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Merged PR 3451: Adds Deal Daily Lifecycle and metrics

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# Description

Changes:
* Creates lifecycle_daily_deal, metric_daily_deals and agg_daily_deals. These follow a different strategy due to the nature of the metrics
* Modifies the dimension macro to ensure deal dimension is included in all models except these ones
* Fixes production issue on currently deployed deal lifecycle and metrics

# Checklist

- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [X] I have checked for DRY opportunities with other models and docs.
- [X] I've picked the right materialization for the affected models.

# Other

- [ ] Check if a full-refresh is required after this PR is merged.

Related work items: #23566
This commit is contained in:
Oriol Roqué Paniagua 2024-11-07 10:49:06 +00:00
parent 9ef9a57c03
commit 8c23f91242
7 changed files with 821 additions and 6 deletions
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13
macros/business_kpis_configuration.sql
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@ -102,20 +102,27 @@ Provides a general assignemnt for the Dimensions available for each KPI
dim_global(),
dim_number_of_listings(),
dim_billing_country(),
dim_deal(),
] %}
{# Initialize a list to hold any model-specific dimensions #}
{% set additional_dimensions = [] %}
{# Adds Deal dimension to all models except DEAL metrics #}
{% if entity_name != "DEALS" %}
{% set additional_dimensions = additional_dimensions + [dim_deal()] %}
{% endif %}
{# Add entity-specific dimensions #}
{% if entity_name == "CREATED_BOOKINGS" %}
{% set additional_dimensions = [dim_dash()] %}
{% set additional_dimensions = additional_dimensions + [dim_dash()] %}
{% endif %}
{# Add entity-specific dimensions #}
{% if entity_name == "CHECK_IN_ATTRIBUTED_GUEST_JOURNEYS" %}
{% set additional_dimensions = [dim_has_payment(), dim_has_id_check()] %}
{% set additional_dimensions = additional_dimensions + [
dim_has_payment(),
dim_has_id_check(),
] %}
{% endif %}
{# Combine base dimensions with additional dimensions for the specific model #}

6
models/intermediate/cross/int_mtd_deal_lifecycle.sql
View file

@ -6,7 +6,7 @@ Assumes a host will have a deal being filled.
{{ config(materialized="table", unique_key=["date", "id_deal"]) }}
with
int_core__bookings as (select * from {{ ref("int_core__bookings") }}),
int_core__unified_user as (select * from {{ ref("int_core__unified_user") }}),
int_core__user_host as (select * from {{ ref("int_core__user_host") }}),
int_dates_mtd as (select * from {{ ref("int_dates_mtd") }}),
int_hubspot__deal as (select * from {{ ref("int_hubspot__deal") }}),
@ -44,10 +44,10 @@ with
max(b.created_date_utc) as last_time_booked_date_utc,
max(b.previous_booked_date) as second_to_last_time_booked_date_utc
from int_dates_mtd d
inner join int_core__unified_user h on d.date >= h.created_date_utc
inner join int_core__user_host h on d.date >= date(h.created_date_utc)
left join
booked_days_per_host b
on h.id_user = b.id_user_host
on h.id_user_host = b.id_user_host
and d.date >= b.created_date_utc
where h.id_deal is not null
group by d.date, h.id_deal

33
models/intermediate/kpis/int_kpis__agg_daily_deals.sql Normal file
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@ -0,0 +1,33 @@
{% set dimensions = get_kpi_dimensions_per_model("DEALS") %}
{{ config(materialized="table", unique_key=["date", "dimension", "dimension_value"]) }}
{% for dimension in dimensions %}
select
-- Unique Key --
d.date,
{{ dimension.dimension }} as dimension,
{{ dimension.dimension_value }} as dimension_value,
-- Date Attributes --
d.is_current_month,
d.is_end_of_month,
d.is_month_to_date,
-- Metrics --
sum(new_deals) as new_deals,
sum(never_booked_deals) as never_booked_deals,
sum(first_time_booked_deals) as first_time_booked_deals,
sum(active_deals) as active_deals,
sum(churning_deals) as churning_deals,
sum(inactive_deals) as inactive_deals,
sum(reactivated_deals) as reactivated_deals,
sum(deals_booked_in_month) as deals_booked_in_month,
sum(deals_booked_in_6_months) as deals_booked_in_6_months,
sum(deals_booked_in_12_months) as deals_booked_in_12_months
from {{ ref("int_kpis__dimension_dates") }} d
left join {{ ref("int_kpis__metric_daily_deals") }} as mdd on d.date = mdd.date
group by 1, 2, 3, 4, 5, 6
{% if not loop.last %}
union all
{% endif %}
{% endfor %}

