELECT email.user_id, -- Establish a summary of records with ages
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, WITH summary AS (
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT sent_emails.user_id, sent_emails.created_at::date - users.created_at::date AS created_age SELECT sent_emails.user_id, sent_emails.created_at::date - users.created_at::date AS created_age
FROM config, sent_emails JOIN users ON sent_emails.user_id = users.id FROM config, sent_emails
JOIN users ON sent_emails.user_id = users.id
WHERE sent_emails.created_at >= config.date_cutoff WHERE sent_emails.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff AND users.created_at >= config.date_cutoff
) email AND sent_emails.created_at <= users.created_at::date + config.long_term
GROUP BY email.user_id ),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
) )
-- Finally, we'll flatten it all out into a repurposable flat table -- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id, SELECT users_to_examine.id,
users_to_examine.active,
users_to_examine.billing_state,
users_to_examine.free_trial_ends_at,
users_to_examine.category, users_to_examine.category,
users_to_examine.created_at,
users_to_examine.month_created,
users_to_examine.updated_at,
users_to_examine.lifespan,
COALESCE(email_counts.short_term, 0) AS emails_short_term, COALESCE(email_counts.short_term, 0) AS emails_short_term,
COALESCE(email_counts.medium_term, 0) AS emails_medium_term, COALESCE(email_counts.medium_term, 0) AS emails_medium_term,
COALESCE(email_counts.long_term, 0) AS emails_long_term, COALESCE(email_counts.long_term, 0) AS emails_long_term,
COALESCE(email_counts.total, 0) AS emails COALESCE(email_counts.total, 0) AS emails,
COALESCE(email_counts.weekly_counts, '{}'::jsonb) AS emails_weekly_counts
FROM users_to_examine FROM users_to_examine
LEFT OUTER JOIN email_counts ON email_counts.user_id = users_to_examine.id LEFT JOIN email_counts ON email_counts.user_id = users_to_examine.id
; ;

151
2026-01-02_query-03-sms-appts-checklist.sql
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@ -1,151 +0,0 @@
-- First establish some base parameters for our users
WITH config AS (
SELECT
14 AS short_term,
90 AS medium_term,
360 AS long_term,
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'active-user' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE BILLING_STATE != 'CHURNED'
AND lifespan >= config.long_term
),
-- Messages sent by era
sms_counts AS (
SELECT texts.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT sent_texts.user_id, sent_texts.created_at::date - users.created_at::date AS created_age
FROM config, sent_texts JOIN users ON sent_texts.user_id = users.id
WHERE sent_texts.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) texts
GROUP BY texts.user_id
),
-- Appointments by era
appointment_counts AS (
SELECT appts.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT appointments.user_id, appointments.created_at::date - users.created_at::date AS created_age
FROM config, appointments JOIN users ON appointments.user_id = users.id
WHERE appointments.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) appts
GROUP BY appts.user_id
),
-- Checklists created by era
checklist_counts AS (
SELECT checks.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT checklist_templates.user_id, checklists.created_at::date - users.created_at::date AS created_age
FROM config, checklist_templates
JOIN users ON checklist_templates.user_id = users.id
JOIN checklists ON checklists.checklist_template_id = checklist_templates.id
WHERE checklist_templates.created_at >= config.date_cutoff
AND checklists.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) checks
GROUP BY checks.user_id
),
-- Checklist events created by era
checklist_filled_counts AS (
SELECT events.user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, (
SELECT employees.user_id, checklist_item_events.created_at::date - users.created_at::date AS created_age
FROM config, employees
JOIN users ON employees.user_id = users.id
JOIN checklist_item_events ON checklist_item_events.employee_id = employees.id
WHERE checklist_item_events.created_at >= config.date_cutoff
AND checklist_item_events.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
) events
GROUP BY events.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.category,
users_to_examine.month_created,
users_to_examine.lifespan,
COALESCE(sms_counts.short_term, 0) AS sms_short_term,
COALESCE(sms_counts.medium_term, 0) AS sms_medium_term,
COALESCE(sms_counts.long_term, 0) AS sms_long_term,
COALESCE(sms_counts.total, 0) AS sms,
COALESCE(appointment_counts.short_term, 0) AS appointments_short_term,
COALESCE(appointment_counts.medium_term, 0) AS appointments_medium_term,
COALESCE(appointment_counts.long_term, 0) AS appointments_long_term,
COALESCE(appointment_counts.total, 0) AS appointments,
