Merged PR 4931: KPIs Refactor Stage 3 - Remove previous Churn models
# Description Removes the models: * int_monthly_12m_window_contribution_by_deal * int_monthly_churn_metrics as well as their entries in the schema files. Project compiles and KPIs run works. This closes stage 3 of the refactor. # Checklist - [ ] The edited models and dependants run properly with production data. - [ ] The edited models are sufficiently documented. - [ ] The edited models contain PK tests, and I've ran and passed them. - [ ] I have checked for DRY opportunities with other models and docs. - [ ] I've picked the right materialization for the affected models. # Other - [ ] Check if a full-refresh is required after this PR is merged. Remove previous Churn models Related work items: #28948
This commit is contained in:
Oriol Roqué Paniagua
parent
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3 changed files with 0 additions and 427 deletions
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{{ config(materialized="table", unique_key=["date", "id_deal"]) }}
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with
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int_monthly_aggregated_metrics_history_by_deal as (
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select * from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
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),
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aggregated_metrics_by_deal_over_12_months as (
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select
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am.date,
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am.id_deal,
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am.deal_lifecycle_state,
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1.0 * count(*) over (
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partition by am.id_deal
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order by am.date
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rows between 12 preceding and 1 preceding
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) as preceding_months_count_by_deal,
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1.0 * sum(am.total_revenue_in_gbp) over (
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partition by am.id_deal
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order by am.date
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rows between 12 preceding and 1 preceding
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) as sum_total_revenue_preceding_12_months,
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1.0 * sum(am.created_bookings) over (
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partition by am.id_deal
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order by am.date
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rows between 12 preceding and 1 preceding
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) as sum_created_bookings_preceding_12_months,
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1.0 * sum(am.listings_booked_in_month) over (
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partition by am.id_deal
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order by am.date
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rows between 12 preceding and 1 preceding
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) as sum_listings_booked_in_month_preceding_12_months
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from int_monthly_aggregated_metrics_history_by_deal am
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),
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global_computation_over_12_months as (
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select
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ambd.id_deal,
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ambd.date,
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ambd.deal_lifecycle_state,
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-- Amount of active months per deal used in the computation
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ambd.preceding_months_count_by_deal,
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case
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when ambd.preceding_months_count_by_deal < 12 then true else false
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end as has_deal_been_created_less_than_12_months_ago,
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-- AVERAGE APPROACH --
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-- 12 months window sum of metric divided by amount of active months per
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-- deal
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ambd.sum_total_revenue_preceding_12_months
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/ ambd.preceding_months_count_by_deal
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as avg_total_revenue_preceding_12_months,
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ambd.sum_created_bookings_preceding_12_months
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/ ambd.preceding_months_count_by_deal
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as avg_created_bookings_preceding_12_months,
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ambd.sum_listings_booked_in_month_preceding_12_months
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/ ambd.preceding_months_count_by_deal
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as avg_listings_booked_in_month_preceding_12_months,
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-- Global amounts by sum of the 12 months window sum of metric on a given
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-- date
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sum(
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ambd.sum_total_revenue_preceding_12_months
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/ ambd.preceding_months_count_by_deal
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) over (partition by ambd.date)
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as avg_global_total_revenue_preceding_12_months,
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sum(
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ambd.sum_created_bookings_preceding_12_months
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/ ambd.preceding_months_count_by_deal
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) over (partition by ambd.date)
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as avg_global_created_bookings_preceding_12_months,
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sum(
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ambd.sum_listings_booked_in_month_preceding_12_months
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/ ambd.preceding_months_count_by_deal
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) over (partition by ambd.date)
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as avg_global_listings_booked_in_month_preceding_12_months
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from aggregated_metrics_by_deal_over_12_months ambd
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)
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select
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id_deal,
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date,
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deal_lifecycle_state,
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-- Amount of active months per deal used in the computation
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preceding_months_count_by_deal,
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-- Revenue pre-computation metrics for further aggregation downstream
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coalesce(
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avg_total_revenue_preceding_12_months, 0
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) as avg_total_revenue_preceding_12_months,
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coalesce(
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avg_global_total_revenue_preceding_12_months, 0
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) as avg_global_total_revenue_preceding_12_months,
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-- AVERAGE APPROACH --
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-- 12 months window sum of metric divided by amount of active months per deal
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coalesce(
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avg_total_revenue_preceding_12_months
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/ nullif(avg_global_total_revenue_preceding_12_months, 0),
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0
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) as total_revenue_12m_average_contribution,
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coalesce(
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avg_created_bookings_preceding_12_months
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/ nullif(avg_global_created_bookings_preceding_12_months, 0),
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0
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) as created_bookings_12m_average_contribution,
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coalesce(
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avg_listings_booked_in_month_preceding_12_months
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/ nullif(avg_global_listings_booked_in_month_preceding_12_months, 0),
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0
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) as listings_booked_in_month_12m_average_contribution
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from global_computation_over_12_months
