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Merged PR 3137: Growth score to reporting

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# Description

Copies intermediate to reporting for growth score by deal. Schema is copy-paste from intermediate changing the model's name.

# 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.
- [NA] 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: #22635
This commit is contained in:
Oriol Roqué Paniagua 2024-10-14 12:26:01 +00:00
parent a6191eba46
commit eb213acb9e
4 changed files with 792 additions and 65 deletions
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190
models/intermediate/cross/int_monthly_growth_score_by_deal.sql
View file

@ -74,6 +74,7 @@ with
-- Dates - For Information Purposes
given_month.first_day_month as given_month_first_day_month,
previous_1_month.first_day_month as previous_1_month_first_day_month,
previous_2_month.first_day_month as previous_2_month_first_day_month,
previous_12_month.first_day_month as previous_12_month_first_day_month,
previous_13_month.first_day_month as previous_13_month_first_day_month,
aggregated_revenue.from_first_day_month
@ -84,6 +85,7 @@ with
-- Revenue --
given_month.revenue_in_gbp as given_month_revenue_in_gbp,
previous_1_month.revenue_in_gbp as previous_1_month_revenue_in_gbp,
previous_2_month.revenue_in_gbp as previous_2_month_revenue_in_gbp,
previous_12_month.revenue_in_gbp as previous_12_month_revenue_in_gbp,
previous_13_month.revenue_in_gbp as previous_13_month_revenue_in_gbp,
-- In these YoY and MoM computations, if negative revenue is found,
@ -92,6 +94,9 @@ with
nullif(greatest(given_month.revenue_in_gbp, 0), 0)
/ nullif(greatest(previous_1_month.revenue_in_gbp, 0), 0)
- 1 as mom_revenue_growth,
nullif(greatest(previous_1_month.revenue_in_gbp, 0), 0)
/ nullif(greatest(previous_2_month.revenue_in_gbp, 0), 0)
- 1 as mom_1_month_shift_revenue_growth,
nullif(greatest(given_month.revenue_in_gbp, 0), 0)
/ nullif(greatest(previous_12_month.revenue_in_gbp, 0), 0)
- 1 as yoy_revenue_growth,
@ -146,6 +151,12 @@ with
on previous_1_month.first_day_month + interval '1 months'
= given_month.first_day_month
and previous_1_month.id_deal = given_month.id_deal
-- Retrieve monthly data from same deal and 2 months ago
left join
deal_history_from_previous_months as previous_2_month
on previous_2_month.first_day_month + interval '2 months'
= given_month.first_day_month
and previous_2_month.id_deal = given_month.id_deal
-- Retrieve monthly data from same deal and the equivalent
-- month from last year
left join
@ -154,8 +165,7 @@ with
= given_month.first_day_month
and previous_12_month.id_deal = given_month.id_deal
-- Retrieve monthly data from same deal and the equivalent
-- month from last
-- year and 1 month before
-- month from last year and 1 month before
left join
deal_history_from_previous_months as previous_13_month
on previous_13_month.first_day_month + interval '13 months'
@ -166,62 +176,124 @@ with
revenue_12_months_window_aggregation_per_deal aggregated_revenue
on aggregated_revenue.id_deal = given_month.id_deal
and aggregated_revenue.first_day_month = given_month.first_day_month
),
growth_score_computation as (
select
m.date,
m.id_deal,
m.main_deal_name,
m.main_billing_country_iso_3_per_deal,
m.deal_lifecycle_state,
m.given_month_first_day_month,
m.previous_1_month_first_day_month,
m.previous_2_month_first_day_month,
m.previous_12_month_first_day_month,
m.previous_13_month_first_day_month,
m.aggregated_revenue_from_first_day_month,
m.aggregated_revenue_to_first_day_month,
m.given_month_revenue_in_gbp,
m.previous_1_month_revenue_in_gbp,
m.previous_2_month_revenue_in_gbp,
m.previous_12_month_revenue_in_gbp,
m.previous_13_month_revenue_in_gbp,
cast(m.mom_revenue_growth as numeric(19, 6)) as mom_revenue_growth,
cast(
m.mom_1_month_shift_revenue_growth as numeric(19, 6)
) as mom_1_month_shift_revenue_growth,
cast(m.yoy_revenue_growth as numeric(19, 6)) as yoy_revenue_growth,
cast(
m.yoy_1_month_shift_revenue_growth as numeric(19, 6)
) as yoy_1_month_shift_revenue_growth,
m.given_month_created_bookings,
m.previous_1_month_created_bookings,
