2f80642f6c
# Description Eliminates models that have been switched with new kpis flow. Also deletes temporary tests and schema entries. # Checklist - [X] The edited models and dependants run properly with production data. - [NA] 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. - [NA] 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: #23762
1420 lines
50 KiB
YAML
1420 lines
50 KiB
YAML
models:
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- name: int_daily_currency_exchange_rates
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description: >-
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This model holds a lot of data on currency exchange rates. The time
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granularity is daily. Each record holds a currency pair for a specific
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day, source and version.
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Actual rates are sourced from xe.com data. The `guessed` and `forecast`
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versions are built by simply 'pushing' the first/last exchange rate on
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record. Basically, wherever we dont' have data for a date, we pick the
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closest actual data point that comes from xe.com. Bear in mind this means
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that `forecast` version records will change on a daily basis as actual
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data moves forwards, meaning you shouldn't assume your money amounts
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converted in the future should always stay put.
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Note that, given the dimensionality, getting a simple time series for a
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currency pair will require a bit of filtering.
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Reverse rates are explicit. This means that, for any given day and any
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given currency pair, you will find two records with opposite from/to
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positions. So, for 2024-01-01, you will find both a EUR->USD record and a
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USD->EUR record with the opposite rate (1/rate).
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columns:
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- name: id_exchange_rate
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data_type: text
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description: A unique ID for the record, derived from concatenating the
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currencies, date, source and version. Currency order is relevant
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(EURUSD != USDEUR).
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tests:
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- not_null
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- unique
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- name: from_currency
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data_type: character
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description: The source currency, represented as an ISO 4217 code.
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tests:
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- not_null
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- name: to_currency
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data_type: character
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description: The target currency, represented as an ISO 4217 code.
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tests:
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- not_null
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- name: rate
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data_type: numeric
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description: >-
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The exchange rate, represented as the units of the target currency
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that one unit of source currency gets you. So, from_currency=USD to
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to_currency=PLN with rate=4.2 should be read as '1 US Dollar buys me
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4.2 Polish Zlotys'.
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For same currency pairs (EUR to EUR, USD to USD, etc). The rate will
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always be one.
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The rate can be smaller than one, but can't be negative.
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tests:
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- not_negative_or_zero
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- not_null
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- name: rate_date_utc
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data_type: date
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description: The date in which the rate record is relevant.
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tests:
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- not_null
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- name: source
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data_type: text
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description:
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Where is the data coming from. Records that are composed from
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making assumptions on real data will contain `_inferred`.
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- name: rate_version
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data_type: text
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description:
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The version of the rate. This can be one of `actual` (the rate is a
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reality fact), `forecast` (the rate sits in the future and is a guess
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in nature) or `guess` (the rate sits in the past and is a guess in
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nature). Note that one currency pair can have multiple rate versions
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on the same date.
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tests:
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- accepted_values:
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values:
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- guess
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- actual
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- forecast
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- not_null
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- name: updated_at_utc
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data_type: timestamp with time zone
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description:
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For external sources, this will be the point in time when the
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information was obtained from them. For stuff we make up here in the
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DWH, this will be the point in time when we made the assumption.
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tests:
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- not_null
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- name: int_simple_exchange_rates
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description: >-
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A simplified vision of exchange rates, derived from
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`int_daily_currency_exchange_rates`. Come here if you don't want to
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understand nuances and complexities and just want to convert rates.
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The time granularity is daily. Each record holds a currency pair for a
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specific day. You will only find one conversion rate per currency pair and
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date.
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tests:
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- dbt_utils.unique_combination_of_columns:
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combination_of_columns:
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- from_currency
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- to_currency
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- rate_date_utc
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columns:
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- name: from_currency
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data_type: character
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description: The source currency, represented as an ISO 4217 code.
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tests:
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- not_null
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- name: to_currency
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data_type: character
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description: The source currency, represented as an ISO 4217 code.
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tests:
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- not_null
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- name: rate
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data_type: numeric
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description: The target currency, represented as an ISO 4217 code.