219
models/intermediate/kpis/int_kpis__lifecycle_daily_deal.sql Normal file
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@ -0,0 +1,219 @@
/*
This model provides Month-To-Date (MTD) deal lifecycle based on booking metrics.
Assumes a host will have a deal being filled.
*/
{{ config(materialized="table", unique_key=["date", "id_deal"]) }}
with
int_core__bookings as (select * from {{ ref("int_core__bookings") }}),
int_core__user_host as (select * from {{ ref("int_core__user_host") }}),
int_kpis__dimension_dates as (select * from {{ ref("int_kpis__dimension_dates") }}),
int_hubspot__deal as (select * from {{ ref("int_hubspot__deal") }}),
hubspot_deal_offboardings as (
-- At the moment it's not possible to account for deal reactivation within
-- hubspot, meaning, once a deal has a cancellation date we'll be considering
-- it as churned. However, a deal can be reactivated and if so, the
-- cancellation date might be set up to null, thus loosing the previous
-- history of offboarding and affecting backwards history. Lastly,
-- cancellation date not being null is preferred vs. the hubspot deal stage
-- since an actual churned deal might be in a process of reactivation,
-- discussing with our colleagues from sales / am teams. In this discussion
-- period, the stage could be different than cancelled, but still not
-- reactivated.
select id_deal, cancellation_date_utc
from int_hubspot__deal
where cancellation_date_utc is not null
),
booked_days_per_deal as (
select
icuh.id_deal,
icb.created_date_utc,
lag(icb.created_date_utc, 1) over (
partition by icuh.id_deal order by icb.created_date_utc asc
) as previous_booked_date
from int_core__bookings icb
inner join int_core__user_host icuh on icb.id_user_host = icuh.id_user_host
where icuh.id_deal is not null
group by icuh.id_deal, icb.created_date_utc
),
deal_historic_booking_dates as (
select
d.date,
h.id_deal,
min(date(h.created_date_utc)) as creation_date_utc,
min(b.created_date_utc) as first_time_booked_date_utc,
max(b.created_date_utc) as last_time_booked_date_utc,
max(b.previous_booked_date) as second_to_last_time_booked_date_utc
from int_kpis__dimension_dates d
inner join int_core__user_host h on d.date >= date(h.created_date_utc)
left join
booked_days_per_deal b
on h.id_deal = b.id_deal
and d.date >= b.created_date_utc
where h.id_deal is not null
group by d.date, h.id_deal
),
deal_historic_features as (
select
hhbf.date,
hhbf.id_deal,
hhbf.creation_date_utc,
hhbf.first_time_booked_date_utc,
hhbf.last_time_booked_date_utc,
hhbf.second_to_last_time_booked_date_utc,
hdo.cancellation_date_utc,
case
when hhbf.date >= hdo.cancellation_date_utc then true else false
end as deal_has_been_offboarded,
case
when
date_trunc('month', hdo.cancellation_date_utc)
= date_trunc('month', hhbf.date)
then true
else false
end as deal_was_offboarded_this_month,
case
when hhbf.first_time_booked_date_utc is not null then true else false
end as deal_has_at_least_one_booking,
case
when
date_trunc('month', hhbf.creation_date_utc)
= date_trunc('month', hhbf.date)
then true
else false
end as deal_was_created_this_month,
case
when
date_trunc('month', hhbf.first_time_booked_date_utc)
= date_trunc('month', hhbf.last_time_booked_date_utc)
then true
else false
end as deal_has_first_booking,
case
when
date_trunc('month', hhbf.last_time_booked_date_utc)
= date_trunc('month', hhbf.date)
then true
else false
end as has_been_booked_within_current_month,
case
when
date_trunc('month', hhbf.last_time_booked_date_utc)
+ interval '6 months'
> date_trunc('month', hhbf.date)
then true
else false
