models: - name: int_daily_currency_exchange_rates description: >- This model holds a lot of data on currency exchange rates. The time granularity is daily. Each record holds a currency pair for a specific day, source and version. Actual rates are sourced from xe.com data. The `guessed` and `forecast` versions are built by simply 'pushing' the first/last exchange rate on record. Basically, wherever we dont' have data for a date, we pick the closest actual data point that comes from xe.com. Bear in mind this means that `forecast` version records will change on a daily basis as actual data moves forwards, meaning you shouldn't assume your money amounts converted in the future should always stay put. Note that, given the dimensionality, getting a simple time series for a currency pair will require a bit of filtering. Reverse rates are explicit. This means that, for any given day and any given currency pair, you will find two records with opposite from/to positions. So, for 2024-01-01, you will find both a EUR->USD record and a USD->EUR record with the opposite rate (1/rate). columns: - name: id_exchange_rate data_type: text description: A unique ID for the record, derived from concatenating the currencies, date, source and version. Currency order is relevant (EURUSD != USDEUR). tests: - not_null - unique - name: from_currency data_type: character description: The source currency, represented as an ISO 4217 code. tests: - not_null - name: to_currency data_type: character description: The target currency, represented as an ISO 4217 code. tests: - not_null - name: rate data_type: numeric description: >- The exchange rate, represented as the units of the target currency that one unit of source currency gets you. So, from_currency=USD to to_currency=PLN with rate=4.2 should be read as '1 US Dollar buys me 4.2 Polish Zlotys'. For same currency pairs (EUR to EUR, USD to USD, etc). The rate will always be one. The rate can be smaller than one, but can't be negative. tests: - not_negative_or_zero - not_null - name: rate_date_utc data_type: date description: The date in which the rate record is relevant. tests: - not_null - name: source data_type: text description: Where is the data coming from. Records that are composed from making assumptions on real data will contain `_inferred`. - name: rate_version data_type: text description: The version of the rate. This can be one of `actual` (the rate is a reality fact), `forecast` (the rate sits in the future and is a guess in nature) or `guess` (the rate sits in the past and is a guess in nature). Note that one currency pair can have multiple rate versions on the same date. tests: - accepted_values: values: - guess - actual - forecast - not_null - name: updated_at_utc data_type: timestamp with time zone description: For external sources, this will be the point in time when the information was obtained from them. For stuff we make up here in the DWH, this will be the point in time when we made the assumption. tests: - not_null - name: int_simple_exchange_rates description: >- A simplified vision of exchange rates, derived from `int_daily_currency_exchange_rates`. Come here if you don't want to understand nuances and complexities and just want to convert rates. The time granularity is daily. Each record holds a currency pair for a specific day. You will only find one conversion rate per currency pair and date. tests: - dbt_utils.unique_combination_of_columns: combination_of_columns: - from_currency - to_currency - rate_date_utc columns: - name: from_currency data_type: character description: The source currency, represented as an ISO 4217 code. tests: - not_null - name: to_currency data_type: character description: The source currency, represented as an ISO 4217 code. tests: - not_null - name: rate data_type: numeric description: The target currency, represented as an ISO 4217 code. tests: - not_null - name: rate_date_utc data_type: date description: The date in which the rate record is relevant. tests: - not_null - name: updated_at_utc data_type: timestamp with time zone description: For external sources, this will be the point in time when the information was obtained from them. For stuff we make up here in the DWH, this will be the point in time when we made the assumption. tests: - not_null - name: int_mtd_vs_previous_year_metrics description: | This model is used for global KPIs. It aggregates all the mtd models with the different metrics per source and computes any necessary weighted metric across different sources. Each metric has a date, dimension and dimension value that defines the primary key of this model. Finally, it displays any metric on the current date, the previous year date and it computes the relative increment by using the macro: - calculate_safe_relative_increment 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: int_mtd_aggregated_metrics description: | The `int_mtd_aggregated_metrics` model aggregates multiple metrics on a year, month, and day basis. The primary source of data is the `int_mtd_vs_previous_year_metrics` model, which contain the combination of metrics data per source. This model just changes the display format to unpivot the information into a set of metric, value, previous_year_value and relative_increment at a given date. It uses Jinja code to avoid code replication. tests: - dbt_utils.unique_combination_of_columns: combination_of_columns: - date - metric - dimension - dimension_value columns: - name: year data_type: int description: year number of the given date. tests: - not_null - name: month data_type: int description: month number of the given date. tests: - not_null - name: day data_type: int description: day monthly number of the given date. tests: - not_null - name: is_end_of_month 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 corresponding to the date field. tests: - not_null - name: date data_type: date description: | main date for the computation, that is used for filters. 