COALESCE(checklist_counts.short_term, 0) AS checklists_short_term,
COALESCE(checklist_counts.medium_term, 0) AS checklists_medium_term,
COALESCE(checklist_counts.long_term, 0) AS checklists_long_term,
COALESCE(checklist_counts.total, 0) AS checklists,
COALESCE(checklist_filled_counts.short_term, 0) AS checklists_filled_short_term,
COALESCE(checklist_filled_counts.medium_term, 0) AS checklists_filled_medium_term,
COALESCE(checklist_filled_counts.long_term, 0) AS checklists_filled_long_term,
COALESCE(checklist_filled_counts.total, 0) AS checklists_filled
FROM users_to_examine
LEFT OUTER JOIN sms_counts ON sms_counts.user_id = users_to_examine.id
LEFT OUTER JOIN appointment_counts ON appointment_counts.user_id = users_to_examine.id
LEFT OUTER JOIN checklist_counts ON checklist_counts.user_id = users_to_examine.id
LEFT OUTER JOIN checklist_filled_counts ON checklist_filled_counts.user_id = users_to_examine.id
;

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-- First establish some base parameters for our users
WITH config AS (
SELECT
-- For now we're using this as our free-trial filter. We'll treat users
-- that churn during this period differently since they likely haven't actually
-- tried the software very much. We'll also use this to isolate feature usage
-- from this period, as it's likely that user goals are different at this stage.
14 AS short_term,
-- This is our ~day74 cutoff. For users, we want to see patterns of usage here
-- that might differ from those who either churn later or hang around for a while.
-- For feature aggregates, this should represent the "serious" efforts to use ZenMaid.
90 AS medium_term,
-- This is a (in theory) rarer group of churned users that lasted beyond our day 74
-- danger zone but still churned.
-- Beyond this we'll consider anyone remaining a "success" (even if they churned at some
-- point) since things like company closures, etc. start to come into play more
-- frequently.
-- For features, we'll look at this as features used in the company's day-to-day, and not
-- evaluation attempts.
360 AS long_term,
-- We'll only look back a few years since we want to make fairish comparisons betwen users
-- exposed to "modern" ZenMaid tools.
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'long-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
-- We could also filter this by the following if we wanted to look at current users:
-- billing_state NOT IN ('CHURNED', 'ONBOARDING', 'UNKNOWN')
-- ...but for a multi-year peek into history, we'll lose too many good users who left
-- for other reasons like closures, etc.
WHERE lifespan >= config.long_term
),
-- Messages sent by era
sms_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT sent_texts.user_id, sent_texts.created_at::date - users.created_at::date AS created_age
FROM config, sent_texts
JOIN users ON sent_texts.user_id = users.id
WHERE sent_texts.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND sent_texts.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Appointments by era
appointment_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT appointments.user_id, appointments.created_at::date - users.created_at::date AS created_age
FROM config, appointments
JOIN users ON appointments.user_id = users.id
WHERE appointments.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND appointments.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.active,
users_to_examine.billing_state,
users_to_examine.free_trial_ends_at,
users_to_examine.category,
users_to_examine.created_at,
users_to_examine.month_created,
users_to_examine.updated_at,
users_to_examine.lifespan,
COALESCE(sms_counts.short_term, 0) AS sms_short_term,
COALESCE(sms_counts.medium_term, 0) AS sms_medium_term,
COALESCE(sms_counts.long_term, 0) AS sms_long_term,
COALESCE(sms_counts.total, 0) AS sms,
COALESCE(sms_counts.weekly_counts, '{}'::jsonb) AS sms_weekly_counts,
COALESCE(appointment_counts.short_term, 0) AS appointments_short_term,
COALESCE(appointment_counts.medium_term, 0) AS appointments_medium_term,
COALESCE(appointment_counts.long_term, 0) AS appointments_long_term,
COALESCE(appointment_counts.total, 0) AS appointments,
COALESCE(appointment_counts.weekly_counts, '{}'::jsonb) AS appointments_weekly_counts
FROM users_to_examine
LEFT JOIN sms_counts ON sms_counts.user_id = users_to_examine.id
LEFT JOIN appointment_counts ON appointment_counts.user_id = users_to_examine.id
;

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-- First establish some base parameters for our users
WITH config AS (
SELECT
-- For now we're using this as our free-trial filter. We'll treat users
-- that churn during this period differently since they likely haven't actually
-- tried the software very much. We'll also use this to isolate feature usage
-- from this period, as it's likely that user goals are different at this stage.