end as has_been_booked_within_last_6_months,
case
when
date_trunc('month', hhbf.last_time_booked_date_utc)
+ interval '12 months'
> date_trunc('month', hhbf.date)
then true
else false
end as has_been_booked_within_last_12_months,
case
when
date_trunc('month', hhbf.last_time_booked_date_utc)
+ interval '12 months'
= date_trunc('month', hhbf.date)
then true
else false
end as last_booking_was_12_months_ago,
case
when
date_trunc('month', hhbf.second_to_last_time_booked_date_utc)
+ interval '12 months'
< date_trunc('month', hhbf.last_time_booked_date_utc)
then true
else false
end as had_previous_booking_more_than_12_months_before_the_last
from deal_historic_booking_dates hhbf
left join hubspot_deal_offboardings hdo on hhbf.id_deal = hdo.id_deal
)
select
date,
id_deal,
creation_date_utc,
first_time_booked_date_utc,
last_time_booked_date_utc,
second_to_last_time_booked_date_utc,
cancellation_date_utc,
case
-- 01-New: The deal has been created this month and has not had any booking.
-- Additionally, the deal has not been offboarded in hubspot.
when
not deal_has_at_least_one_booking
and deal_was_created_this_month
and not deal_has_been_offboarded
then '01-New'
-- 02-Never Booked: The deal has been created before this month and has not
-- had any booking. Additionally, the deal has not been offboarded in hubspot.
when
not deal_has_at_least_one_booking
and not deal_was_created_this_month
and not deal_has_been_offboarded
then '02-Never Booked'
-- 03-First Time Booked: The deal has been booked for the first time and it
-- has been created this month. Additionally, the deal has not been offboarded
-- in hubspot.
when
deal_has_first_booking
and has_been_booked_within_current_month
and not deal_has_been_offboarded
then '03-First Time Booked'
-- 04-Active: The deal has had at least 1 booking in its history and it's
-- been less than 12 months since the last booking and has not been offboarded
-- in hubspot and is not reactivated and is not FTB
when
deal_has_at_least_one_booking
and has_been_booked_within_last_12_months
and not deal_has_been_offboarded
-- not reactivated
and not (
had_previous_booking_more_than_12_months_before_the_last
and has_been_booked_within_current_month
)
-- not FTB
and not (deal_has_first_booking and has_been_booked_within_current_month)
then '04-Active'
-- 05-Churning: The deal has been offboarded this month. Alternatively, The
-- deal has been booked at least once and it's been 12 months since the last
-- booking
when
(
deal_has_at_least_one_booking
and last_booking_was_12_months_ago
and not deal_has_been_offboarded
)
or deal_was_offboarded_this_month
then '05-Churning'
-- 06-Inactive: The deal has been offboarded in the past but not this month.
-- Alternatively, the deal is not offboarded and the deal has been booked at
-- least once and it's been more than 12 months since the last booking.
when
(
deal_has_at_least_one_booking
and not has_been_booked_within_last_12_months
and not last_booking_was_12_months_ago
and not deal_has_been_offboarded
)
or (deal_has_been_offboarded and not deal_was_offboarded_this_month)
then '06-Inactive'
-- 07-Reactivated: The deal is not offboarded but was churned/inactive, and
-- now has had a new booking this month
when
had_previous_booking_more_than_12_months_before_the_last
and has_been_booked_within_current_month
and not deal_has_been_offboarded
then '07-Reactivated'
else null
end as deal_lifecycle_state,
has_been_booked_within_current_month,
has_been_booked_within_last_6_months,
has_been_booked_within_last_12_months,
deal_has_been_offboarded as has_been_offboarded
from deal_historic_features