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@ -1,111 +0,0 @@
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{% set dimensions = get_kpi_dimensions_per_model("CHURN_RATES") %}
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{% set churn_lifecycle_states = "('05-Churning')" %}
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{{ config(materialized="table", unique_key=["date", "dimension", "dimension_value"]) }}
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with
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int_monthly_12m_window_contribution_by_deal as (
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select * from {{ ref("int_monthly_12m_window_contribution_by_deal") }}
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),
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int_kpis__agg_dates_main_kpis as (
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select *
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from {{ ref("int_kpis__agg_dates_main_kpis") }}
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where
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dimension in ('global', 'by_number_of_listings', 'by_billing_country')
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and dimension_value <> 'UNSET'
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),
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int_kpis__dimension_daily_accommodation as (
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select * from {{ ref("int_kpis__dimension_daily_accommodation") }}
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),
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int_kpis__dimension_deals as (select * from {{ ref("int_kpis__dimension_deals") }}),
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churn_metrics_per_date as (
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{% for dimension in dimensions %}
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select
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m12wc.date,
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{{ dimension.dimension }} as dimension,
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{{ dimension.dimension_value }} as dimension_value,
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-- Revenue Churn 12m rolling window (absolute figures) --
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sum(
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case
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when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
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then m12wc.avg_total_revenue_preceding_12_months
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else 0
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end
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) as total_revenue_churn_preceding_12_months,
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-- Global Revenue 12m rolling window --
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max(
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m12wc.avg_global_total_revenue_preceding_12_months
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) as total_revenue_global_preceding_12_months,
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-- Churn Rates --
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sum(
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case
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when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
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then m12wc.total_revenue_12m_average_contribution
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else 0
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end
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) as total_revenue_churn_average_contribution,
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sum(
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case
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when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
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then m12wc.created_bookings_12m_average_contribution
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else 0
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end
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) as created_bookings_churn_average_contribution,
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sum(
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case
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when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
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then m12wc.listings_booked_in_month_12m_average_contribution
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else 0
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end
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) as listings_booked_in_month_churn_average_contribution
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from int_monthly_12m_window_contribution_by_deal m12wc
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{% if dimension.dimension == "'by_number_of_listings'" %}
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inner join
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int_kpis__dimension_daily_accommodation dda
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on m12wc.id_deal = dda.id_deal
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and m12wc.date = dda.date
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{% elif dimension.dimension == "'by_billing_country'" %}
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inner join
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int_kpis__dimension_deals ud
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on m12wc.id_deal = ud.id_deal
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and ud.main_billing_country_iso_3_per_deal is not null
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{% endif %}
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where deal_lifecycle_state is not null
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group by 1, 2, 3
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{% if not loop.last %}
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union all
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{% endif %}
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{% endfor %}
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)
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-- Final aggregation of subqueries --
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select
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d.year,
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d.month,
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d.day,
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d.date,
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d.dimension,
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d.dimension_value,
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d.is_end_of_month,
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d.is_current_month,
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c.total_revenue_churn_preceding_12_months,
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c.total_revenue_global_preceding_12_months,
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cast(
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c.total_revenue_churn_average_contribution as numeric(19, 6)
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) as total_revenue_churn_average_contribution,
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cast(
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c.created_bookings_churn_average_contribution as numeric(19, 6)
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) as created_bookings_churn_average_contribution,
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cast(
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c.listings_booked_in_month_churn_average_contribution as numeric(19, 6)
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) as listings_booked_in_month_churn_average_contribution
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from int_kpis__agg_dates_main_kpis d
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left join
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churn_metrics_per_date c
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on c.date = d.date
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and c.dimension = d.dimension
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and c.dimension_value = d.dimension_value
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-- Remove current month dates since data won't be available anyway. This is specific
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-- for this churn metrics model
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where d.is_current_month = false
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@ -1106,210 +1106,6 @@ models:
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- MAJOR GAIN
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- MAJOR GAIN
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- UNSET
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- UNSET
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- name: int_monthly_12m_window_contribution_by_deal
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deprecation_date: 2025-04-08
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description: |
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The main goal of this model is to provide how much a deal
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contributes to a given metric on the global amount over a
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period of 12 months.