14 AS short_term,
-- This is our ~day74 cutoff. For users, we want to see patterns of usage here
-- that might differ from those who either churn later or hang around for a while.
-- For feature aggregates, this should represent the "serious" efforts to use ZenMaid.
90 AS medium_term,
-- This is a (in theory) rarer group of churned users that lasted beyond our day 74
-- danger zone but still churned.
-- Beyond this we'll consider anyone remaining a "success" (even if they churned at some
-- point) since things like company closures, etc. start to come into play more
-- frequently.
-- For features, we'll look at this as features used in the company's day-to-day, and not
-- evaluation attempts.
360 AS long_term,
-- We'll only look back a few years since we want to make fairish comparisons betwen users
-- exposed to "modern" ZenMaid tools.
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'long-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
-- We could also filter this by the following if we wanted to look at current users:
-- billing_state NOT IN ('CHURNED', 'ONBOARDING', 'UNKNOWN')
-- ...but for a multi-year peek into history, we'll lose too many good users who left
-- for other reasons like closures, etc.
WHERE lifespan >= config.long_term
),
-- Checklists created by era
checklist_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT checklist_templates.user_id, checklists.created_at::date - users.created_at::date AS created_age
FROM config, checklist_templates
JOIN users ON checklist_templates.user_id = users.id
JOIN checklists ON checklists.checklist_template_id = checklist_templates.id
WHERE checklists.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND checklists.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Checklist events created by era
checklist_filled_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT employees.user_id, checklist_item_events.created_at::date - users.created_at::date AS created_age
FROM config, employees
JOIN users ON employees.user_id = users.id
JOIN checklist_item_events ON checklist_item_events.employee_id = employees.id
WHERE checklist_item_events.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND checklist_item_events.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.active,
users_to_examine.billing_state,
users_to_examine.free_trial_ends_at,
users_to_examine.category,
users_to_examine.created_at,
users_to_examine.month_created,
users_to_examine.updated_at,
users_to_examine.lifespan,
COALESCE(checklist_counts.short_term, 0) AS checklists_short_term,
COALESCE(checklist_counts.medium_term, 0) AS checklists_medium_term,
COALESCE(checklist_counts.long_term, 0) AS checklists_long_term,
COALESCE(checklist_counts.total, 0) AS checklists,
COALESCE(checklist_counts.weekly_counts, '{}'::jsonb) AS checklists_weekly_counts,
COALESCE(checklist_filled_counts.short_term, 0) AS checklists_filled_short_term,
COALESCE(checklist_filled_counts.medium_term, 0) AS checklists_filled_medium_term,
COALESCE(checklist_filled_counts.long_term, 0) AS checklists_filled_long_term,
COALESCE(checklist_filled_counts.total, 0) AS checklists_filled,
COALESCE(checklist_filled_counts.weekly_counts, '{}'::jsonb) AS checklists_filled_weekly_counts
FROM users_to_examine
LEFT JOIN checklist_counts ON checklist_counts.user_id = users_to_examine.id
LEFT JOIN checklist_filled_counts ON checklist_filled_counts.user_id = users_to_examine.id
;

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-- First establish some base parameters for our users
WITH config AS (
SELECT
-- For now we're using this as our free-trial filter. We'll treat users
-- that churn during this period differently since they likely haven't actually
-- tried the software very much. We'll also use this to isolate feature usage
-- from this period, as it's likely that user goals are different at this stage.
14 AS short_term,
-- This is our ~day74 cutoff. For users, we want to see patterns of usage here
-- that might differ from those who either churn later or hang around for a while.