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tests:
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- not_null
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- name: rate_date_utc
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data_type: date
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description: The date in which the rate record is relevant.
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tests:
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- not_null
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- name: updated_at_utc
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data_type: timestamp with time zone
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description:
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For external sources, this will be the point in time when the
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information was obtained from them. For stuff we make up here in the
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DWH, this will be the point in time when we made the assumption.
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tests:
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- not_null
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- name: int_mtd_vs_previous_year_metrics
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description: |
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This model is used for global KPIs.
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It aggregates all the mtd models with the different metrics per source
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and computes any necessary weighted metric across different sources.
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Each metric has a date, dimension and dimension value that defines
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the primary key of this model.
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Finally, it displays any metric on the current date, the previous year
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date and it computes the relative increment by using the macro:
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- calculate_safe_relative_increment
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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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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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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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tests:
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- not_null
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- name: int_dates_mtd
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description: |
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This model provides Month-To-Date (MTD) necessary dates for MTD-based models to work.
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- For month-to-month complete information, it retrieves all end month dates that have elapsed since 2020.
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- For month-to-date information, it retrieves the days of the current month of this year up to yesterday.
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Additionally, it also gets the days of its equivalent month from last year previous the current day of month of today.
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Example:
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Imagine we have are at 4th June 2024.
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- We will get the dates for 1st, 2nd, 3rd of June 2024.
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- We will also get the dates for 1st, 2nd, 3rd of June 2023.
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- We will get all end of months from 2020 to yesterday,
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i.e., 31st January 2020, 29th February 2020, ..., 30th April 2024, 31st May 2024.
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deprecation_date: 2024-11-30
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columns:
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- name: year
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data_type: int
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description: Year number of the given date.
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tests:
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- not_null
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- name: month
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data_type: int
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description: Month number of the given date.
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tests:
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- not_null
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- name: day
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data_type: int
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description: Day monthly number of the given date.
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tests:
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- not_null
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- name: is_end_of_month
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data_type: boolean
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description: Is end of month, 1 for yes, 0 for no.
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tests:
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- not_null
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- name: is_current_month
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data_type: boolean
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description: |
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Checks if the date is within the current executed month,
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1 for yes, 0 for no.
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tests:
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- not_null
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- name: first_day_month
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data_type: date
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description: |
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First day of the month correspoding to the date field.
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It comes from int_dates_mtd logic.
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tests:
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- not_null
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- name: date
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data_type: date
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description: |
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Main date for the computation, that is used for filters.
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It's the primary key for this model.
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tests:
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- not_null
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- unique
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- name: int_dates_by_deal
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description: |
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This model provides the necessary dates for each deal for deal-based KPIs models to work.
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It only considers those dates starting from when the host user of the deal was first available.
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deprecation_date: 2024-11-30
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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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- id_deal
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columns:
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- name: year
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data_type: int
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description: Year number of the given date.
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tests:
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- not_null
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- name: month
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data_type: int
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description: Month number of the given date.
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tests:
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- not_null
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- name: day
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data_type: int
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description: Day monthly number of the given date.
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tests:
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- not_null
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- name: last_day_month
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data_type: date
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description: |
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Last day of the month correspoding to the date field.
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It comes from int_dates_mtd logic.
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tests:
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- not_null
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- name: first_day_month
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data_type: date
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description: |
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First day of the month correspoding to the date field.
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It comes from int_dates_mtd logic.
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tests:
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- not_null
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- name: date
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data_type: date
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description: |
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Main date for the computation, that is used for filters.
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It's the primary key for this model.
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tests:
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- not_null
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- name: id_deal
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data_type: string
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description: |
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Main identifier of the B2B clients. A deal can have multiple hosts.
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A host should usually have a deal, but it does not happen on all cases.
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In this KPI reporting we force that Deal is not null to avoid potential
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data quality issues.
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tests:
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- not_null
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- name: main_deal_name
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data_type: string
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description: |
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Main name for this ID deal.
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tests:
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- not_null
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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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ISO 3166-1 alpha-3 main country code in which the Deal is billed.
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In some cases it's null.
