Aggregated model for guest kpi metrics
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Joaquin Ossa
parent
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commit
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2 changed files with 81 additions and 250 deletions
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@ -12,6 +12,7 @@
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"checkin_cover_fees_in_gbp",
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"total_guest_payments_in_gbp",
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) %}
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with
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int_kpis__product_guest_daily_metrics as (
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select
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@ -44,6 +45,7 @@ with
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{{ yesterday }} - interval '1 month' as pm_current_date,
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{{ yesterday }} - interval '1 week' as pw_current_date,
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extract(day from {{ yesterday }}) as current_day_of_month
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),
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aggregated_metrics as (
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{% for metric in metric_names %}
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@ -136,256 +138,6 @@ with
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union all
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{% endif %}
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{% endfor %}
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),
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metric_values as (
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select
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date_day,
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has_payment,
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has_id_check,
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main_billing_country_iso_3_per_deal,
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created_guest_journeys_not_cancelled,
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completed_guest_journeys_not_cancelled,
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-- Calculate the relevant sums for different timeframes
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sum(
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case
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when date_day between dr.current_year_start and {{ yesterday }}
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then created_guest_journeys_not_cancelled
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end
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) as ytd_created,
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sum(
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case
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when date_day between dr.current_year_start and {{ yesterday }}
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then completed_guest_journeys_not_cancelled
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end
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) as ytd_completed,
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sum(
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case
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when date_day between dr.py_year_start and dr.py_current_date
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then created_guest_journeys_not_cancelled
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end
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) as py_created,
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sum(
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case
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when date_day between dr.py_year_start and dr.py_current_date
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then completed_guest_journeys_not_cancelled
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end
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) as py_completed,
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sum(
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case
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when date_day between dr.py_year_start and dr.py_current_date
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then created_guest_journeys_not_cancelled
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end
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) as pp_created,
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sum(
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case
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when date_day between dr.py_year_start and dr.py_current_date
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then completed_guest_journeys_not_cancelled
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end
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) as pp_completed
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from int_kpis__product_guest_daily_metrics dm
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cross join date_ranges dr
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where extract(day from date_day) <= dr.current_day_of_month
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group by
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date_day, has_payment, has_id_check, main_billing_country_iso_3_per_deal
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),
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calculated_metrics as (
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select
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'guest_journey_conversion_rate' as metric,
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has_payment,
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has_id_check,
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main_billing_country_iso_3_per_deal,
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'YTD' as timeframe,
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case
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when ytd_created = 0 then null else ytd_completed / ytd_created
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end as current_value,
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case
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when py_created = 0 then null else py_completed / py_created
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end as py_value,
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case
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when pp_created = 0 then null else pp_completed / pp_created
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end as pp_value
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from metric_values
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)
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-- select
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-- 'guest_journey_conversion_rate' as metric,
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-- has_payment,
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-- has_id_check,
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-- main_billing_country_iso_3_per_deal,
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-- 'YTD' as timeframe,
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-- case
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-- when
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-- sum(
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-- case
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-- when
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-- date_day
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-- between dr.current_year_start and {{ yesterday }}
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-- then created_guest_journeys_not_cancelled
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-- end
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-- )
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-- = 0
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-- then null
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-- else
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-- sum(
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-- case
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-- when
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-- date_day
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-- between dr.current_year_start and {{ yesterday }}
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-- then completed_guest_journeys_not_cancelled
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-- end
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-- ) / sum(
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-- case
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-- when
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-- date_day
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-- between dr.current_year_start and {{ yesterday }}
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-- then created_guest_journeys_not_cancelled
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-- end
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-- )
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-- end as current_value,
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-- case
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-- when
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-- sum(
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-- case
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-- when
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-- date_day
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-- between dr.py_year_start and dr.py_current_date
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-- then created_guest_journeys_not_cancelled
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-- end
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-- )
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-- = 0
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-- then null
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-- else
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-- sum(
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-- case
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-- when
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-- date_day
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-- between dr.py_year_start and dr.py_current_date
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-- then completed_guest_journeys_not_cancelled
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-- end
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-- ) / sum(
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-- case
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-- when
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-- date_day
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-- between dr.py_year_start and dr.py_current_date
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-- then created_guest_journeys_not_cancelled
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-- end
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-- ) as py_value,
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-- case
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-- when
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-- sum(
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-- case
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-- when
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-- date_day
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-- between dr.py_year_start and dr.py_current_date
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-- then created_guest_journeys_not_cancelled
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-- end
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-- )
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-- = 0
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-- then null
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-- else
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-- sum(
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-- case
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-- when
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-- date_day
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-- between dr.py_year_start and dr.py_current_date
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-- then completed_guest_journeys_not_cancelled
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-- end
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-- ) / sum(
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-- case
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-- when
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-- date_day