-- For feature aggregates, this should represent the "serious" efforts to use ZenMaid.
90 AS medium_term,
-- This is a (in theory) rarer group of churned users that lasted beyond our day 74
-- danger zone but still churned.
-- Beyond this we'll consider anyone remaining a "success" (even if they churned at some
-- point) since things like company closures, etc. start to come into play more
-- frequently.
-- For features, we'll look at this as features used in the company's day-to-day, and not
-- evaluation attempts.
360 AS long_term,
-- We'll only look back a few years since we want to make fairish comparisons betwen users
-- exposed to "modern" ZenMaid tools.
CAST('2023-01-01' AS timestamp) AS date_cutoff
),
-- Then establish a standard core set of user data
users_with_churn_stats AS (
SELECT id, active, billing_state,
DATE_TRUNC('month', created_at) AS month_created,
created_at, free_trial_ends_at, updated_at,
updated_at::date - created_at::date AS lifespan
FROM users, config
-- We'll only look at newish users.
WHERE created_at >= config.date_cutoff
),
-- Next use that core data to extract the records we want to analyze
-- and categorize them by date and status
users_to_examine AS (
-- First the users who exited just after the trial
SELECT 'quick-exit' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan <= config.short_term
UNION
-- Next, users who gave the tools some time but still left
SELECT 'fair-trial' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan < config.medium_term
AND lifespan > config.short_term
UNION
-- Then some longer-time users who still churned.
-- We'll cut this off after 6 months since afterwards
-- company closures, etc. could factor in.
SELECT 'short-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
WHERE billing_state = 'CHURNED'
AND lifespan >= config.medium_term
AND lifespan < config.long_term
UNION
-- Finally, people who are still kicking.
-- This cohort is going to be a little wrong for older
-- dates since it only includes those who are STILL around.
SELECT 'long-termer' AS category, uwcs.*
FROM users_with_churn_stats uwcs, config
-- We could also filter this by the following if we wanted to look at current users:
-- billing_state NOT IN ('CHURNED', 'ONBOARDING', 'UNKNOWN')
-- ...but for a multi-year peek into history, we'll lose too many good users who left
-- for other reasons like closures, etc.
WHERE lifespan >= config.long_term
),
-- Now we'll start pulling counts from our various core features to help check usage.
-- We'll ALSO bucket these by the same eras we're using to split up our users.
-- For example, things created in the short_term are probably less serious integration
-- efforts - users are just poking around at the software to see if they want to give it a
-- serious try.
-- Medium-term (up to our day 74ish cutoff) is when users REALLY decide if they want to build
-- a business around ZenMaid.
-- Long-term should represent users who are actively using ZenMaid for their day-to-day operations.
-- Well add booking forms created by era
booking_form_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT booking_forms.user_id, booking_forms.created_at::date - users.created_at::date AS created_age
FROM config, booking_forms
JOIN users ON booking_forms.user_id = users.id
WHERE booking_forms.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND booking_forms.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Bookings made by era
booking_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT booking_forms.user_id, bookings.created_at::date - users.created_at::date AS created_age
FROM config,
booking_forms
JOIN users ON booking_forms.user_id = users.id
JOIN bookings ON bookings.booking_form_id = booking_forms.id
WHERE booking_forms.created_at >= config.date_cutoff
AND bookings.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (
SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts
JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Employees added by era
employee_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT employees.user_id, employees.created_at::date - users.created_at::date AS created_age
FROM config, employees
JOIN users ON employees.user_id = users.id