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- name: int_mtd_aggregated_metrics
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description: |
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The `int_mtd_aggregated_metrics` model aggregates multiple metrics on a year, month, and day basis.
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The primary source of data is the `int_mtd_vs_previous_year_metrics` model, which contain the combination
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of metrics data per source. This model just changes the display format to unpivot the information into
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a set of metric, value, previous_year_value and relative_increment at a given date. It uses Jinja
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code to avoid code replication.
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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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- metric
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- dimension
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- dimension_value
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columns:
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- name: year
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data_type: int
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description: year number of the given date.
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tests:
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- not_null
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- name: month
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data_type: int
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description: month number of the given date.
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tests:
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- not_null
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- name: day
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data_type: int
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description: day monthly number of the given date.
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tests:
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- not_null
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- name: is_end_of_month
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data_type: boolean
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description: is end of month, 1 for yes, 0 for no.
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tests:
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- not_null
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- name: is_current_month
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data_type: boolean
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description: |
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checks if the date is within the current executed month,
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1 for yes, 0 for no.
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tests:
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- not_null
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- name: first_day_month
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data_type: date
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description: |
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first day of the month correspoding to the date field.
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It comes from int_dates_mtd logic.
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tests:
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- not_null
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- name: date
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data_type: date
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description: |
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main date for the computation, that is used for filters.
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It comes from int_dates_mtd logic.
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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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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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tests:
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- not_null
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- name: previous_year_date
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data_type: date
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description: |
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corresponds to the date of the previous year, with respect to the field date.
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It comes from int_dates_mtd logic. It's only displayed for information purposes,
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should not be needed for reporting.
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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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tests:
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- not_null
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- name: order_by
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data_type: integer
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description: |
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order for displaying purposes. Null values are accepted, but keep
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in mind that then there's no default controlled display order.
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- name: number_format
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data_type: text
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description: allows for grouping and formatting for displaying purposes.
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tests:
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- accepted_values:
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values:
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[
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"integer",
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"percentage",
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"currency_gbp",
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"converted_metric_currency_gbp",
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]
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- name: 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 MTD computation of the metric
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at a given date.
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- name: previous_year_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 MTD computation of the metric
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on the previous year at a given date.
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- name: relative_increment
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data_type: numeric
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description: |
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numeric value that corresponds to the relative increment between value and previous year value,
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following the computation: value / previous_year_value - 1.
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- name: relative_increment_with_sign_format
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data_type: numeric
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description: |
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relative_increment value multiplied by -1 in case this metric's growth doesn't have a
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positive impact for Superhog, otherwise is equal to relative_increment.
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This value is specially created for formatting in PBI
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- name: int_monthly_aggregated_metrics_history_by_deal
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description: |
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This model aggregates the monthly historic information regarding the different metrics computed
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at deal level. The primary sources of data are the `int_yyy__monthly_XXXXX_history_by_deal`
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models which contain the raw metrics data per source.
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Unlike the int_mtd_aggregated_metrics, this model does not abstract each metric, since
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no comparison versus last year is performed. In short, it just gathers the information stored
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in the abovementioned models.
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To keep in mind: aggregating the information of this model will not necessarily result into
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the int_mtd_aggregated metrics because 1) the mtd version contains more computing dates
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than the by deal version, the latest being a subset of the first, and 2) the deal based model
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enforces that a booking/guest journey/listing/etc has a host with a deal assigned, which is
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not necessarily the case.
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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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- id_deal
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columns:
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- name: date
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data_type: date
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description: The last day of the month or yesterday for historic metrics.
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tests:
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- not_null
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- name: id_deal
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data_type: character varying
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description: Id of the deal associated to the host.
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tests:
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- not_null
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- name: main_deal_name
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data_type: string
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description: |
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Main name for this ID deal.
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tests:
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- not_null
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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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ISO 3166-1 alpha-3 main country code in which the Deal is billed.
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In some cases it's null.
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- name: int_dates_mtd_by_dimension
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description: |
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This model provides Month-To-Date (MTD) necessary dates, dimension and dimension_values
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for MTD-based models to work.