m.previous_12_month_created_bookings,
cast(
m.mom_created_bookings_growth as numeric(19, 6)
) as mom_created_bookings_growth,
cast(
m.yoy_created_bookings_growth as numeric(19, 6)
) as yoy_created_bookings_growth,
m.given_month_listings_booked_in_month,
m.previous_1_month_listings_booked_in_month,
m.previous_12_month_listings_booked_in_month,
cast(
m.mom_listings_booked_in_month_growth as numeric(19, 6)
) as mom_listings_booked_in_month_growth,
cast(
m.yoy_listings_booked_in_month_growth as numeric(19, 6)
) as yoy_listings_booked_in_month_growth,
m.deal_revenue_12_months_window,
m.effective_deal_revenue_12_months_window,
m.effective_global_revenue_12_months_window,
m.deal_contribution_share_to_global_revenue,
m.deal_contribution_rank_to_global_revenue,
cast(
(
coalesce(m.mom_created_bookings_growth, 0)
+ coalesce(m.mom_listings_booked_in_month_growth, 0)
+ coalesce(m.mom_1_month_shift_revenue_growth, 0)
)
/ 3 as numeric(19, 6)
) as avg_mom_growth_score,
cast(
(
coalesce(m.yoy_created_bookings_growth, 0)
+ coalesce(m.yoy_listings_booked_in_month_growth, 0)
+ coalesce(m.yoy_1_month_shift_revenue_growth, 0)
)
/ 3 as numeric(19, 6)
) as avg_yoy_growth_score,
cast(
(
coalesce(m.yoy_created_bookings_growth, 0)
+ coalesce(m.yoy_listings_booked_in_month_growth, 0)
+ coalesce(m.yoy_1_month_shift_revenue_growth, 0)
+ coalesce(m.mom_created_bookings_growth, 0)
+ coalesce(m.mom_listings_booked_in_month_growth, 0)
+ coalesce(m.mom_1_month_shift_revenue_growth, 0)
)
/ 6 as numeric(19, 6)
) as avg_growth_score,
cast(
(
(
coalesce(m.yoy_created_bookings_growth, 0)
+ coalesce(m.yoy_listings_booked_in_month_growth, 0)
+ coalesce(m.yoy_1_month_shift_revenue_growth, 0)
+ coalesce(m.mom_created_bookings_growth, 0)
+ coalesce(m.mom_listings_booked_in_month_growth, 0)
+ coalesce(m.mom_1_month_shift_revenue_growth, 0)
)
/ 6
)
* deal_contribution_share_to_global_revenue as numeric(19, 6)
) as weighted_avg_growth_score
from metrics_attribution_to_given_month_per_deal m
)
select
m.date,
m.id_deal,
m.main_deal_name,
m.main_billing_country_iso_3_per_deal,
m.deal_lifecycle_state,
m.given_month_first_day_month,
m.previous_1_month_first_day_month,
m.previous_12_month_first_day_month,
m.previous_13_month_first_day_month,
m.aggregated_revenue_from_first_day_month,
m.aggregated_revenue_to_first_day_month,
m.given_month_revenue_in_gbp,
m.previous_1_month_revenue_in_gbp,
m.previous_12_month_revenue_in_gbp,
m.previous_13_month_revenue_in_gbp,
m.mom_revenue_growth,
m.yoy_revenue_growth,
m.yoy_1_month_shift_revenue_growth,
m.given_month_created_bookings,
m.previous_1_month_created_bookings,
m.previous_12_month_created_bookings,
m.mom_created_bookings_growth,
m.yoy_created_bookings_growth,
m.given_month_listings_booked_in_month,
m.previous_1_month_listings_booked_in_month,
m.previous_12_month_listings_booked_in_month,
m.mom_listings_booked_in_month_growth,
m.yoy_listings_booked_in_month_growth,
m.deal_revenue_12_months_window,
m.effective_deal_revenue_12_months_window,
m.effective_global_revenue_12_months_window,
m.deal_contribution_share_to_global_revenue,
m.deal_contribution_rank_to_global_revenue,
(
coalesce(m.yoy_created_bookings_growth, 0)
+ coalesce(m.yoy_listings_booked_in_month_growth, 0)
+ coalesce(m.yoy_1_month_shift_revenue_growth, 0)
)
/ 3 as avg_growth_score,
(
(
coalesce(m.yoy_created_bookings_growth, 0)
+ coalesce(m.yoy_listings_booked_in_month_growth, 0)
+ coalesce(m.yoy_1_month_shift_revenue_growth, 0)
)
/ 3
)
* deal_contribution_share_to_global_revenue as weighted_avg_growth_score
from metrics_attribution_to_given_month_per_deal m
*,
-- Applies a categorisation based on the score value. These scores thresholds are
-- opinionated
case
when weighted_avg_growth_score <= -0.001
then 'TOP LOSER'
when weighted_avg_growth_score > -0.001 and weighted_avg_growth_score < 0
then 'LOSER'
when weighted_avg_growth_score = 0
then 'FLAT'
when weighted_avg_growth_score > 0 and weighted_avg_growth_score < 0.001
then 'WINNER'
when weighted_avg_growth_score >= 0.001
then 'TOP WINNER'
else 'UNSET'
end as categorisation_weighted_avg_growth_score
from growth_score_computation