73
models/intermediate/kpis/int_kpis__metric_daily_deals.sql Normal file
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@ -0,0 +1,73 @@
{{
config(
materialized="table",
unique_key=[
"date",
"main_billing_country_iso_3_per_deal",
"active_accommodations_per_deal_segmentation",
],
)
}}
select
-- Unique Key --
ldl.date,
-- Dimensions --
coalesce(
icd.main_billing_country_iso_3_per_deal, 'UNSET'
) as main_billing_country_iso_3_per_deal,
coalesce(
icmas.active_accommodations_per_deal_segmentation, 'UNSET'
) as active_accommodations_per_deal_segmentation,
count(
distinct case
when ldl.deal_lifecycle_state = '01-New' then ldl.id_deal else null
end
) as new_deals,
count(
distinct case
when ldl.deal_lifecycle_state = '02-Never Booked' then ldl.id_deal else null
end
) as never_booked_deals,
count(
distinct case
when ldl.deal_lifecycle_state = '03-First Time Booked'
then ldl.id_deal
else null
end
) as first_time_booked_deals,
count(
distinct case
when ldl.deal_lifecycle_state = '04-Active' then ldl.id_deal else null
end
) as active_deals,
count(
distinct case
when ldl.deal_lifecycle_state = '05-Churning' then ldl.id_deal else null
end
) as churning_deals,
count(
distinct case
when ldl.deal_lifecycle_state = '06-Inactive' then ldl.id_deal else null
end
) as inactive_deals,
count(
distinct case
when ldl.deal_lifecycle_state = '07-Reactivated' then ldl.id_deal else null
end
) as reactivated_deals,
sum(
case when has_been_booked_within_current_month then 1 else 0 end
) as deals_booked_in_month,
sum(
case when has_been_booked_within_last_6_months then 1 else 0 end
) as deals_booked_in_6_months,
sum(
case when has_been_booked_within_last_12_months then 1 else 0 end
) as deals_booked_in_12_months
from {{ ref("int_kpis__lifecycle_daily_deal") }} as ldl
left join {{ ref("int_core__deal") }} as icd on ldl.id_deal = icd.id_deal
left join
{{ ref("int_kpis__dimension_daily_accommodation") }} as icmas
on ldl.id_deal = icmas.id_deal
and ldl.date = icmas.date
group by 1, 2, 3

