data-dwh-dbt-project/models/intermediate/kpis/int_kpis__agg_monthly_churn_contribution.sql

{% set dimensions = get_kpi_dimensions_per_model("CHURN_RATES") %}
{% set churn_lifecycle_states = "('05-Churning')" %}

{{ config(materialized="table", unique_key=["date", "dimension", "dimension_value"]) }}
with
    int_kpis__lifecycle_daily_deal as (
        select * from {{ ref("int_kpis__lifecycle_daily_deal") }}
    ),
    int_kpis__agg_dates_main_kpis as (
        select * from {{ ref("int_kpis__agg_dates_main_kpis") }}
    ),
    created_bookings as (
        select *
        from {{ ref("int_kpis__agg_monthly_created_bookings") }}
        where dimension in ('by_deal') and dimension_value <> 'UNSET'
    ),
    listings as (
        select *
        from {{ ref("int_kpis__agg_daily_listings") }}
        where
            dimension in ('by_deal')
            and dimension_value <> 'UNSET'
            and is_end_of_month = true
    ),
    total_and_retained_revenue as (
        select *
        from {{ ref("int_kpis__agg_monthly_total_and_retained_revenue") }}
        where dimension in ('by_deal') and dimension_value <> 'UNSET'
    ),
    int_kpis__dimension_daily_accommodation as (
        select * from {{ ref("int_kpis__dimension_daily_accommodation") }}
    ),
    int_kpis__dimension_deals as (select * from {{ ref("int_kpis__dimension_deals") }}),
    deal_date_per_month as (
        select d.date, dimension_value as id_deal
        from int_kpis__agg_dates_main_kpis d
        where
            d.dimension = 'by_deal'
            and d.dimension_value <> 'UNSET'
            and d.is_end_of_month = true
    ),
    aggregated_metrics_by_deal_over_12_months as (
        select
            d.date,
            d.id_deal,
            deal_lifecycle.deal_lifecycle_state,
            1.0 * count(*) over (
                partition by d.id_deal
                order by d.date
                rows between 12 preceding and 1 preceding
            ) as preceding_months_count_by_deal,
            1.0 * sum(total_and_retained_revenue.total_revenue_in_gbp) over (
                partition by d.id_deal
                order by d.date
                rows between 12 preceding and 1 preceding
            ) as sum_total_revenue_preceding_12_months,
            1.0 * sum(created_bookings.created_bookings) over (
                partition by d.id_deal
                order by d.date
                rows between 12 preceding and 1 preceding
            ) as sum_created_bookings_preceding_12_months,
            1.0 * sum(listings.listings_booked_in_month) over (
                partition by d.id_deal
                order by d.date
                rows between 12 preceding and 1 preceding
            ) as sum_listings_booked_in_month_preceding_12_months
        from deal_date_per_month d
        left join
            int_kpis__lifecycle_daily_deal deal_lifecycle
            on d.date = deal_lifecycle.date
            and d.id_deal = deal_lifecycle.id_deal
        left join
            created_bookings
            on d.date = created_bookings.end_date
            and d.id_deal = created_bookings.dimension_value
        left join
            listings on d.date = listings.date and d.id_deal = listings.dimension_value
        left join
            total_and_retained_revenue
            on d.date = total_and_retained_revenue.end_date
            and d.id_deal = total_and_retained_revenue.dimension_value
    ),
    global_computation_over_12_months as (
        select
            ambd.id_deal,
            ambd.date,
            ambd.deal_lifecycle_state,

            -- 12 months window sum of metric divided by amount of active months per
            -- deal
            ambd.sum_total_revenue_preceding_12_months
            / ambd.preceding_months_count_by_deal
            as avg_total_revenue_preceding_12_months,
            ambd.sum_created_bookings_preceding_12_months
            / ambd.preceding_months_count_by_deal
            as avg_created_bookings_preceding_12_months,
            ambd.sum_listings_booked_in_month_preceding_12_months
            / ambd.preceding_months_count_by_deal
            as avg_listings_booked_in_month_preceding_12_months,

            -- Global amounts by sum of the 12 months window sum of metric on a given
            -- date
            sum(
                ambd.sum_total_revenue_preceding_12_months
                / ambd.preceding_months_count_by_deal
            ) over (partition by ambd.date)
            as avg_global_total_revenue_preceding_12_months,
            sum(
                ambd.sum_created_bookings_preceding_12_months
                / ambd.preceding_months_count_by_deal
            ) over (partition by ambd.date)
            as avg_global_created_bookings_preceding_12_months,
            sum(
                ambd.sum_listings_booked_in_month_preceding_12_months
                / ambd.preceding_months_count_by_deal
            ) over (partition by ambd.date)
            as avg_global_listings_booked_in_month_preceding_12_months

        from aggregated_metrics_by_deal_over_12_months ambd
    ),
    churn_metrics_per_date as (
        {% for dimension in dimensions %}
            select
                m12wc.date,
                {{ dimension.dimension }} as dimension,
                {{ dimension.dimension_value }} as dimension_value,

                -- Revenue Churn 12m rolling window (absolute figures) --
                sum(
                    case
                        when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
                        then coalesce(avg_total_revenue_preceding_12_months, 0)
                        else 0
                    end
                ) as total_revenue_churn_preceding_12_months,

                -- Global Revenue 12m rolling window --
                max(
                    coalesce(avg_global_total_revenue_preceding_12_months, 0)
                ) as total_revenue_global_preceding_12_months,

                -- Churn Rates --
                sum(
                    case
                        when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
                        then
                            coalesce(
                                avg_total_revenue_preceding_12_months / nullif(
                                    avg_global_total_revenue_preceding_12_months, 0
                                ),
                                0
                            )
                        else 0
                    end
                ) as total_revenue_churn_average_contribution,
                sum(
                    case
                        when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
                        then
                            coalesce(
                                avg_created_bookings_preceding_12_months / nullif(
                                    avg_global_created_bookings_preceding_12_months, 0
                                ),
                                0
                            )
                        else 0
                    end
                ) as created_bookings_churn_average_contribution,
                sum(
                    case
                        when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
                        then
                            coalesce(
                                avg_listings_booked_in_month_preceding_12_months
                                / nullif(
                                    avg_global_listings_booked_in_month_preceding_12_months,
                                    0
                                ),
                                0
                            )
                        else 0
                    end
                ) as listings_booked_in_month_churn_average_contribution
            from global_computation_over_12_months m12wc
            {% if dimension.dimension == "'by_number_of_listings'" %}
                inner join
                    int_kpis__dimension_daily_accommodation dda
                    on m12wc.id_deal = dda.id_deal
                    and m12wc.date = dda.date
            {% elif dimension.dimension == "'by_billing_country'" %}
                inner join
                    int_kpis__dimension_deals ud
                    on m12wc.id_deal = ud.id_deal
                    and ud.main_billing_country_iso_3_per_deal is not null
            {% endif %}
            where deal_lifecycle_state is not null
            group by 1, 2, 3
            {% if not loop.last %}
                union all
            {% endif %}
        {% endfor %}
    )
-- Final aggregation of subqueries --
select
    d.year,
    d.month,
    d.day,
    d.date,
    d.dimension,
    d.dimension_value,
    d.is_end_of_month,
    d.is_current_month,
    c.total_revenue_churn_preceding_12_months,
    c.total_revenue_global_preceding_12_months,
    cast(
        c.total_revenue_churn_average_contribution as numeric(19, 6)
    ) as total_revenue_churn_average_contribution,
    cast(
        c.created_bookings_churn_average_contribution as numeric(19, 6)
    ) as created_bookings_churn_average_contribution,
    cast(
        c.listings_booked_in_month_churn_average_contribution as numeric(19, 6)
    ) as listings_booked_in_month_churn_average_contribution
from int_kpis__agg_dates_main_kpis d
left join
    churn_metrics_per_date c
    on c.date = d.date
    and c.dimension = d.dimension
    and c.dimension_value = d.dimension_value
where
    -- Remove current month dates since data won't be available anyway. This is specific
    -- for this churn metrics model
    d.is_current_month = false
    -- Apply only dimensions relevant for Churn Rates
    and d.dimension in ('global', 'by_number_of_listings', 'by_billing_country')
    and d.dimension_value <> 'UNSET'