89
models/intermediate/cross/schema.yml
View file

@ -605,11 +605,12 @@ models:
- Total revenue (in gbp)
The main idea is, for each deal, to compare each of these metrics by
checking the latest monthly value vs. the monthly value of the equivalent
month on the previous year - in other words, a year-on-year (YoY) comparison.
checking the latest monthly value vs. 1) the monthly value of the equivalent
month on the previous year and 2) the monthly value of the previous month
- in other words, a year-on-year (YoY) and month-on-month (MoM) comparison.
We do this comparison by doing a relative incremental.
The growth score is computed then by averaging the outcome of the 3 scores.
The growth score is computed then by averaging the outcome of the 6 scores.
Lastly, in order to provide a prioritisation sense, we have a weighted growth
score that results from the multiplication of the growth score per the revenue
weight a specific deal has provided in the previous 12 months.
@ -687,6 +688,15 @@ models:
It can be null if no previous history for that
deal is found.
- name: previous_2_month_first_day_month
data_type: date
description: |
Informative field. It indicates the first day of the
month 2 months before with respect to date.
If date = 2024-09-30, this field will be 2024-07-01.
It can be null if no previous history for that
deal is found.
- name: previous_12_month_first_day_month
data_type: date
description: |
@ -749,12 +759,22 @@ models:
This value can be null, thus indicating that no
history is available.
- name: previous_2_month_revenue_in_gbp
data_type: decimal
description: |
Monthly value representing revenue in GBP
for a specific deal. This value corresponds to
the monthly amount generated 2 months ago
This value can be negative.
This value can be null, thus indicating that no
history is available.
- name: previous_12_month_revenue_in_gbp
data_type: decimal
description: |
Monthly value representing revenue in GBP
for a specific deal. This value corresponds to
monthly amount generated 12 months ago.
the monthly amount generated 12 months ago.
This value can be negative.
This value can be null, thus indicating that no
history is available.
@ -764,7 +784,7 @@ models:
description: |
Monthly value representing revenue in GBP
for a specific deal. This value corresponds to
monthly amount generated 13 months ago.
the monthly amount generated 13 months ago.
This value can be negative.
This value can be null, thus indicating that no
history is available.
@ -782,6 +802,20 @@ models:
min_value: -1
strictly: false
- name: mom_1_month_shift_revenue_growth
data_type: decimal
description: |
Relative increment of the revenue generated in the
previous month with respect to the one generated 2
months ago.
It can be null if any revenue used in the computation
is null or it's negative.
This field is used for the growth score computation.
tests:
- dbt_expectations.expect_column_values_to_be_between:
min_value: -1
strictly: false
- name: yoy_revenue_growth
data_type: decimal
description: |
@ -855,6 +889,7 @@ models:
the previous month.
It can be null if the bookings created in the
previous month are null.
This field is used for the growth score computation.
tests:
- dbt_expectations.expect_column_values_to_be_between:
min_value: -1
@ -920,6 +955,7 @@ models:
the previous month.
It can be null if the listings booked in month in the
previous month are null.
This field is used for the growth score computation.
tests:
- dbt_expectations.expect_column_values_to_be_between:
min_value: -1
@ -1007,7 +1043,18 @@ models:
tests:
- not_null
- name: avg_growth_score
- name: avg_mom_growth_score
data_type: decimal
description: |
Represents the average score of MoM growth of created
bookings, MoM growth of listings booked in month and
MoM shifted by one month of revenue.
It indicates the tendency of growth of the deal without
taking into account its revenue size. It cannot be null.
tests:
- not_null
- name: avg_yoy_growth_score
data_type: decimal
description: |
Represents the average score of YoY growth of created
@ -1018,6 +1065,17 @@ models:
tests:
- not_null
- name: avg_growth_score
data_type: decimal
description: |
Represents the average score of YoY and MoM growth of created
bookings, YoY and MoM growth of listings booked in month and
YoY and MoM shifted by one month of revenue.
It indicates the tendency of growth of the deal without
taking into account its revenue size. It cannot be null.
tests:
- not_null
- name: weighted_avg_growth_score
data_type: decimal
description: |
@ -1029,3 +1087,22 @@ models:
the financial impact this deal tendency can have.
tests:
- not_null
- name: categorisation_weighted_avg_growth_score
data_type: string
description: |
Discrete categorisation of weighted_avg_growth_score.
It helps easily identifying which accounts are top losers,
losers, flat, winners and top winners.
Currently the categorisation is based on the score itself
rather than selecting a top up/down.
tests:
- not_null
- accepted_values:
values:
- TOP LOSER
- LOSER
- FLAT
- WINNER
- TOP WINNER
- UNSET