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: previous_year_date data_type: date description: | corresponds to the date of the previous year, with respect to the field date. It's only displayed for information purposes, should not be needed for reporting. - name: metric data_type: text description: name of the business metric. tests: - not_null - name: order_by data_type: integer description: | order for displaying purposes. Null values are accepted, but keep in mind that then there's no default controlled display order. - name: number_format data_type: text description: allows for grouping and formatting for displaying purposes. tests: - accepted_values: values: [ "integer", "percentage", "currency_gbp", "converted_metric_currency_gbp", ] - name: value data_type: numeric description: | numeric value (integer or decimal) that corresponds to the MTD computation of the metric at a given date. - name: previous_year_value data_type: numeric description: | numeric value (integer or decimal) that corresponds to the MTD computation of the metric on the previous year at a given date. - name: relative_increment data_type: numeric description: | numeric value that corresponds to the relative increment between value and previous year value, following the computation: value / previous_year_value - 1. - name: relative_increment_with_sign_format data_type: numeric description: | relative_increment value multiplied by -1 in case this metric's growth doesn't have a positive impact for Superhog, otherwise is equal to relative_increment. This value is specially created for formatting in PBI - name: int_monthly_aggregated_metrics_history_by_deal description: | This model aggregates the monthly historic information regarding the different metrics computed at deal level. The primary sources of data are the `int_yyy__monthly_XXXXX_history_by_deal` models which contain the raw metrics data per source. Unlike the int_mtd_aggregated_metrics, this model does not abstract each metric, since no comparison versus last year is performed. In short, it just gathers the information stored in the abovementioned models. To keep in mind: aggregating the information of this model will not necessarily result into the int_mtd_aggregated metrics because 1) the mtd version contains more computing dates than the by deal version, the latest being a subset of the first, and 2) the deal based model enforces that a booking/guest journey/listing/etc has a host with a deal assigned, which is not necessarily the case. tests: - dbt_utils.unique_combination_of_columns: combination_of_columns: - date - id_deal columns: - name: date data_type: date description: The last day of the month or yesterday for historic metrics. tests: - not_null - name: id_deal data_type: character varying description: Id of the deal associated to the host. tests: - not_null - name: main_deal_name data_type: string description: | Main name for this ID deal. tests: - not_null - name: main_billing_country_iso_3_per_deal data_type: string description: | ISO 3166-1 alpha-3 main country code in which the Deal is billed. In some cases it's 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). - name: int_edeposit_and_athena_verifications description: "This table holds records on verifications for Guesty and Edeposit bookings. It contains details on validations checked on the guests, guest information and some booking details like checkin-checkout date or the status of the verification. The id values found here are completely unrelated to the ones found in Core DWH. Note that id_verifications and booking_id should normally be 1 to 1. Though there are exception, the API will accept a duplicate booking and the users will be charged for it. A duplicate would return a unique id_verification." columns: - name: id_verification data_type: text description: "unique Superhog generated id for this verification" tests: - unique - not_null - name: id_booking data_type: text description: "unique Superhog generated id for a booking. note that this could be duplicated and both will be charged, it's up to the user to no generate duplicate verifications" - name: id_user_partner data_type: text description: "unique Superhog generated id for partner" tests: - not_null - name: id_accommodation data_type: text description: "unique Superhog generated id for a listing" - name: version data_type: text description: "value to identify if it is Guesty (V1) or E-deposit (V2)" tests: - accepted_values: values: - V1 - V2 - name: verification_source data_type: text description: "source of the verification for the booking" tests: - accepted_values: values: - Guesty - Edeposit - name: verification_status data_type: text description: "status of the verification" - name: nightly_fee_local data_type: double precision description: "fee charged per night" - name: number_nights data_type: integer description: "number of nights for the booking" - name: total_fee_local data_type: double precision description: "total fee for the booking" - name: email_flag data_type: text description: "screening result for email" - name: phone_flag data_type: text description: "screening result for phone" - name: watch_list data_type: text description: "screening result of the guest" - name: channel data_type: text description: "" - name: checkin_at_utc data_type: timestamp without time zone description: "Timestamp of checkin for the booking" - name: checkin_date_utc data_type: date description: "Date of checkin for the booking" - name: checkout_at_utc data_type: timestamp without time zone description: "Timestamp of checkout for the booking" - name: checkout_date_utc data_type: date description: "Date of checkout for the booking" - name: is_cancelled data_type: boolean description: "" - name: cancelled_at_utc data_type: timestamp without time zone description: "Timestamp of cancellation of the booking" - name: cancelled_date_utc data_type: date description: "Date of cancellation for the booking" - name: user_email data_type: text description: "" - name: guest_email data_type: text description: "" - name: guest_last_name data_type: text description: "" - name: guest_first_name data_type: text description: "" - name: guest_telephone data_type: text description: "" - name: company_name data_type: text description: "" - name: property_manager_name data_type: text description: "" - name: property_manager_email data_type: text description: "" - name: listing_name data_type: text description: "" - name: listing_town data_type: text description: "" - name: listing_country data_type: text description: "" - name: listing_postcode data_type: text description: "" - name: pets_allowed data_type: boolean description: "" - name: level_of_protection_amount data_type: integer description: "" - name: level_of_protection_currency data_type: text description: "" - name: status_updated_at_utc data_type: timestamp without time zone description: "Timestamp when status was last updated" - name: status_updated_date_utc data_type: date description: "Date of last status update of the verification" - name: updated_at_utc data_type: timestamp without time zone description: "Timestamp of last updated of the verification" - name: updated_date_utc data_type: date description: "Date of last update of the verification" - name: athena_creation_at_utc data_type: timestamp without time zone description: "Athena timestamp referring to when the booking was created. It's provided by Guesty, but is not mandatory. In case of doubt use created_at_utc or created_date_utc fields" - name: athena_creation_date_utc data_type: date description: "Athena date referring to when the booking was created. It's provided by Guesty, but is not mandatory. In case of doubt use created_at_utc or created_date_utc fields" - name: created_at_utc data_type: timestamp without time zone description: "Timestamp of creation of the verification in the system" - name: created_date_utc data_type: date description: "Date of creation of the verification in the system"