84 lines
3.2 KiB
SQL
84 lines
3.2 KiB
SQL
-- First establish some base parameters for our users
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WITH config AS (
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SELECT
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14 AS short_term,
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90 AS medium_term,
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360 AS long_term,
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CAST('2023-01-01' AS timestamp) AS date_cutoff
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),
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-- Then establish a standard core set of user data
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users_with_churn_stats AS (
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SELECT id, active, billing_state,
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DATE_TRUNC('month', created_at) AS month_created,
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created_at, free_trial_ends_at, updated_at,
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updated_at::date - created_at::date AS lifespan
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FROM users, config
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-- We'll only look at newish users.
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WHERE created_at >= config.date_cutoff
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),
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-- Next use that core data to extract the records we want to analyze
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-- and categorize them by date and status
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users_to_examine AS (
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-- First the users who exited just after the trial
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SELECT 'quick-exit' AS category, uwcs.*
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FROM users_with_churn_stats uwcs, config
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WHERE billing_state = 'CHURNED'
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AND lifespan <= config.short_term
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UNION
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-- Next, users who gave the tools some time but still left
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SELECT 'fair-trial' AS category, uwcs.*
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FROM users_with_churn_stats uwcs, config
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WHERE billing_state = 'CHURNED'
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AND lifespan < config.medium_term
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AND lifespan > config.short_term
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UNION
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-- Then some longer-time users who still churned.
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-- We'll cut this off after 6 months since afterwards
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-- company closures, etc. could factor in.
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SELECT 'short-termer' AS category, uwcs.*
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FROM users_with_churn_stats uwcs, config
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WHERE billing_state = 'CHURNED'
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AND lifespan >= config.medium_term
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AND lifespan < config.long_term
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UNION
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-- Finally, people who are still kicking.
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-- This cohort is going to be a little wrong for older
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-- dates since it only includes those who are STILL around.
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SELECT 'active-user' AS category, uwcs.*
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FROM users_with_churn_stats uwcs, config
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WHERE BILLING_STATE != 'CHURNED'
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AND lifespan >= config.long_term
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),
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-- Emails sent by era
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email_counts AS (
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SELECT email.user_id,
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SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term,
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SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term,
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SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term,
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COUNT(*) AS total
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FROM config, (
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SELECT sent_emails.user_id, sent_emails.created_at::date - users.created_at::date AS created_age
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FROM config, sent_emails JOIN users ON sent_emails.user_id = users.id
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WHERE sent_emails.created_at >= config.date_cutoff
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AND users.created_at >= config.date_cutoff
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) email
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GROUP BY email.user_id
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)
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-- Finally, we'll flatten it all out into a repurposable flat table
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SELECT users_to_examine.id,
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users_to_examine.category,
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COALESCE(email_counts.short_term, 0) AS emails_short_term,
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COALESCE(email_counts.medium_term, 0) AS emails_medium_term,
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COALESCE(email_counts.long_term, 0) AS emails_long_term,
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COALESCE(email_counts.total, 0) AS emails
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FROM users_to_examine
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LEFT OUTER JOIN email_counts ON email_counts.user_id = users_to_examine.id
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;
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