WHERE employees.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND employees.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Contacts created by era
contact_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT customers.user_id, customers.created_at::date - users.created_at::date AS created_age
FROM config, customers
JOIN users ON customers.user_id = users.id
WHERE customers.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND customers.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Emails sent by era
email_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT sent_emails.user_id, sent_emails.created_at::date - users.created_at::date AS created_age
FROM config, sent_emails
JOIN users ON sent_emails.user_id = users.id
WHERE sent_emails.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND sent_emails.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Messages sent by era
sms_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT sent_texts.user_id, sent_texts.created_at::date - users.created_at::date AS created_age
FROM config, sent_texts
JOIN users ON sent_texts.user_id = users.id
WHERE sent_texts.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND sent_texts.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Appointments by era
appointment_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT appointments.user_id, appointments.created_at::date - users.created_at::date AS created_age
FROM config, appointments
JOIN users ON appointments.user_id = users.id
WHERE appointments.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND appointments.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Checklists created by era
checklist_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT checklist_templates.user_id, checklists.created_at::date - users.created_at::date AS created_age
FROM config, checklist_templates
JOIN users ON checklist_templates.user_id = users.id
JOIN checklists ON checklists.checklist_template_id = checklist_templates.id
WHERE checklists.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND checklists.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
),
-- Checklist events created by era
checklist_filled_counts AS (
-- Establish a summary of records with ages
WITH summary AS (
SELECT employees.user_id, checklist_item_events.created_at::date - users.created_at::date AS created_age
FROM config, employees
JOIN users ON employees.user_id = users.id
JOIN checklist_item_events ON checklist_item_events.employee_id = employees.id
WHERE checklist_item_events.created_at >= config.date_cutoff
AND users.created_at >= config.date_cutoff
AND checklist_item_events.created_at <= users.created_at::date + config.long_term
),
-- Group by week for line charts
weekly_counts AS (
SELECT user_id,
jsonb_object_agg(
'week_' || weeks_in,
weekly_count
) AS weekly_counts,
COUNT(*) AS total
FROM (SELECT user_id, FLOOR(created_age / 7) AS weeks_in, COUNT(*) as weekly_count
FROM summary
GROUP BY user_id, weeks_in) by_weeks
GROUP BY user_id
),
-- Group by era for bar charts
bucket_counts AS (
SELECT user_id,
SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
COUNT(*) AS total
FROM config, summary
GROUP BY user_id
)
-- Put it all together
SELECT bucket_counts.*, weekly_counts.weekly_counts
FROM bucket_counts JOIN weekly_counts ON weekly_counts.user_id = bucket_counts.user_id
)
-- Finally, we'll flatten it all out into a repurposable flat table
SELECT users_to_examine.id,
users_to_examine.active,
users_to_examine.billing_state,
users_to_examine.free_trial_ends_at,
users_to_examine.category,
users_to_examine.created_at,
users_to_examine.month_created,
users_to_examine.updated_at,
users_to_examine.lifespan,
COALESCE(booking_form_counts.short_term, 0) AS booking_forms_short_term,
COALESCE(booking_form_counts.medium_term, 0) AS booking_forms_medium_term,
COALESCE(booking_form_counts.long_term, 0) AS booking_forms_long_term,
COALESCE(booking_form_counts.total, 0) AS booking_forms,
COALESCE(booking_form_counts.weekly_counts, '{}'::jsonb) AS booking_forms_weekly_counts,
COALESCE(booking_counts.short_term, 0) AS bookings_short_term,
COALESCE(booking_counts.medium_term, 0) AS bookings_medium_term,
COALESCE(booking_counts.long_term, 0) AS bookings_long_term,
