Added Model

models/marts/reporting/rep_exchange__survival_analysis.sql

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+-- Survival analysis code reference from https://www.crosstab.io/articles/sql-survival-curves/
+
+-- AVAILABLE FILTER VALUE 
+-- Source:preset_custom_filter Column:day_use_to_churn
+{% set day_use_to_churn = 61 %}
+
+-- IMPORT
+WITH companies AS (
+    SELECT * FROM {{ ref('dim_exchange__companies') }} 
+),
+
+transaction_orders AS (
+    SELECT * FROM {{ ref('fct_exchange__transaction_orders') }}
+),
+
+
+-- LOGIC
+-- duration_table cte computes the survival time and customer churned indicator
+duration_table AS (
+
+    SELECT
+        companies.company_id,                   --subject
+        companies.company_created_datetime,     -- event start datetime
+        COUNT(transaction_orders.order_id)                                                              AS count_order,
+        COUNT(IFF(transaction_orders.order_status = 'COMPLETED', transaction_orders.order_id, null))    AS count_completed_order,
+        MAX(transaction_orders.order_created_datetime)                                                  AS last_order_datetime,
+        COALESCE(last_order_datetime, company_created_datetime)                                         AS last_activity_datetime,
+        IFF(last_order_datetime IS NULL, 1, 0)                                                          AS is_never_order_company,
+
+        -- if is_churn_company = 0, the data will be censored
+        IFF(DATEDIFF(day, last_activity_datetime, CURRENT_DATE()) >= {{day_use_to_churn}}, 1, 0 )       AS is_churn_company,
+        
+        CASE 
+            WHEN is_churn_company = 1 THEN
+                DATEDIFF(day, companies.company_created_datetime, last_activity_datetime ) + {{day_use_to_churn}}
+            ELSE
+                DATEDIFF(day, companies.company_created_datetime, CURRENT_DATE())
+        END     AS survival_time_days           -- event duration
+    
+    FROM companies
+    
+    LEFT JOIN transaction_orders
+        ON (companies.company_id = transaction_orders.company_id)
+
+    GROUP BY
+        companies.company_id,
+        companies.company_created_datetime
+
+    HAVING
+        -- companies without order will not be relevant to the analysis
+        is_never_order_company = 0
+
+),
+
+-- the daily_tally cte count the total number of observations at each survival_time_day
+-- and the number of company that have churned at that survival_time_day
+daily_observation_tally AS (
+
+    SELECT
+        survival_time_days,
+        COUNT(survival_time_days)   AS total_number_of_observations,
+        SUM(is_churn_company)       AS events   -- considering only churned company
+
+    FROM duration_table
+    
+    GROUP BY survival_time_days
+
+    ORDER BY survival_time_days
+
+),
+
+-- the cumulative_tally cte counts the number of subjects still at risk of experiencing churn
+cumulative_tally AS (
+
+    SELECT
+        survival_time_days,
+        events,
+        total_number_of_observations,
+        
+        ( SELECT COUNT( DISTINCT(company_id) ) FROM duration_table )    AS total_number_of_subjects,
+
+        -- cumulative sum of observations at all previous survival_time_days SUBTRACTED by total_number_of_subjects
+        total_number_of_subjects - COALESCE(SUM(total_number_of_observations) OVER (
+            ORDER BY survival_time_days ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING), 0
+            )   AS at_risk
+
+    FROM daily_observation_tally
+
+),
+
+-- At each survival_time_day, count number of censored subject
+-- censored subjects = # subject at risk - churned - # subject at risk in the next duration
+compute_censored_subjects AS (
+    
+    SELECT
+        total_number_of_subjects,
+        survival_time_days,
+        at_risk,
+        total_number_of_observations,
+        events,
+        at_risk - events - COALESCE(LEAD(at_risk, 1) OVER (ORDER BY survival_time_days), 0)     AS censored
+
+    FROM cumulative_tally
+
+    -- Simply subtracting events from number of observations would incorrectly ignore subjects censored at durations that are dropped from the output table
+    WHERE events > 0
+
+),
+
+
+compute_probability AS (
+
+    SELECT
+        *,
+
+        -- The survival probability represents the probability of customers that will not churn up to a specific tenure
+        -- Example: survival_day = 61, survival_prob = 95%. For customers with 61 days of tenure, the customer has a 95% chance of not churning.
+        EXP(SUM(LN(1 - events / at_risk)) OVER (
+            ORDER BY survival_time_days ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+        ))      AS survival_probability,
+
+        100 * (1 - EXP(SUM(LN(1 - events / at_risk)) OVER (
+            ORDER BY survival_time_days ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+        )))     AS conversion_percentage,
+
+        SUM(events / at_risk) OVER (
+            ORDER BY survival_time_days ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+        )       AS cumulative_hazard,
+
+        '{{ modules.datetime.datetime.now(modules.pytz.timezone("Asia/Kuala_Lumpur")) }}'       AS _dbt_ran_datetime
+
+    FROM compute_censored_subjects
+
+),
+
+
+-- FINAL
+final__rep_exchange__survival_analysis AS (
+    
+    SELECT
+        -- dimension
+        survival_time_days,
+        at_risk,
+        total_number_of_observations,
+        events,
+        censored,
+
+        -- measures
+        survival_probability,
+        conversion_percentage,
+        cumulative_hazard,
+
+        -- metadata
+        _dbt_ran_datetime
+    
+    FROM compute_probability
+
+)
+
+SELECT * FROM final__rep_exchange__survival_analysis