363
models/intermediate/kpis/schema.yml
View file

@ -203,6 +203,129 @@ models:
data_type: boolean
description: If the listing has had a booking created in the past 12 months.
- name: int_kpis__lifecycle_daily_deal
description: |
This model computes the daily lifecycle of accounts, at deal level.
The information regarding the booking-related time allows for the current status of any
deal regarding its activity. This information is encapsulated in the following columns:
deal_lifecycle_state: contains one of the following states
- 01-New: Deals that have been created in the current month, without bookings, that are not offboarded.
- 02-Never Booked: Deals that have been created before the current month, without bookings, that are not offboarded.
- 03-First Time Booked: Deals that have been booked for the first time in the current month, that are not offboarded.
- 04-Active: Deals that have booking activity in the past 12 months (that are not FTB nor reactivated), that are not offboarded.
- 05-Churning: Either Deals that are offboarded in that month or Deals that are becoming inactive because of lack of bookings in the past 12 months
- 06-Inactive: Either Deals that have been previously offboarded or Deals that have not had a booking for more than 12 months.
- 07-Reactivated: Deals that have had a booking in the current month that were inactive or churning before, that are not offboarded.
- Finally, if none of the logic applies, which should not happen, null will be set and a dbt alert will raise.
Since the states of Active, First Time Booked and Reactivated indicate certain booking activity and are
mutually exclusive, the model also provides information of the recency of the bookings by the following
booleans:
- has_been_booked_within_current_month: If a deal has had a booking created in the current month
- has_been_booked_within_last_6_months: If a deal has had a booking created in the past 6 months
- has_been_booked_within_last_12_months: If a deal has had a booking created in the past 12 months
Note that if a deal has had a booking created in a given month, all 3 columns will be true. Similarly,
if the last booking created to a deal was 5 months ago, only the column has_been_booked_in_1_month
will be false; while the other 2 will be true.
Some final considerations:
- It's possible but not common that a Deal gets offboarded on the same month that has had some bookings created.
- It shouldn't happen that a Deal that is Inactive has some bookings created. However, there's few cases in which
this happens likely because of misconfiguration between Hubspot and Core. This should be reported to increase
data quality.
tests:
- dbt_utils.unique_combination_of_columns:
combination_of_columns:
- date
- id_deal
columns:
- name: date
data_type: date
description: Date in which a Deal has a given lifecycle state.
tests:
- not_null
- name: id_deal
data_type: character varying
description: Unique identifier of the Account.
tests:
- not_null
- name: creation_date_utc
data_type: date
description: Date of when the first host associated to that deal was created.
- name: first_time_booked_date_utc
data_type: date
description: |
Date of the first booking created for a given deal. Can be null if the deal
has never had a booking associated with it.
- name: last_time_booked_date_utc
data_type: date
description: |
Date of the last booking created for a given deal. Can be null if the deal
has never had a booking associated with it. Can be the same as first_time_booked_date_utc
if the deal only had 1 booking in its history.
- name: second_to_last_time_booked_date_utc
data_type: date
description: |
Date of the second-to-last booking created for a given deal, meaning the creation
date of the booking that precedes the last one. It's relevant for the reactivation computation
on the lifecycle. Can be null if the deal has never had a booking associated with it or if
the deal only had 1 booking in its history.
- name: cancellation_date_utc
data_type: date
description: |
Date of when the deal was cancelled, according to Hubspot. This is the date we're considering
for hard offboarding. It can be null, meaning the account has not been offboarded.
- name: deal_lifecycle_state
data_type: character varying
description: |
Contains the lifecycle state of a deal. The accepted values are:
01-New, 02-Never Booked, 03-First Time Booked, 04-Active, 05-Churning, 06-Inactive,
07-Reactivated. Failing to implement the logic will result in alert.
tests:
- not_null
- accepted_values:
values:
- 01-New
- 02-Never Booked
- 03-First Time Booked
- 04-Active
- 05-Churning
- 06-Inactive
- 07-Reactivated
- name: has_been_booked_within_current_month
data_type: boolean
description: |
If the deal has had a booking already created in the current month.
Note that if the Booking is created on the 5th day, this column will
be false for the days 1st to 4th, and true from the day 5th onwards.
- name: has_been_booked_within_last_6_months
data_type: boolean
description: |
If the deal has had a booking created in the past 6 months.
- name: has_been_booked_within_last_12_months
data_type: boolean
description: |
If the deal has had a booking created in the past 12 months.
- name: has_been_offboarded
data_type: boolean
description: |
If the deal has been cancelled or not. Note that if the Deal
has been offboarded on the 5th day, this column will be false
for the days 1st to 4th, and true from the day 5th onwards.
- name: int_kpis__dimension_daily_accommodation
description: |
This model computes the deal segmentation per number of
@ -4957,3 +5080,243 @@ models:
description: |
The month-to-date Waiver Amount Paid Back to Hosts, in GBP,
without taxes for a given date, dimension and value.
- name: int_kpis__metric_daily_deals
description: |
This model computes the Daily Deal metrics at the deepest granularity.