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It provides the basic "empty" structure from which metrics will be built upon. This is, on
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top of the Date that characterises int_dates_mtd, including the dimensions and their
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respective values that should appear in any mtd metric model.
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Example:
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- For the "global" dimension, we will only have the "global" dimension value.
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- For the "by_number_of_listing" dimension, we will have different values
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according to the segments defined, ex: 0, 1-5, 6-20, etc.
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... and so on and forth for any available dimension. These combinations should appear
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for each date of the MTD models.
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deprecation_date: 2024-11-30
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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: year
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data_type: int
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description: Year number of the given date.
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tests:
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- not_null
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- name: month
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data_type: int
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description: Month number of the given date.
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tests:
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- not_null
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- name: day
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data_type: int
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description: Day monthly number of the given date.
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tests:
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- not_null
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- name: is_end_of_month
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data_type: boolean
|
|
description: Is end of month, 1 for yes, 0 for no.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: is_current_month
|
|
data_type: boolean
|
|
description: |
|
|
Checks if the date is within the current executed month,
|
|
1 for yes, 0 for no.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: first_day_month
|
|
data_type: date
|
|
description: |
|
|
First day of the month correspoding to the date field.
|
|
It comes from int_dates_mtd logic.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: date
|
|
data_type: date
|
|
description: |
|
|
Main date for the computation, metrics include monthly information
|
|
until this date.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: dimension
|
|
data_type: string
|
|
description: The dimension or granularity of the metrics.
|
|
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.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: int_monthly_growth_score_by_deal
|
|
description: |
|
|
The main goal of this model is to provide a growth score by deal and month.
|
|
The idea behind it is that each deal will have some business performance
|
|
associated to it over the months, and that comparing how it is currently
|
|
performing vs. historical data we can determine whether the tendency is to
|
|
grow or to decay. This is specially useful for AMs to focus their effort
|
|
towards the clients that have a negative tendency.
|
|
|
|
The computation of the growth score is based on 3 main indicators:
|
|
- Created bookings
|
|
- Listings booked in month
|
|
- Total revenue (in gbp)
|
|
|
|
The main idea is, for each deal, to compare each of these metrics by
|
|
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 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.
|
|
|
|
However, this is not strictly true for Revenue because 1) we have an invoicing
|
|
delay and 2) in some cases, monthly revenue per deal can be negative. In this
|
|
specific cases, the YoY comparison is shifted by one month, and an effective
|
|
revenue value for the revenue share is computed, that cannot be lower than 0.
|
|
|
|
In order to keep both a properly set up score and revenue consistency, both
|
|
a real revenue value and effective revenue value are present in this model,
|
|
while no MoM or YoY value is computed if negative revenue is found.
|
|
|
|
Lastly, this model provides informative date fields, deal attributes, absolute
|
|
metric values and MoM & YoY relative incrementals to enrich reporting.
|
|
|
|
tests:
|
|
- dbt_utils.unique_combination_of_columns:
|
|
combination_of_columns:
|
|
- date
|
|
- id_deal
|
|
|
|
columns:
|
|
- name: date
|
|
data_type: date
|
|
description: |
|
|
Date corresponding to the last day of the month. Given month
|
|
metrics are inclusive to this date. Together with id_deal, it
|
|
acts as the primary key of this model.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: id_deal
|
|
data_type: string
|
|
description: |
|
|
Unique identifier of a Deal. Together with date, it acts as
|
|
the primary key of this model.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: main_deal_name
|
|
data_type: string
|
|
description: |
|
|
Main name for a Deal, representing the client.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: main_billing_country_iso_3_per_deal
|
|
data_type: string
|
|
description: |
|
|
Main billing country for this client. In some cases
|
|
it can be null.
|
|
|
|
- name: deal_lifecycle_state
|
|
data_type: string
|
|
description: |
|
|
Identifier of the lifecycle state of a given deal
|
|
in a given month.
|
|
|
|
- name: deal_hubspot_stage
|
|
data_type: string
|
|
description: |
|
|
Current hubspot stage for a given deal.
