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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
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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.