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-- between dr.py_year_start and dr.py_current_date
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-- then created_guest_journeys_not_cancelled
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-- end
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-- ) as pp_value
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-- from int_kpis__product_guest_daily_metrics dm, date_ranges dr
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-- where extract(day from date_day) <= dr.current_day_of_month
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-- group by metric, has_payment, has_id_check, main_billing_country_iso_3_per_deal
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-- union all
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-- select
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-- 'guest_revenue_per_guest_journey_created' as metric,
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-- has_payment,
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-- has_id_check,
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-- main_billing_country_iso_3_per_deal,
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-- 'YTD' as timeframe,
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-- sum(
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-- case
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-- when date_day between dr.current_year_start and {{ yesterday }}
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-- then total_guest_payments_in_gbp
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-- end
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-- ) / sum(
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-- case
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-- when date_day between dr.current_year_start and {{ yesterday }}
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-- then created_guest_journeys_not_cancelled
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-- end
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-- ) as current_value,
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-- sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then total_guest_payments_in_gbp
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-- end
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-- ) / sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then completed_guest_journeys_not_cancelled
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-- end
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-- ) as py_value,
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-- sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then total_guest_payments_in_gbp
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-- end
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-- ) / sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then completed_guest_journeys_not_cancelled
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-- end
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-- ) as pp_value
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-- from int_kpis__product_guest_daily_metrics dm, date_ranges dr
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-- where extract(day from date_day) <= dr.current_day_of_month
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-- group by metric, has_payment, has_id_check, main_billing_country_iso_3_per_deal
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-- union all
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-- select
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-- 'csat_score' as metric,
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-- has_payment,
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-- has_id_check,
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-- main_billing_country_iso_3_per_deal,
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-- 'YTD' as timeframe,
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-- sum(
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-- case
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-- when date_day between dr.current_year_start and {{ yesterday }}
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-- then total_csat_score_count * average_csat_score
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-- end
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-- ) / sum(
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-- case
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-- when date_day between dr.current_year_start and {{ yesterday }}
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-- then total_csat_score_count
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-- end
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-- ) as current_value,
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-- sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then total_csat_score_count * average_csat_score
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-- end
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-- ) / sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then total_csat_score_count
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-- end
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-- ) as py_value,
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-- sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then total_csat_score_count * average_csat_score
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-- end
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-- ) / sum(
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-- case
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-- when date_day between dr.py_year_start and dr.py_current_date
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-- then total_csat_score_count
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-- end
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-- ) as pp_value
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-- from int_kpis__product_guest_daily_metrics dm, date_ranges dr
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-- where extract(day from date_day) <= dr.current_day_of_month
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-- group by metric, has_payment, has_id_check, main_billing_country_iso_3_per_deal
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-- )
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select *
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from aggregated_metrics
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@ -5287,6 +5287,85 @@ models:
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Sum of total payments paid by guests, excluding taxes, in GBP
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on the same date in the previous year, segmented by the specified dimension.
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- name: int_kpis__product_guest_agg_metrics
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description:
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This model aggregates multiple metrics on a Year-to-date, Month-to-date or
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Week-to-date basis. This model changes the display format of the model
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int_kpis__product_guest_daily_metrics pivoting the metrics columns and
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adding a timeframe dimension.
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columns:
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- name: metric
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data_type: text
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description: Name of the business metric
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- name: has_payment
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data_type: string
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description: Has there been any guest payments on the guest journey.
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tests:
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- not_null
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- accepted_values:
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values:
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- W/O Payment
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- With Payment
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- name: has_id_check
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data_type: string
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description: Does the verification in the guest journey
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includes Government Id Check for the bookings.
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tests:
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- not_null
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- accepted_values:
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values:
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- W/O Id Check
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- With Id Check
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- name: main_billing_country_iso_3_per_deal
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data_type: string
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description: |
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Main billing country of the host aggregated at Deal level.
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tests:
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- not_null
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- name: timeframe
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data_type: text
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description: |
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Timeframe considered for the aggregation, it could be Year-to-date,
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Month-to-date or Week-to-date
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tests:
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- not_null
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- accepted_values:
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values:
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- YTD
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- MTD
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- WTD
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- name: current_value
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data_type: numeric
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description: |
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Numeric value (integer or decimal) that corresponds to the timeframe
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computation of the metric at the current date.
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For example if the current date is 27/11/2024 and the timeframe is MTD,
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then this value would correspond to the computation of the metric for
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the dates between 01/11/2024 and 27/11/2024.
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- name: py_value
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data_type: numeric
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description: |
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Numeric value (integer or decimal) that corresponds to the timeframe
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computation of the metric at the current date but on the previous year.
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For example if the current date is 27/11/2024 and the timeframe is MTD,
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then this value would correspond to the computation of the metric for
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the dates between 01/11/2023 and 27/11/2023.
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- name: pp_value
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data_type: numeric
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description: |
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Numeric value (integer or decimal) that corresponds to the timeframe
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computation of the metric at the current date but on the previous period.
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For example if the current date is 27/11/2024 and the timeframe is MTD,
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then this value would correspond to the computation of the metric for
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the dates between 01/10/2024 and 27/10/2024.
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- name: int_kpis__metric_daily_new_dash_created_services
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description: |
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This model computes the Daily Created Services at the deepest granularity.
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