COALESCE(booking_counts.total, 0) AS bookings,
COALESCE(booking_counts.weekly_counts, '{}'::jsonb) AS bookings_weekly_counts,
COALESCE(employee_counts.short_term, 0) AS employees_short_term,
COALESCE(employee_counts.medium_term, 0) AS employees_medium_term,
COALESCE(employee_counts.long_term, 0) AS employees_long_term,
COALESCE(employee_counts.total, 0) AS employees,
COALESCE(employee_counts.weekly_counts, '{}'::jsonb) AS employee_weekly_counts,
COALESCE(contact_counts.short_term, 0) AS contacts_short_term,
COALESCE(contact_counts.medium_term, 0) AS contacts_medium_term,
COALESCE(contact_counts.long_term, 0) AS contacts_long_term,
COALESCE(contact_counts.total, 0) AS contacts,
COALESCE(contact_counts.weekly_counts, '{}'::jsonb) AS contacts_weekly_counts,
COALESCE(email_counts.short_term, 0) AS emails_short_term,
COALESCE(email_counts.medium_term, 0) AS emails_medium_term,
COALESCE(email_counts.long_term, 0) AS emails_long_term,
COALESCE(email_counts.total, 0) AS emails,
COALESCE(email_counts.weekly_counts, '{}'::jsonb) AS emails_weekly_counts,
COALESCE(sms_counts.short_term, 0) AS sms_short_term,
COALESCE(sms_counts.medium_term, 0) AS sms_medium_term,
COALESCE(sms_counts.long_term, 0) AS sms_long_term,
COALESCE(sms_counts.total, 0) AS sms,
COALESCE(sms_counts.weekly_counts, '{}'::jsonb) AS sms_weekly_counts,
COALESCE(appointment_counts.short_term, 0) AS appointments_short_term,
COALESCE(appointment_counts.medium_term, 0) AS appointments_medium_term,
COALESCE(appointment_counts.long_term, 0) AS appointments_long_term,
COALESCE(appointment_counts.total, 0) AS appointments,
COALESCE(appointment_counts.weekly_counts, '{}'::jsonb) AS appointments_weekly_counts,
COALESCE(checklist_counts.short_term, 0) AS checklists_short_term,
COALESCE(checklist_counts.medium_term, 0) AS checklists_medium_term,
COALESCE(checklist_counts.long_term, 0) AS checklists_long_term,
COALESCE(checklist_counts.total, 0) AS checklists,
COALESCE(checklist_counts.weekly_counts, '{}'::jsonb) AS checklists_weekly_counts,
COALESCE(checklist_filled_counts.short_term, 0) AS checklists_filled_short_term,
COALESCE(checklist_filled_counts.medium_term, 0) AS checklists_filled_medium_term,
COALESCE(checklist_filled_counts.long_term, 0) AS checklists_filled_long_term,
COALESCE(checklist_filled_counts.total, 0) AS checklists_filled,
COALESCE(checklist_filled_counts.weekly_counts, '{}'::jsonb) AS checklists_filled_weekly_counts
FROM users_to_examine
LEFT JOIN booking_form_counts ON booking_form_counts.user_id = users_to_examine.id
LEFT JOIN booking_counts ON booking_counts.user_id = users_to_examine.id
LEFT JOIN employee_counts ON employee_counts.user_id = users_to_examine.id
LEFT JOIN contact_counts ON contact_counts.user_id = users_to_examine.id
LEFT JOIN email_counts ON email_counts.user_id = users_to_examine.id
LEFT JOIN sms_counts ON sms_counts.user_id = users_to_examine.id
LEFT JOIN appointment_counts ON appointment_counts.user_id = users_to_examine.id
LEFT JOIN checklist_counts ON checklist_counts.user_id = users_to_examine.id
LEFT JOIN checklist_filled_counts ON checklist_filled_counts.user_id = users_to_examine.id
;

6
README.md
View File

@ -13,3 +13,9 @@ poetry run jupyter notebook
# or # or
poetry run jupyter lab poetry run jupyter lab
``` ```
## Export to PDF
```bash
poetry run jupyter nbconvert --to pdf churn-analysis.ipynb --no-input
```

8979
churn-analysis.ipynb
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29334
churn.csv
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File diff suppressed because one or more lines are too long

2
poetry.lock generated
View File

@ -2576,4 +2576,4 @@ files = [
[metadata] [metadata]
lock-version = "2.1" lock-version = "2.1"
python-versions = "^3.13" python-versions = "^3.13"
content-hash = "85e5d41ce436d80a788baf7d9bab8c00d0e9f7957eebb0b219e4146125aea4db" content-hash = "875e088d07955f818673c7fd9992a9bbc85de3e6198acb78d09bfc9cfed0b821"

1
pyproject.toml
View File

@ -10,6 +10,7 @@ python = "^3.13"
jupyter = "^1.1.1" jupyter = "^1.1.1"
pandas = "^3.0.0" pandas = "^3.0.0"
plotly = "^6.0.0" plotly = "^6.0.0"
nbconvert = "^7.17.0"
[build-system] [build-system]
requires = ["poetry-core>=2.0.0,<3.0.0"] requires = ["poetry-core>=2.0.0,<3.0.0"]