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Merged PR 4942: KPIs Refactor Stage 4 - Remove old onboarding mrr models

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# Description

Removes old onboarding mrr models, that are no longer used.

dbt compiles correctly and no deprecated warning is showing anymore.

This finishes the refactor

Related work items: #28949
This commit is contained in:
Oriol Roqué Paniagua 2025-04-07 14:30:08 +00:00
parent 34ac83b3ec
commit 17f38b2106
3 changed files with 0 additions and 203 deletions
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72
models/intermediate/cross/int_monthly_onboarding_mrr_per_deal.sql
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@ -1,72 +0,0 @@
with
int_monthly_aggregated_metrics_history_by_deal as (
select
(date_trunc('month', date) + interval '2 month' - interval '1 day')::date
as next_month_end_date,
*
from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
),
int_kpis__dimension_deals as (select * from {{ ref("int_kpis__dimension_deals") }}),
deal_attributes as (
select
id_deal,
coalesce(
main_billing_country_iso_3_per_deal, 'UNSET'
) as main_billing_country_iso_3_per_deal,
effective_deal_start_month,
hubspot_deal_cancellation_month,
coalesce(
hubspot_listing_segmentation, 'UNSET'
) as hubspot_listing_segmentation
from int_kpis__dimension_deals
-- Exclude deals without live dates
where effective_deal_start_date_utc is not null
)
-- Calculate expected MRR per deal by each dimension
select
m.next_month_end_date as date,
'by_number_of_listings' as dimension,
d.hubspot_listing_segmentation as dimension_value,
sum(coalesce(m.total_revenue_in_gbp, 0)) / count(*) as expected_mrr_per_deal
from int_monthly_aggregated_metrics_history_by_deal m
inner join
deal_attributes d
on m.id_deal = d.id_deal
and date_trunc('month', m.date) >= date_trunc('month', d.effective_deal_start_month)
and date_trunc('month', m.date)
<= coalesce(d.hubspot_deal_cancellation_month, '2099-01-01')
and date_trunc('month', m.date)::date <> date_trunc('month', now())::date
where d.hubspot_listing_segmentation <> 'UNSET'
group by 1, 2, 3
union all
select
m.next_month_end_date as date,
'by_billing_country' as dimension,
d.main_billing_country_iso_3_per_deal as dimension_value,
sum(coalesce(m.total_revenue_in_gbp, 0)) / count(*) as expected_mrr_per_deal
from int_monthly_aggregated_metrics_history_by_deal m
inner join
deal_attributes d
on m.id_deal = d.id_deal
and date_trunc('month', m.date) >= date_trunc('month', d.effective_deal_start_month)
and date_trunc('month', m.date)
<= coalesce(d.hubspot_deal_cancellation_month, '2099-01-01')
and date_trunc('month', m.date)::date <> date_trunc('month', now())::date
where d.hubspot_listing_segmentation <> 'UNSET'
group by 1, 2, 3
union all
select
m.next_month_end_date as date,
'global' as dimension,
'global' as dimension_value,
sum(coalesce(m.total_revenue_in_gbp, 0)) / count(*) as expected_mrr_per_deal
from int_monthly_aggregated_metrics_history_by_deal m
inner join
deal_attributes d
on m.id_deal = d.id_deal
and date_trunc('month', m.date) >= date_trunc('month', d.effective_deal_start_month)
and date_trunc('month', m.date)
<= coalesce(d.hubspot_deal_cancellation_month, '2099-01-01')
and date_trunc('month', m.date)::date <> date_trunc('month', now())::date
where d.hubspot_listing_segmentation <> 'UNSET'
group by 1, 2, 3

31
models/intermediate/cross/int_mtd_agg_onboarding_mrr_revenue.sql
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@ -1,31 +0,0 @@
with
int_kpis__agg_daily_deals as (
select date, dimension_value as hubspot_listing_segmentation, new_deals
from {{ ref("int_kpis__agg_daily_deals") }}
where dimension = 'by_number_of_listings'
),
number_of_listing_expected_mrr as (
select
mom.date,
mom.dimension,
mom.dimension_value,
ad.new_deals as number_of_new_deals,
mom.expected_mrr_per_deal * ad.new_deals as expected_mrr
from {{ ref("int_monthly_onboarding_mrr_per_deal") }} mom
left join
int_kpis__agg_daily_deals ad
on mom.date = ad.date
and mom.dimension_value = ad.hubspot_listing_segmentation
where mom.dimension = 'by_number_of_listings'
)
select *
from number_of_listing_expected_mrr
union all
select
date,
'global' as dimension,
'global' as dimension_value,
sum(number_of_new_deals) as number_of_new_deals,
sum(expected_mrr) as expected_mrr
from number_of_listing_expected_mrr
group by date

100
models/intermediate/cross/schema.yml
View file

@ -1694,106 +1694,6 @@ models:
data_type: boolean
description: "Flag to indicate if the deal is in Xero."
- name: int_monthly_onboarding_mrr_per_deal
deprecation_date: 2025-04-09
description: |
"This table provides data on the Onboarding Monthly Recurring Revenue (MRR).
The Onboarding MRR is an estimate of the expected monthly revenue generated by
each new deal. It is calculated by taking the total revenue generated by all
active accounts over the last 12 previous months (before the ongoing month)
and dividing it by the number of active months for each account during this
period.
For example in December 2023 we will calculate the Onboarding MRR for a deal
using the revenue from December 2022 to November 2023."
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: expected_mrr_per_deal
data_type: numeric
description: |
"Expected MRR for each new deal."
data_tests:
- not_null
- name: int_mtd_agg_onboarding_mrr_revenue
deprecation_date: 2025-04-09
description: |
This model contains the month-to-date aggregated metrics for onboarding MRR.
It includes the total expected MRR revenue for the month, aggregated by
dimension for 'global' and 'by_number_of_listings' only.
- The 'by_number_of_listings' dimension is calculated by multiplying the
expected MRR per deal by the number of new deals in that segment.
- The 'global' dimension represents the sum of all expected MRRs across
all segments.
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
- name: dimension_value
data_type: string
description: The value or segment available for the selected dimension.
data_tests:
- not_null
- name: number_of_new_deals
data_type: numeric
description: Number of new deals in the month.
- name: expected_mrr
data_type: numeric
description: |
Total expected Onboarding MRR.
This is calculated by multiplying the expected MRR per deal by the number of new deals.
For the "global" dimension, it is the sum of all expected MRRs across segments.
- name: int_ytd_mtd_main_metrics_overview
description: |
This model provides a high-level overview of the main metrics for the month-to-date