Merge pull request #3 from TasmanAnalytics/feature/mrr-improvements

✨ Improve mrr accuracy
TasmanAnalytics · Feb 22, 2024 · 1d1cb15 · 1d1cb15
2 parents 982e56f + 002960c
commit 1d1cb15
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Showing 3 changed files with 50 additions and 11 deletions.
diff --git a/models/analysis/_analysis.yml b/models/analysis/_analysis.yml
@@ -235,7 +235,7 @@ models:
               source_model: "{{ var('revenuecat_mrr_test_seed') }}"
               source_date: report_date
               source_metric: mrr
-              percent_mismatch_threshold: 0.1
+              percent_mismatch_threshold: "{{ var('revenuecat_mrr_test_threshold', 1) }}"
 
       - name: platform
         data_type: varchar

diff --git a/models/analysis/revenuecat_monthly_recurring_revenue.sql b/models/analysis/revenuecat_monthly_recurring_revenue.sql
@@ -1,8 +1,14 @@
 with
 
 subscription_transactions as (
-    select * from {{ ref('revenuecat_subscription_transactions') }} where valid_to is null
-),
+    select * from {{ ref('revenuecat_subscription_transactions') }} 
+    where 
+        valid_to is null 
+        and is_trial_period = false 
+        and ownership_type != 'FAMILY_SHARED' 
+        and datediff('s', start_time, expected_end_time)::float > 0 
+        and store != 'promotional'
+), 
 
 date_spine as (
     select distinct date_month from {{ ref('revenuecat_date_spine') }}
@@ -44,8 +50,13 @@ final as (
 
     left join
         transaction_monthly_revenue
-        on date_spine.date_month >= transaction_monthly_revenue.start_time::date
-        and last_day(date_spine.date_month, month) <= last_day(transaction_monthly_revenue.effective_end_time::date, month)
+        on transaction_monthly_revenue.start_time::date <= last_day(date_month, month)
+        and case 
+                when transaction_monthly_revenue.effective_end_time >= convert_timezone('America/Los_Angeles', 'UTC', convert_timezone('UTC', current_timestamp)::date) -- Assumes that RevenueCat runs their data model at this time. This gives an 99.9% accurate MRR number for the current month & prior months
+                then transaction_monthly_revenue.effective_end_time::date > date_spine.date_month
+                else transaction_monthly_revenue.effective_end_time::date > last_day(date_spine.date_month, month)
+            end
+
 
     group by
         date_spine.date_month,

diff --git a/models/core/revenuecat_subscription_transactions.sql b/models/core/revenuecat_subscription_transactions.sql
@@ -1,7 +1,7 @@
 {{
   config(
     materialized = 'incremental',
-    unique_key = 'row_id',
+    unique_key = 'transaction_row_id',
     )
 }}
 
@@ -50,11 +50,39 @@ renamed as (
         price_in_usd * tax_percentage as estimated_tax_in_usd,
         price_in_usd - commission_in_usd - estimated_tax_in_usd as proceeds_in_usd,
         -- Documentation for normalizing mrr_in_usd: https://docs.revenuecat.com/docs/monthly-recurring-revenue-mrr_in_usd-chart
-        case
-            when product_duration = 'P1M' then price_in_usd
-            when product_duration = 'P1Y' then price_in_usd / 12
-            when product_duration = 'P1W' then price_in_usd * 4
-            else 0
+		case 
+            when effective_end_time is not null then
+                case 
+                    /* handle cases where product_duration cannot be used for the transaction first */
+                    when (is_in_intro_offer_period = 'true' or product_duration is null) 
+                        then 
+                            case
+                                when datediff(day, start_time, expected_end_time) between 0 and 1 then round(30 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) = 3 then round(10 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 6 and 8 then round(4 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 12 and 16 then round(2 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 27 and 33 then round(1 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 58 and 62 then round(0.5 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 88 and 95 then round(0.333333 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 179 and 185 then round(0.1666666 * price_in_usd, 2)
+                                when datediff(day, start_time, expected_end_time) between 363 and 375 then round(0.08333 * price_in_usd, 2)
+                                else round(((28 / (datediff('s', start_time, expected_end_time) / (24 * 3600))) * price_in_usd), 2)
+                            end
+                        /* then handle cases where product_duration can be used */
+                    when product_duration = 'P1D' then round(30 * price_in_usd, 2)
+                    when product_duration = 'P3D' then round(10 * price_in_usd, 2)
+                    when product_duration = 'P7D' then round(4 * price_in_usd, 2)
+                    when product_duration = 'P1W' then round(4 * price_in_usd, 2)
+                    when product_duration = 'P2W' then round(2 * price_in_usd, 2)
+                    when product_duration = 'P4W' then round(1 * price_in_usd, 2)
+                    when product_duration = 'P1M' then round(1 * price_in_usd, 2)
+                    when product_duration = 'P2M' then round(0.5 * price_in_usd, 2)
+                    when product_duration = 'P3M' then round(0.333333 * price_in_usd, 2)
+                    when product_duration = 'P6M' then round(0.1666666 * price_in_usd, 2)
+                    when product_duration = 'P12M' then round(0.08333 * price_in_usd, 2)
+                    when product_duration = 'P1Y' then round(0.08333 * price_in_usd, 2)
+                    else round(((28 / (datediff('s', start_time, expected_end_time) / (24 * 3600))) * price_in_usd), 2)
+                end
         end as mrr_in_usd,
         takehome_percentage,