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At the moment, this is only done for 3 metrics:
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- total_revenue_in_gbp
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- created_bookings
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- listings_booked_in_month
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The contribution is based on an Average approach:
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Over a period of 12 months, sum the value of a given a metric
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for each deal, and divide it by the amount of months we're considering
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for that deal. Sum all the average amounts per deals to get a global.
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Divide the avg per deal value vs. the sum of avgs global one.
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The average approach "boosts" the contribution of those accounts
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that have been active for less than 12 months.
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data_tests:
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- dbt_utils.unique_combination_of_columns:
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combination_of_columns:
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- date
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- id_deal
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columns:
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- name: date
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data_type: date
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description: |
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Date corresponding to the last day of the month.
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Metrics are inclusive to this date. Together with id_deal, it
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acts as the primary key of this model.
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data_tests:
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- not_null
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- name: id_deal
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data_type: string
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description: |
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Unique identifier of a Deal. Together with date, it acts as
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the primary key of this model.
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data_tests:
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- not_null
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- name: deal_lifecycle_state
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data_type: string
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description: |
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Identifier of the lifecycle state of a given deal
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in a given month.
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- name: preceding_months_count_by_deal
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data_type: integer
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description: |
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Number of months preceding to the one given by date
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that are used for the historic metric retrieval for
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a given deal. In essence it states the amount of
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months a given deal has been active before a the month
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given by date, capped at 12 months.
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data_tests:
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- dbt_expectations.expect_column_values_to_be_between:
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min_value: 0
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max_value: 12
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strictly: false
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- name: has_deal_been_created_less_than_12_months_ago
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data_type: boolean
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description: |
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Flag to identify if a given deal has been created less
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than 12 months ago (true) or not (false). It's based on the
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preceding_months_count_by_deal, and will be true on the first
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year of deal activity.
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- name: total_revenue_12m_average_contribution
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data_type: numeric
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description: |
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Share of the deal contribution on total revenue
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vs. the global amount, on the preceding 12 months
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with respect to date. It uses the average approach.
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It can be negative.
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data_tests:
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- not_null
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- name: created_bookings_12m_average_contribution
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data_type: numeric
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description: |
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Share of the deal contribution on created bookings
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vs. the global amount, on the preceding 12 months
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with respect to date. It uses the average approach.
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data_tests:
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- not_null
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- dbt_expectations.expect_column_values_to_be_between:
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min_value: 0
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max_value: 1
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strictly: false
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- name: listings_booked_in_month_12m_average_contribution
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data_type: numeric
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description: |
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Share of the deal contribution on listings booked in month
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vs. the global amount, on the preceding 12 months
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with respect to date. It uses the average approach.
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data_tests:
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- not_null
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- dbt_expectations.expect_column_values_to_be_between:
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min_value: 0
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max_value: 1
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strictly: false
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- name: avg_total_revenue_preceding_12_months
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data_type: numeric
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description: |
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Total revenue in GBP generated by a single deal
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in the 12 months period. This uses an average approach,
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meaning that the revenue of that deal is divided by the
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amount of months it has been active.
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- name: avg_global_total_revenue_preceding_12_months
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data_type: numeric
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description: |
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Total revenue in GBP generated by a all deals
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in the 12 months period. This uses an average approach,
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meaning that the revenue of each deal is divided by the
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amount of months it has been active.