Be aware that this Deal entity will differ from how the rest of models
usually operate. This is because we compute Deal metrics, thus it does
not make sense to compute these at Deal level.
Also, Deal metrics at daily level already contain the time dimension
aggregates needed, thus we won't have mtd or monthly equivalent models,
but rather just select from this daily model the needed days to recover
the necessary information.
The unique key corresponds to the deepest granularity of the model,
in this case:
- date,
- main_billing_country_iso_3_per_deal,
- active_accommodations_per_deal_segmentation
tests:
- dbt_utils.unique_combination_of_columns:
combination_of_columns:
- date
- main_billing_country_iso_3_per_deal
- active_accommodations_per_deal_segmentation
columns:
- name: date
data_type: date
description: Date containing the Deal metrics.
tests:
- not_null
- name: active_accommodations_per_deal_segmentation
data_type: string
description: |
Segment value based on the number of listings booked in 12 months
for a given deal and date.
tests:
- not_null
- accepted_values:
values:
- "0"
- "01-05"
- "06-20"
- "21-60"
- "61+"
- "UNSET"
- name: main_billing_country_iso_3_per_deal
data_type: string
description: |
Main billing country of the host aggregated at Deal level.
tests:
- not_null
- name: new_deals
data_type: bigint
description: |
Count of new deals in a given date and per specified dimension.
- name: never_booked_deals
data_type: bigint
description: |
Count of never booked deals in a given date and per specified dimension.
- name: first_time_booked_deals
data_type: bigint
description: |
Count of first-time booked deals in a given date and per specified dimension.
- name: active_deals
data_type: bigint
description: |
Count of active deals in a given date and per specified dimension.
- name: inactive_deals
data_type: bigint
description: |
Count of inactive deals in a given date and per specified dimension.
- name: churning_deals
data_type: bigint
description: |
Count of churning deals in a given date and per specified dimension.
- name: reactivated_deals
data_type: bigint
description: |
Count of reactivated deals in a given date and per specified dimension.
- name: deals_booked_in_month
data_type: bigint
description: |
Count of deals booked within the month in a given date and per specified dimension.
- name: deals_booked_in_6_months
data_type: bigint
description: |
Count of deals booked within the past 6 months in a given date and per specified dimension.
- name: deals_booked_in_12_months
data_type: bigint
description: |
Count of deals booked within the past 12 months in a given date and per specified dimension.
- name: int_kpis__agg_daily_deals
description: |
This model computes the dimension aggregation for
Daily Deal metrics.
The primary key of this model is date, dimension
and dimension_value.
Be aware that this Deal entity will differ from how the rest of models
usually operate. This is because we compute Deal metrics, thus it does
not make sense to compute these at Deal level.
Also, Deal metrics at daily level already contain the time dimension
aggregates needed, thus we won't have mtd or monthly equivalent models,
but rather just select from this daily model the needed days to recover
the necessary information.
tests:
- dbt_utils.unique_combination_of_columns:
combination_of_columns:
- date
- dimension
- dimension_value
columns:
- name: date
data_type: date
description: Date containing the Deal metrics.
tests:
- not_null
- name: dimension
data_type: string
description: The dimension or granularity of the metrics.
tests:
- assert_dimension_completeness:
metric_column_names:
- new_deals
- never_booked_deals
- first_time_booked_deals
- active_deals
- churning_deals
- inactive_deals
- reactivated_deals
- deals_booked_in_month
- deals_booked_in_6_months
- deals_booked_in_12_months
- 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.
tests:
- not_null
- name: is_end_of_month
data_type: boolean
description: True if it's end of month, false otherwise.
tests:
- not_null
- name: is_current_month
data_type: boolean
description: |
True if the date is within the current month, false otherwise.
tests:
- not_null
- name: is_month_to_date
data_type: boolean
description: |
True if the date is within the scope of month-to-date, false otherwise.
The scope of month-to-date takes into account both 1) a date being in
the current month or 2) a date corresponding to the same month of the
previous year, which day number cannot be higher than yesterday's day
number.
tests:
- not_null
- name: new_deals
data_type: bigint
description: |
Count of new deals for a given date, dimension and value.
- name: never_booked_deals
data_type: bigint
description: |
Count of never booked deals for a given date, dimension and value.
- name: first_time_booked_deals
data_type: bigint
description: |
Count of first-time booked deals for a given date, dimension and value.
- name: active_deals
data_type: bigint
description: |
Count of active deals for a given date, dimension and value.
- name: inactive_deals
data_type: bigint
description: |
Count of inactive deals for a given date, dimension and value.
- name: churning_deals
data_type: bigint
description: |
Count of churning deals for a given date, dimension and value.
- name: reactivated_deals
data_type: bigint
description: |
Count of reactivated deals for a given date, dimension and value.
- name: deals_booked_in_month
data_type: bigint
description: |
Count of deals booked within the month for a given date, dimension and value.
- name: deals_booked_in_6_months
data_type: bigint
description: |
Count of deals booked within the past 6 months for a given date, dimension and value.
- name: deals_booked_in_12_months
data_type: bigint
description: |
Count of deals booked within the past 12 months for a given date, dimension and value.