|
|
|
|
- name: account_manager
|
|
data_type: string
|
|
description: |
|
|
Current Account Manager in charge of a given deal, according
|
|
to Hubspot.
|
|
|
|
- name: live_date_utc
|
|
data_type: date
|
|
description: |
|
|
Date in which the account has gone live, according to Hubspot.
|
|
|
|
- name: cancellation_date_utc
|
|
data_type: date
|
|
description: |
|
|
Date in which the account has been offboarded, according to
|
|
Hubspot.
|
|
|
|
- name: given_month_first_day_month
|
|
data_type: date
|
|
description: |
|
|
Informative field. It indicates the first day of the
|
|
month corresponding to date.
|
|
If date = 2024-09-30, this field will be 2024-09-01.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: previous_1_month_first_day_month
|
|
data_type: date
|
|
description: |
|
|
Informative field. It indicates the first day of the
|
|
previous month with respect to date.
|
|
If date = 2024-09-30, this field will be 2024-08-01.
|
|
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: |
|
|
Informative field. It indicates the first day of the
|
|
month with respect to date, but on the previous year.
|
|
If date = 2024-09-30, this field will be 2023-09-01.
|
|
It can be null if no previous history for that
|
|
deal is found.
|
|
|
|
- name: previous_13_month_first_day_month
|
|
data_type: date
|
|
description: |
|
|
Informative field. It indicates the first day of the
|
|
previous month with respect to date, but on the previous year.
|
|
If date = 2024-09-30, this field will be 2023-08-01.
|
|
It can be null if no previous history for that
|
|
deal is found.
|
|
|
|
- name: aggregated_revenue_from_first_day_month
|
|
data_type: date
|
|
description: |
|
|
Informative field. It indicates the first day of the
|
|
month from the lower bound range in which the revenue
|
|
aggregation is computed.
|
|
The aggregation uses the previous 12 months in which we
|
|
know the revenue, thus:
|
|
If date = 2024-09-30, this field will be 2023-09-01.
|
|
It can be null if no previous history for that
|
|
deal is found.
|
|
|
|
- name: aggregated_revenue_to_first_day_month
|
|
data_type: date
|
|
description: |
|
|
Informative field. It indicates the first day of the
|
|
month from the upper bound range in which the revenue
|
|
aggregation is computed.
|
|
The aggregation uses the previous 12 months in which we
|
|
know the revenue, thus:
|
|
If date = 2024-09-30, this field will be 2023-08-01.
|
|
It can be null if no previous history for that
|
|
deal is found.
|
|
|
|
- name: given_month_revenue_in_gbp
|
|
data_type: decimal
|
|
description: |
|
|
Monthly value representing revenue in GBP
|
|
for a specific deal. This value corresponds to
|
|
the given month. This value can be negative,
|
|
but not null.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: previous_1_month_revenue_in_gbp
|
|
data_type: decimal
|
|
description: |
|
|
Monthly value representing revenue in GBP
|
|
for a specific deal. This value corresponds to
|
|
the previous month.
|
|
This value can be negative.
|
|
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
|
|
the monthly amount generated 12 months ago.
|
|
This value can be negative.
|
|
This value can be null, thus indicating that no
|
|
history is available.
|
|
|
|
- name: previous_13_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 13 months ago.
|
|
This value can be negative.
|
|
This value can be null, thus indicating that no
|
|
history is available.
|
|
|
|
- name: mom_revenue_growth
|
|
data_type: decimal
|
|
description: |
|
|
Relative increment of the revenue generated in the
|
|
current month with respect to the one generated in
|
|
the previous month.
|
|
It can be null if any revenue used in the computation
|
|
is null or it's negative.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
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: |
|
|
Relative increment of the revenue generated in the
|
|
current month with respect to the one generated 12
|
|
months ago.
|
|
It can be null if any revenue used in the computation
|
|
is null or it's negative.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: -1
|
|
strictly: false
|
|
|
|
- name: yoy_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 13
|
|
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: given_month_created_bookings
|
|
data_type: integer
|
|
description: |
|
|
Monthly value representing created bookings
|
|
for a specific deal. This value corresponds to
|
|
the given month. This value cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: previous_1_month_created_bookings
|
|
data_type: integer
|
|
description: |
|
|
Monthly value representing created bookings
|
|
for a specific deal. This value corresponds to
|
|
the previous month.
|
|
This value can be null, thus indicating that no
|
|
history is available.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: previous_12_month_created_bookings
|
|
data_type: integer
|
|
description: |
|
|
Monthly value representing created bookings
|
|
for a specific deal. This value corresponds to
|
|
monthly amount generated 12 months ago.
|
|
This value can be null, thus indicating that no
|
|
history is available.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: mom_created_bookings_growth
|
|
data_type: decimal
|
|
description: |
|
|
Relative increment of the bookings created in the
|
|
current month with respect to the ones created in
|
|
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
|
|
strictly: false
|
|
|
|
- name: yoy_created_bookings_growth
|
|
data_type: decimal
|
|
description: |
|
|
Relative increment of the bookings created in the
|
|
current month with respect to the ones created 12
|
|
months ago.
|
|
It can be null if the bookings created 12 months
|
|
ago are null.
|
|
This field is used for the growth score computation.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: -1
|
|
strictly: false
|
|
|
|
- name: given_month_listings_booked_in_month
|
|
data_type: integer
|
|
description: |
|
|
Monthly value representing the listings booked in month
|
|
for a specific deal. This value corresponds to
|
|
the given month. This value cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: previous_1_month_listings_booked_in_month
|
|
data_type: integer
|
|
description: |
|
|
Monthly value representing the listings booked in month
|
|
for a specific deal. This value corresponds to
|
|
the previous month.
|
|
This value can be null, thus indicating that no
|
|
history is available.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: previous_12_month_listings_booked_in_month
|
|
data_type: integer
|
|
description: |
|
|
Monthly value representing the listings booked in month
|
|
for a specific deal. This value corresponds to
|
|
monthly amount generated 12 months ago.
|
|
This value can be null, thus indicating that no
|
|
history is available.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: mom_listings_booked_in_month_growth
|
|
data_type: decimal
|
|
description: |
|
|
Relative increment of the the listings booked in month
|
|
in the current month with respect to the ones of
|
|
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
|
|
strictly: false
|
|
|
|
- name: yoy_listings_booked_in_month_growth
|
|
data_type: decimal
|
|
description: |
|
|
Relative increment of the listings booked in month
|
|
in the current month with respect to the ones of 12
|
|
months ago.
|
|
It can be null if the listings booked in month of 12
|
|
months ago are null.
|
|
This field is used for the growth score computation.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: -1
|
|
strictly: false
|
|
|
|
- name: deal_revenue_12_months_window
|
|
data_type: decimal
|
|
description: |
|
|
Total aggregated revenue in GBP generated by a deal
|
|
in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
It can be negative if the sum is negative.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: effective_deal_revenue_12_months_window
|
|
data_type: decimal
|
|
description: |
|
|
Effective aggregated revenue in GBP generated by a deal
|
|
in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
All negative monthly revenue values are settled as 0,
|
|
thus this value should not be reported.
|
|
It is used for the deal contribution share with respect
|
|
to the global revenue. It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: effective_global_revenue_12_months_window
|
|
data_type: decimal
|
|
description: |
|
|
Effective aggregated revenue in GBP generated by all deals
|
|
in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
All negative monthly revenue values are settled as 0,
|
|
thus this value should not be reported.
|
|
It is used for the deal contribution share with respect
|
|
to the global revenue. It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: deal_contribution_share_to_global_revenue
|
|
data_type: decimal
|
|
description: |
|
|
Represents the size of the deal in terms of revenue. In
|
|
other words, what's the percentage of the global revenue
|
|
that can be attributed to this deal. It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: deal_contribution_rank_to_global_revenue
|
|
data_type: integer
|
|
description: |
|
|
Represents the ordered list of deals by descending size
|
|
in terms of revenue.
|
|
If more than one deal have the same share, the order is
|
|
not under control.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: deal_created_bookings_12_months_window
|
|
data_type: integer
|
|
description: |
|
|
Total created bookings generated by a deal
|
|
in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: global_created_bookings_12_months_window
|
|
data_type: integer
|
|
description: |
|
|
Total created bookings generated by any deal
|
|
in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
It is used for the deal contribution share with respect
|
|
to the global created bookings. It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: deal_contribution_share_to_global_created_bookings
|
|
data_type: decimal
|
|
description: |
|
|
Represents the size of the deal in terms of created bookings.
|
|
In other words, what's the percentage of the global created
|
|
bookings that can be attributed to this deal.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: deal_contribution_rank_to_global_created_bookings
|
|
data_type: integer
|
|
description: |
|
|
Represents the ordered list of deals by descending size
|
|
in terms of created bookings.
|
|
If more than one deal have the same share, the order is
|
|
not under control.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: deal_avg_listings_booked_in_month_12_months_window
|
|
data_type: decimal
|
|
description: |
|
|
Average listings booked in month by a deal
|
|
in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: global_avg_listings_booked_in_month_12_months_window
|
|
data_type: decimal
|
|
description: |
|
|
Sum of the average listings booked in month by
|
|
any deal in the months from the period ranging from the
|
|
aggregated_revenue_from_first_day_month to
|
|
aggregated_revenue_to_first_day_month.
|
|
It is used for the deal contribution share with respect
|
|
to the global average listings booked in month.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: deal_contribution_share_to_global_avg_listings_booked_in_month
|
|
data_type: decimal
|
|
description: |
|
|
Represents the size of the deal in terms of average listings
|
|
booked in month.
|
|
In other words, what's the percentage of the global average listings
|
|
booked in month that can be attributed to this deal.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
strictly: false
|
|
|
|
- name: deal_contribution_rank_to_global_avg_listings_booked_in_month
|
|
data_type: decimal
|
|
description: |
|
|
Represents the ordered list of deals by descending size
|
|
in terms of average listings booked in month.
|
|
If more than one deal have the same share, the order is
|
|
not under control.
|
|
It cannot be null.
|
|
tests:
|
|
- not_null
|
|
|
|
- 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
|
|
bookings, YoY growth of listings booked in month and
|
|
YoY 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_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: |
|
|
It's the weighted version of avg_growth_score that
|
|
takes into account the client size by using the revenue
|
|
contribution share of that deal to the global amount.
|
|
It's the main indicator towards measuring both growth
|
|
(if positive) or decay (if negative) while weighting
|
|
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:
|
|
- MAJOR DECLINE
|
|
- DECLINE
|
|
- FLAT
|
|
- GAIN
|
|
- MAJOR GAIN
|
|
- UNSET
|
|
|
|
- name: int_monthly_12m_window_contribution_by_deal
|
|
description: |
|
|
The main goal of this model is to provide how much a deal
|
|
contributes to a given metric on the global amount over a
|
|
period of 12 months.
|
|
|
|
At the moment, this is only done for 3 metrics:
|
|
- total_revenue_in_gbp
|
|
- created_bookings
|
|
- listings_booked_in_month
|
|
|
|
The contribution is based on an Average approach:
|
|
Over a period of 12 months, sum the value of a given a metric
|
|
for each deal, and divide it by the amount of months we're considering
|
|
for that deal. Sum all the average amounts per deals to get a global.
|
|
Divide the avg per deal value vs. the sum of avgs global one.
|
|
The average approach "boosts" the contribution of those accounts
|
|
that have been active for less than 12 months.
|
|
|
|
tests:
|
|
- dbt_utils.unique_combination_of_columns:
|
|
combination_of_columns:
|
|
- date
|
|
- id_deal
|
|
|
|
columns:
|
|
- name: date
|
|
data_type: date
|
|
description: |
|
|
Date corresponding to the last day of the month.
|
|
Metrics are inclusive to this date. Together with id_deal, it
|
|
acts as the primary key of this model.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: id_deal
|
|
data_type: string
|
|
description: |
|
|
Unique identifier of a Deal. Together with date, it acts as
|
|
the primary key of this model.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: deal_lifecycle_state
|
|
data_type: string
|
|
description: |
|
|
Identifier of the lifecycle state of a given deal
|
|
in a given month.
|
|
|
|
- name: preceding_months_count_by_deal
|
|
data_type: integer
|
|
description: |
|
|
Number of months preceding to the one given by date
|
|
that are used for the historic metric retrieval for
|
|
a given deal. In essence it states the amount of
|
|
months a given deal has been active before a the month
|
|
given by date, capped at 12 months.
|
|
tests:
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
max_value: 12
|
|
strictly: false
|
|
|
|
- name: has_deal_been_created_less_than_12_months_ago
|
|
data_type: boolean
|
|
description: |
|
|
Flag to identify if a given deal has been created less
|
|
than 12 months ago (true) or not (false). It's based on the
|
|
preceding_months_count_by_deal, and will be true on the first
|
|
year of deal activity.
|
|
|
|
- name: total_revenue_12m_average_contribution
|
|
data_type: numeric
|
|
description: |
|
|
Share of the deal contribution on total revenue
|
|
vs. the global amount, on the preceding 12 months
|
|
with respect to date. It uses the average approach.
|
|
It can be negative.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: created_bookings_12m_average_contribution
|
|
data_type: numeric
|
|
description: |
|
|
Share of the deal contribution on created bookings
|
|
vs. the global amount, on the preceding 12 months
|
|
with respect to date. It uses the average approach.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
max_value: 1
|
|
strictly: false
|
|
|
|
- name: listings_booked_in_month_12m_average_contribution
|
|
data_type: numeric
|
|
description: |
|
|
Share of the deal contribution on listings booked in month
|
|
vs. the global amount, on the preceding 12 months
|
|
with respect to date. It uses the average approach.
|
|
tests:
|
|
- not_null
|
|
- dbt_expectations.expect_column_values_to_be_between:
|
|
min_value: 0
|
|
max_value: 1
|
|
strictly: false
|
|
|
|
- name: int_monthly_churn_metrics
|
|
description: |
|
|
This model is used for global KPIs.
|
|
|
|
It computes the churn contribution by dimension, dimension value
|
|
and date, in a monthly basis. This model is different from the
|
|
usual mtd ones since it strictly depends on the monthly computation
|
|
of metrics by deal, which is done in a monthly basis rather than mtd.
|
|
In essence, it means we won't have data for the current month.
|
|
|
|
This model retrieves the 12 month contribution to global metrics
|
|
by deal and aggregates it to dimension and dimension value for those
|
|
deals that are tagged as '05-Churning' in that month. Thus, it provides
|
|
a total of 3 churn related metrics, represented as ratios over the total:
|
|
- Total Revenue (in GBP)
|
|
- Created Bookings
|
|
- Listings Booked in Month
|
|
by using the Average contribution method. For further
|
|
information, please refer to the documentation of the model:
|
|
- int_monthly_12m_window_contribution_by_deal
|
|
|
|
Lastly, when checking data at any dimension distinct from Global, at the
|
|
moment these values represent the additive contribution of churn with respect
|
|
to the global amount. This means that, for instance, if we have 10% of churn
|
|
in a month, it can be divided by 9% USA and 1% GBR since 9%+1% = 10%.
|
|
|
|
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.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: dimension
|
|
data_type: string
|
|
description: The dimension or granularity of the metrics.
|
|
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.
|
|
tests:
|
|
- not_null
|
|
|
|
- name: total_revenue_churn_average_contribution
|
|
data_type: numeric
|
|
description: Total Revenue churn rate (average approach).
|
|
|
|
- name: created_bookings_churn_average_contribution
|
|
data_type: numeric
|
|
description: Created Bookings churn rate (average approach).
|
|
|
|
- name: listings_booked_in_month_churn_average_contribution
|
|
data_type: numeric
|
|
description: Listings Booked in Month churn rate (average approach).
|