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- name: int_monthly_churn_metrics
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deprecation_date: 2025-04-08
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description: |
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This model is used for global KPIs.
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It computes the churn contribution by dimension, dimension value
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and date, in a monthly basis. This model is different from the
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usual mtd ones since it strictly depends on the monthly computation
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of metrics by deal, which is done in a monthly basis rather than mtd.
|
|
||||||
In essence, it means we won't have data for the current month.
|
|
||||||
|
|
||||||
This model retrieves the 12 month contribution to global metrics
|
|
||||||
by deal and aggregates it to dimension and dimension value for those
|
|
||||||
deals that are tagged as '05-Churning' in that month. Thus, it provides
|
|
||||||
a total of 3 churn related metrics, represented as ratios over the total:
|
|
||||||
- Total Revenue (in GBP)
|
|
||||||
- Created Bookings
|
|
||||||
- Listings Booked in Month
|
|
||||||
by using the Average contribution method. For further
|
|
||||||
information, please refer to the documentation of the model:
|
|
||||||
- int_monthly_12m_window_contribution_by_deal
|
|
||||||
|
|
||||||
Lastly, when checking data at any dimension distinct from Global, at the
|
|
||||||
moment these values represent the additive contribution of churn with respect
|
|
||||||
to the global amount. This means that, for instance, if we have 10% of churn
|
|
||||||
in a month, it can be divided by 9% USA and 1% GBR since 9%+1% = 10%.
|
|
||||||
|
|
||||||
data_tests:
|
|
||||||
- dbt_utils.unique_combination_of_columns:
|
|
||||||
combination_of_columns:
|
|
||||||
- date
|
|
||||||
- dimension
|
|
||||||
- dimension_value
|
|
||||||
|
|
||||||
columns:
|
|
||||||
- name: date
|
|
||||||
data_type: date
|
|
||||||
description: The date for the month-to-date metrics.
|
|
||||||
data_tests:
|
|
||||||
- not_null
|
|
||||||
|
|
||||||
- name: dimension
|
|
||||||
data_type: string
|
|
||||||
description: The dimension or granularity of the metrics.
|
|
||||||
data_tests:
|
|
||||||
- accepted_values:
|
|
||||||
values:
|
|
||||||
- global
|
|
||||||
- by_number_of_listings
|
|
||||||
- by_billing_country
|
|
||||||
|
|
||||||
- name: dimension_value
|
|
||||||
data_type: string
|
|
||||||
description: The value or segment available for the selected dimension.
|
|
||||||
data_tests:
|
|
||||||
- not_null
|
|
||||||
|
|
||||||
- name: total_revenue_churn_preceding_12_months
|
|
||||||
data_type: numeric
|
|
||||||
description: |
|
|
||||||
Total Revenue attributed to have churned considering the
|
|
||||||
revenue generated by the deals in the 12 months period.
|
|
||||||
|
|
||||||
- name: total_revenue_global_preceding_12_months
|
|
||||||
data_type: numeric
|
|
||||||
description: |
|
|
||||||
Total Revenue generated by all deals in the 12 months period.
|
|
||||||
|
|
||||||
- name: total_revenue_churn_average_contribution
|
|
||||||
data_type: numeric
|
|
||||||
description: Total Revenue churn rate (average approach).
|
|
||||||
|
|
||||||
- name: created_bookings_churn_average_contribution
|
|
||||||
data_type: numeric
|
|
||||||
description: Created Bookings churn rate (average approach).
|
|
||||||
|
|
||||||
- name: listings_booked_in_month_churn_average_contribution
|
|
||||||
data_type: numeric
|
|
||||||
description: Listings Booked in Month churn rate (average approach).
|
|
||||||
|
|
||||||
- name: int_edeposit_and_athena_verifications
|
- name: int_edeposit_and_athena_verifications
|
||||||
description:
|
description:
|
||||||
"This table holds records on verifications for Guesty and Edeposit bookings.
|
"This table holds records on verifications for Guesty and Edeposit bookings.
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue