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Merged PR 4931: KPIs Refactor Stage 3 - Remove previous Churn models

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# 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 2025-04-07 06:41:07 +00:00
parent 4b9babf6b4
commit 63aebf4220
3 changed files with 0 additions and 427 deletions
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112
models/intermediate/cross/int_monthly_12m_window_contribution_by_deal.sql
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@ -1,112 +0,0 @@
{{ config(materialized="table", unique_key=["date", "id_deal"]) }}
with
int_monthly_aggregated_metrics_history_by_deal as (
select * from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
),
aggregated_metrics_by_deal_over_12_months as (
select
am.date,
am.id_deal,
am.deal_lifecycle_state,
1.0 * count(*) over (
partition by am.id_deal
order by am.date
rows between 12 preceding and 1 preceding
) as preceding_months_count_by_deal,
1.0 * sum(am.total_revenue_in_gbp) over (
partition by am.id_deal
order by am.date
rows between 12 preceding and 1 preceding
) as sum_total_revenue_preceding_12_months,
1.0 * sum(am.created_bookings) over (
partition by am.id_deal
order by am.date
rows between 12 preceding and 1 preceding
) as sum_created_bookings_preceding_12_months,
1.0 * sum(am.listings_booked_in_month) over (
partition by am.id_deal
order by am.date
rows between 12 preceding and 1 preceding
) as sum_listings_booked_in_month_preceding_12_months
from int_monthly_aggregated_metrics_history_by_deal am
),
global_computation_over_12_months as (
select
ambd.id_deal,
ambd.date,
ambd.deal_lifecycle_state,
-- Amount of active months per deal used in the computation
ambd.preceding_months_count_by_deal,
case
when ambd.preceding_months_count_by_deal < 12 then true else false
end as has_deal_been_created_less_than_12_months_ago,
-- AVERAGE APPROACH --
-- 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
)
select
id_deal,
date,
deal_lifecycle_state,
-- Amount of active months per deal used in the computation
preceding_months_count_by_deal,
-- Revenue pre-computation metrics for further aggregation downstream
coalesce(
avg_total_revenue_preceding_12_months, 0
) as avg_total_revenue_preceding_12_months,
coalesce(
avg_global_total_revenue_preceding_12_months, 0
) as avg_global_total_revenue_preceding_12_months,
-- AVERAGE APPROACH --
-- 12 months window sum of metric divided by amount of active months per deal
coalesce(
avg_total_revenue_preceding_12_months
/ nullif(avg_global_total_revenue_preceding_12_months, 0),
0
) as total_revenue_12m_average_contribution,
coalesce(
avg_created_bookings_preceding_12_months
/ nullif(avg_global_created_bookings_preceding_12_months, 0),
0
) as created_bookings_12m_average_contribution,
coalesce(
avg_listings_booked_in_month_preceding_12_months
/ nullif(avg_global_listings_booked_in_month_preceding_12_months, 0),
0
) as listings_booked_in_month_12m_average_contribution
from global_computation_over_12_months

111
models/intermediate/cross/int_monthly_churn_metrics.sql
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@ -1,111 +0,0 @@
{% 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_monthly_12m_window_contribution_by_deal as (
select * from {{ ref("int_monthly_12m_window_contribution_by_deal") }}
),
int_kpis__agg_dates_main_kpis as (
select *
from {{ ref("int_kpis__agg_dates_main_kpis") }}
where
dimension in ('global', 'by_number_of_listings', 'by_billing_country')
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") }}),
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 m12wc.avg_total_revenue_preceding_12_months
else 0
end
) as total_revenue_churn_preceding_12_months,
-- Global Revenue 12m rolling window --
max(
m12wc.avg_global_total_revenue_preceding_12_months
) as total_revenue_global_preceding_12_months,
-- Churn Rates --
sum(
case
when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
then m12wc.total_revenue_12m_average_contribution
else 0
end
) as total_revenue_churn_average_contribution,
sum(
case
when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
then m12wc.created_bookings_12m_average_contribution
else 0
end
) as created_bookings_churn_average_contribution,
sum(
case
when m12wc.deal_lifecycle_state in {{ churn_lifecycle_states }}
then m12wc.listings_booked_in_month_12m_average_contribution
else 0
end
) as listings_booked_in_month_churn_average_contribution
from int_monthly_12m_window_contribution_by_deal 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
-- Remove current month dates since data won't be available anyway. This is specific
-- for this churn metrics model
where d.is_current_month = false

204
models/intermediate/cross/schema.yml
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@ -1106,210 +1106,6 @@ models:
- MAJOR GAIN
- UNSET
- name: int_monthly_12m_window_contribution_by_deal
deprecation_date: 2025-04-08
description: |
The main goal of this model is to provide how much a deal
contributes to a given metric on the global amount over a
period of 12 months.
At the moment, this is only done for 3 metrics:
- total_revenue_in_gbp
- created_bookings
- listings_booked_in_month
The contribution is based on an Average approach:
Over a period of 12 months, sum the value of a given a metric
for each deal, and divide it by the amount of months we're considering
for that deal. Sum all the average amounts per deals to get a global.
Divide the avg per deal value vs. the sum of avgs global one.
The average approach "boosts" the contribution of those accounts
that have been active for less than 12 months.
data_tests:
- dbt_utils.unique_combination_of_columns:
combination_of_columns:
- date
- id_deal
columns:
- name: date
data_type: date
description: |
Date corresponding to the last day of the month.
Metrics are inclusive to this date. Together with id_deal, it
acts as the primary key of this model.
data_tests:
- not_null
- name: id_deal
data_type: string
description: |
Unique identifier of a Deal. Together with date, it acts as
the primary key of this model.
data_tests:
- not_null
- name: deal_lifecycle_state
data_type: string
description: |
Identifier of the lifecycle state of a given deal
in a given month.
- name: preceding_months_count_by_deal
data_type: integer
description: |
Number of months preceding to the one given by date
that are used for the historic metric retrieval for
a given deal. In essence it states the amount of
months a given deal has been active before a the month
given by date, capped at 12 months.
data_tests:
- dbt_expectations.expect_column_values_to_be_between:
min_value: 0
max_value: 12
strictly: false
- name: has_deal_been_created_less_than_12_months_ago
data_type: boolean
description: |
Flag to identify if a given deal has been created less
than 12 months ago (true) or not (false). It's based on the
preceding_months_count_by_deal, and will be true on the first
year of deal activity.
- name: total_revenue_12m_average_contribution
data_type: numeric
description: |
Share of the deal contribution on total revenue
vs. the global amount, on the preceding 12 months
with respect to date. It uses the average approach.
It can be negative.
data_tests:
- not_null
- name: created_bookings_12m_average_contribution
data_type: numeric
description: |
Share of the deal contribution on created bookings
vs. the global amount, on the preceding 12 months
with respect to date. It uses the average approach.
data_tests:
- not_null
- dbt_expectations.expect_column_values_to_be_between:
min_value: 0
max_value: 1
strictly: false
- name: listings_booked_in_month_12m_average_contribution
data_type: numeric
description: |
Share of the deal contribution on listings booked in month
vs. the global amount, on the preceding 12 months
with respect to date. It uses the average approach.
data_tests:
- not_null
- dbt_expectations.expect_column_values_to_be_between:
min_value: 0
max_value: 1
strictly: false
- name: avg_total_revenue_preceding_12_months
data_type: numeric
description: |
Total revenue in GBP generated by a single deal
in the 12 months period. This uses an average approach,
meaning that the revenue of that deal is divided by the
amount of months it has been active.
- name: avg_global_total_revenue_preceding_12_months
data_type: numeric
description: |
Total revenue in GBP generated by a all deals
in the 12 months period. This uses an average approach,
meaning that the revenue of each deal is divided by the
amount of months it has been active.
- name: int_monthly_churn_metrics
deprecation_date: 2025-04-08
description: |
This model is used for global KPIs.
It computes the churn contribution by dimension, dimension value
and date, in a monthly basis. This model is different from the
usual mtd ones since it strictly depends on the monthly computation
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
description:
"This table holds records on verifications for Guesty and Edeposit bookings.