Merged PR 4942: KPIs Refactor Stage 4 - Remove old onboarding mrr models
# 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
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Oriol Roqué Paniagua
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commit
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3 changed files with 0 additions and 203 deletions
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with
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int_monthly_aggregated_metrics_history_by_deal as (
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select
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(date_trunc('month', date) + interval '2 month' - interval '1 day')::date
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as next_month_end_date,
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*
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from {{ ref("int_monthly_aggregated_metrics_history_by_deal") }}
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),
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int_kpis__dimension_deals as (select * from {{ ref("int_kpis__dimension_deals") }}),
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deal_attributes as (
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select
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id_deal,
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coalesce(
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main_billing_country_iso_3_per_deal, 'UNSET'
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) as main_billing_country_iso_3_per_deal,
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effective_deal_start_month,
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hubspot_deal_cancellation_month,
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coalesce(
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hubspot_listing_segmentation, 'UNSET'
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) as hubspot_listing_segmentation
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from int_kpis__dimension_deals
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-- Exclude deals without live dates
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where effective_deal_start_date_utc is not null
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)
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-- Calculate expected MRR per deal by each dimension
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select
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m.next_month_end_date as date,
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'by_number_of_listings' as dimension,
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d.hubspot_listing_segmentation as dimension_value,
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sum(coalesce(m.total_revenue_in_gbp, 0)) / count(*) as expected_mrr_per_deal
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from int_monthly_aggregated_metrics_history_by_deal m
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inner join
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deal_attributes d
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on m.id_deal = d.id_deal
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and date_trunc('month', m.date) >= date_trunc('month', d.effective_deal_start_month)
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and date_trunc('month', m.date)
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<= coalesce(d.hubspot_deal_cancellation_month, '2099-01-01')
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and date_trunc('month', m.date)::date <> date_trunc('month', now())::date
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where d.hubspot_listing_segmentation <> 'UNSET'
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group by 1, 2, 3
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union all
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select
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m.next_month_end_date as date,
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'by_billing_country' as dimension,
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d.main_billing_country_iso_3_per_deal as dimension_value,
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sum(coalesce(m.total_revenue_in_gbp, 0)) / count(*) as expected_mrr_per_deal
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from int_monthly_aggregated_metrics_history_by_deal m
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inner join
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deal_attributes d
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on m.id_deal = d.id_deal
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and date_trunc('month', m.date) >= date_trunc('month', d.effective_deal_start_month)
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and date_trunc('month', m.date)
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<= coalesce(d.hubspot_deal_cancellation_month, '2099-01-01')
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and date_trunc('month', m.date)::date <> date_trunc('month', now())::date
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where d.hubspot_listing_segmentation <> 'UNSET'
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group by 1, 2, 3
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union all
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select
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m.next_month_end_date as date,
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'global' as dimension,
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'global' as dimension_value,
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sum(coalesce(m.total_revenue_in_gbp, 0)) / count(*) as expected_mrr_per_deal
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from int_monthly_aggregated_metrics_history_by_deal m
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inner join
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deal_attributes d
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on m.id_deal = d.id_deal
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and date_trunc('month', m.date) >= date_trunc('month', d.effective_deal_start_month)
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and date_trunc('month', m.date)
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<= coalesce(d.hubspot_deal_cancellation_month, '2099-01-01')
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and date_trunc('month', m.date)::date <> date_trunc('month', now())::date
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where d.hubspot_listing_segmentation <> 'UNSET'
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group by 1, 2, 3
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@ -1,31 +0,0 @@
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with
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int_kpis__agg_daily_deals as (
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select date, dimension_value as hubspot_listing_segmentation, new_deals
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from {{ ref("int_kpis__agg_daily_deals") }}
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where dimension = 'by_number_of_listings'
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),
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number_of_listing_expected_mrr as (
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select
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mom.date,
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mom.dimension,
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mom.dimension_value,
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ad.new_deals as number_of_new_deals,
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mom.expected_mrr_per_deal * ad.new_deals as expected_mrr
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from {{ ref("int_monthly_onboarding_mrr_per_deal") }} mom
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left join
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int_kpis__agg_daily_deals ad
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on mom.date = ad.date
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and mom.dimension_value = ad.hubspot_listing_segmentation
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where mom.dimension = 'by_number_of_listings'
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)
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select *
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from number_of_listing_expected_mrr
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union all
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select
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date,
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'global' as dimension,
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'global' as dimension_value,
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sum(number_of_new_deals) as number_of_new_deals,
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sum(expected_mrr) as expected_mrr
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from number_of_listing_expected_mrr
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group by date
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@ -1694,106 +1694,6 @@ models:
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data_type: boolean
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data_type: boolean
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description: "Flag to indicate if the deal is in Xero."
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description: "Flag to indicate if the deal is in Xero."
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- name: int_monthly_onboarding_mrr_per_deal
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deprecation_date: 2025-04-09
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description: |
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"This table provides data on the Onboarding Monthly Recurring Revenue (MRR).
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The Onboarding MRR is an estimate of the expected monthly revenue generated by
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each new deal. It is calculated by taking the total revenue generated by all
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active accounts over the last 12 previous months (before the ongoing month)
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and dividing it by the number of active months for each account during this
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period.
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For example in December 2023 we will calculate the Onboarding MRR for a deal
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using the revenue from December 2022 to November 2023."
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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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- dimension
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- dimension_value
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columns:
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- name: date
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data_type: date
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description: The date for the month-to-date metrics.
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data_tests:
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- not_null
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- name: dimension
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data_type: string
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description: The dimension or granularity of the metrics.
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data_tests:
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- accepted_values:
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values:
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- global
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- by_number_of_listings
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- by_billing_country
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- name: dimension_value
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data_type: string
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description: The value or segment available for the selected dimension.
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data_tests:
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- not_null
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- name: expected_mrr_per_deal
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data_type: numeric
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description: |
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"Expected MRR for each new deal."
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data_tests:
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- not_null
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- name: int_mtd_agg_onboarding_mrr_revenue
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deprecation_date: 2025-04-09
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description: |
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This model contains the month-to-date aggregated metrics for onboarding MRR.
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It includes the total expected MRR revenue for the month, aggregated by
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dimension for 'global' and 'by_number_of_listings' only.
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- The 'by_number_of_listings' dimension is calculated by multiplying the
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expected MRR per deal by the number of new deals in that segment.
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- The 'global' dimension represents the sum of all expected MRRs across
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all segments.
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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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- dimension
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- dimension_value
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columns:
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- name: date
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data_type: date
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description: The date for the month-to-date metrics.
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data_tests:
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- not_null
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- name: dimension
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data_type: string
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description: The dimension or granularity of the metrics.
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data_tests:
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- accepted_values:
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values:
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- global
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- by_number_of_listings
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- name: dimension_value
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data_type: string
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description: The value or segment available for the selected dimension.
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data_tests:
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- not_null
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- name: number_of_new_deals
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data_type: numeric
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description: Number of new deals in the month.
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- name: expected_mrr
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data_type: numeric
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description: |
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Total expected Onboarding MRR.
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This is calculated by multiplying the expected MRR per deal by the number of new deals.
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For the "global" dimension, it is the sum of all expected MRRs across segments.
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- name: int_ytd_mtd_main_metrics_overview
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- name: int_ytd_mtd_main_metrics_overview
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description: |
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description: |
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This model provides a high-level overview of the main metrics for the month-to-date
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This model provides a high-level overview of the main metrics for the month-to-date
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