120
tests/tmp_kpis_refactor_equivalent_deals.sql Normal file
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{% set min_date = "2022-01-01" %}
{% set dimensions = ("global", "by_billing_country") %}
-- "by_number_of_listings" excluded on purpose - there's differences because of daily
-- segmentation
with
new_deals as (
select
date,
dimension,
dimension_value,
new_deals,
never_booked_deals,
first_time_booked_deals,
active_deals,
churning_deals,
inactive_deals,
reactivated_deals,
deals_booked_in_month,
deals_booked_in_6_months,
deals_booked_in_12_months
from {{ ref("int_kpis__agg_daily_deals") }}
where
date >= '{{ min_date }}'
and dimension in {{ dimensions }}
and dimension_value <> 'UNSET'
and (is_end_of_month = true or is_month_to_date = true)
),
old_deals as (
select
date,
dimension,
dimension_value,
new_deals,
never_booked_deals,
first_time_booked_deals,
active_deals,
churning_deals,
inactive_deals,
reactivated_deals,
deals_booked_in_month,
deals_booked_in_6_months,
deals_booked_in_12_months
from {{ ref("int_mtd_deal_metrics") }}
where date >= '{{ min_date }}' and dimension in {{ dimensions }}
),
comparison as (
select
coalesce(o.date, n.date) as date,
coalesce(o.dimension, n.dimension) as dimension,
coalesce(o.dimension_value, n.dimension_value) as dimension_value,
o.new_deals as old_new_deals,
n.new_deals as new_new_deals,
coalesce(o.new_deals, 0) - coalesce(n.new_deals, 0) as diff_new_deals,
o.never_booked_deals as old_never_booked_deals,
n.never_booked_deals as new_never_booked_deals,
coalesce(o.never_booked_deals, 0)
- coalesce(n.never_booked_deals, 0) as diff_never_booked_deals,
o.first_time_booked_deals as old_first_time_booked_deals,
n.first_time_booked_deals as new_first_time_booked_deals,
coalesce(o.first_time_booked_deals, 0)
- coalesce(n.first_time_booked_deals, 0) as diff_first_time_booked_deals,
o.active_deals as old_active_deals,
n.active_deals as new_active_deals,
coalesce(o.active_deals, 0)
- coalesce(n.active_deals, 0) as diff_active_deals,
o.inactive_deals as old_inactive_deals,
n.inactive_deals as new_inactive_deals,
coalesce(o.inactive_deals, 0)
- coalesce(n.inactive_deals, 0) as diff_inactive_deals,
o.churning_deals as old_churning_deals,
n.churning_deals as new_churning_deals,
coalesce(o.churning_deals, 0)
- coalesce(n.churning_deals, 0) as diff_churning_deals,
o.reactivated_deals as old_reactivated_deals,
n.reactivated_deals as new_reactivated_deals,
coalesce(o.reactivated_deals, 0)
- coalesce(n.reactivated_deals, 0) as diff_reactivated_deals,
o.deals_booked_in_month as old_deals_booked_in_month,
n.deals_booked_in_month as new_deals_booked_in_month,
coalesce(o.deals_booked_in_month, 0)
- coalesce(n.deals_booked_in_month, 0) as diff_deals_booked_in_month,
o.deals_booked_in_6_months as old_deals_booked_in_6_months,
n.deals_booked_in_6_months as new_deals_booked_in_6_months,
coalesce(o.deals_booked_in_6_months, 0)
- coalesce(n.deals_booked_in_6_months, 0) as diff_deals_booked_in_6_months,
o.deals_booked_in_12_months as old_deals_booked_in_12_months,
n.deals_booked_in_12_months as new_deals_booked_in_12_months,
coalesce(o.deals_booked_in_12_months, 0)
- coalesce(n.deals_booked_in_12_months, 0) as diff_deals_booked_in_12_months
from old_deals o
full outer join
new_deals n
on o.date = n.date
and o.dimension = n.dimension
and o.dimension_value = n.dimension_value
)
select *
from comparison
where
diff_new_deals <> 0
or diff_never_booked_deals <> 0
or diff_first_time_booked_deals <> 0
or diff_active_deals <> 0
or diff_inactive_deals <> 0
or diff_churning_deals <> 0
or diff_reactivated_deals <> 0
or diff_deals_booked_in_month <> 0
or diff_deals_booked_in_6_months <> 0
or diff_deals_booked_in_12_months <> 0
order by date desc