Home Academy Login Try for free

retail

BETA

This module contains procedures to perform analysis to solve specific retail analytics use cases, such as revenue prediction.

BUILD_REVENUE_MODEL_DATA

BUILD_REVENUE_MODEL_DATA(stores_query, stores_variables, competitors_query, aoi_query, grid_type, grid_level, kring, decay, do_variables, do_source, custom_variables, custom_query, output_prefix)

Description

This procedure is the first step of the Revenue Prediction analysis workflow. It prepares the model data to be used in the training and prediction phases by performing the following steps:

  1. Polyfill the geometry from the area of interest using the grid type and resolution level.

  2. Enrich the grid cells with the revenue, stores, Data Observatory (DO) variables and custom variables.

  3. Apply a k-ring decay function to the enriched DO variables and custom variables. This operation smooths the features for a given cell by taking into account the values of these features in the neighboring cells (defined as those within the specified k-ring size), applying a scaling factor determined by the decay function of choice.

  4. Create the revenue revenue_model_data table (see the output description for more details).

  5. Create the revenue revenue_model_data_stats table (see the output description for more details).

Input parameters

  • stores_query: STRING query with variables related to the stores to be used in the model, including their revenue per store (required) and other variables (optional). It must contain the columns revenue (revenue of the store), store (store unique id) and geom (the geographical point of the store). The values of these columns cannot be NULL.

  • stores_variables: ARRAY<STRUCT<variable STRING, aggregation STRING>> list with the columns of the stores_query and their corresponding aggregation method (sum, avg, max, min, count) that will be used to enrich the grid cells. It can be set to NULL. The aggregation of the revenue (avg) and store (count) variables should not be included as they are performed anyway.

  • competitors_query: STRING query with the competitors information to be used in the model. It must contain the columns competitor (competitor store unique id) and geom (the geographical point of the store).

  • aoi_query: STRING query with the geography of the area of interest. It must contain a column geom with a single area (Polygon or MultiPolygon).

  • grid_type: STRING type of the cell grid. Supported values are h3, and quadbin.

  • grid_level: INT64 level or resolution of the cell grid. Check the available H3 levels, and Quadbin levels.

  • kring: INT64 size of the kring where the decay function will be applied. This value can be 0, in which case no kring will be computed and the decay function won't be applied.

  • decay: STRING decay function. Supported values are uniform, inverse, inverse_square and exponential. If set to NULL or '', uniform is used by default.

  • do_variables: ARRAY<STRUCT<variable STRING, aggregation STRING>> variables of the Data Observatory that will be used to enrich the grid cells and therefore train the revenue prediction model in the subsequent step of the Revenue Prediction workflow. For each variable, its slug and the aggregation method must be provided. Use default to use the variable's default aggregation method. Valid aggregation methods are: sum, avg, max, min, count. The catalog procedure DATAOBS_SUBSCRIPTION_VARIABLES can be used to find available variables and their slugs and default aggregation. It can be set to NULL.

  • do_source: STRING name of the location where the Data Observatory subscriptions of the user are stored, in <my-dataobs-project>.<my-dataobs-dataset> format. If only the <my-dataobs-dataset> is included, it uses the project carto-data by default. It can be set to NULL or ''.

  • custom_variables: ARRAY<STRUCT<variable STRING, aggregation STRING>> list with the columns of the custom_query and their corresponding aggregation method (sum, avg, max, min, count) that will be used to enrich the grid cells. It can be set to NULL.

  • custom_query: STRING query that contains a geography column geom and the columns with the custom data that will be used to enrich the grid cells. It can be set to NULL or ''.

  • output_prefix: STRING destination prefix for the output tables. It must contain the project, dataset and a prefix which will be prepended to each output tabla name. For example <my-project>.<my-dataset>.<output-prefix>.

Output

The procedure will output two tables:

  1. Model data table: contains an index column with the cell ids and all the enriched columns: revenue_avg, store_count, competitor_count, stores_variables suffixed by aggregation method, DO variables and custom variables. The name of the table includes the suffix _revenue_model_data, for example <my-project>.<my-dataset>.<output-prefix>_revenue_model_data.

  2. Model data stats table: contains the morans_i value computed for the revenue_avg column, computed with kring 1 and decay uniform. The name of the table includes the suffix _revenue_model_data_stats, for example <my-project>.<my-dataset>.<output-prefix>_revenue_model_data_stats.

Example

CALL `carto-un`.carto.BUILD_REVENUE_MODEL_DATA(
    -- Stores: revenue, store, geom and optional store information
    '''SELECT revenue, store, geom, store_area FROM `<project>.<dataset>.input_stores_data`''',
    -- Stores information variables
    [('store_area','sum')],
    -- Competitors: competitor, geom
    '''SELECT competitor, geom FROM `<project>.<dataset>.input_competitors_data`''',
    -- Area of interest: geom
    '''SELECT geom FROM `<project>.<dataset>.area_of_interest`''',
    -- Grid params: grid type and level
    'h3', 6,
    -- Decay params: kring size and decay function
    3, 'exponential',
    -- Data Observatory enrichment
    [('POPCY_4534fac4', 'sum'), ('INCCYPCAP_7c8377cf', 'avg')],
    '<my-dataobs-project>.<my-dataobs-dataset>',
    -- Custom data enrichment
    [('var1', 'sum'), ('var2', 'avg')],
    '''SELECT var1, var2, geom FROM `<project>.<dataset>.custom_data`''',
    -- Output destination prefix
    '<my-project>.<my-dataset>.<output-prefix>'
);
-- Table `<my-project>.<my-dataset>.<output-prefix>_revenue_model_data` will be created
-- with columns: index, revenue_avg, store_count, store_area_sum, competitor_count, POPCY_4534fac4_sum, INCCYPCAP_7c8377cf_avg, var1_sum, var2_avg
-- Table `<my-project>.<my-dataset>.<output-prefix>_revenue_model_data_stats` will be created
-- with the column: morans_i

BUILD_REVENUE_MODEL

BUILD_REVENUE_MODEL(revenue_model_data, options, output_prefix)

Description

This procedure is the second step of the Revenue Prediction analysis workflow. It creates the model and its description tables from the input model data (output of the BUILD_REVENUE_MODEL_DATA procedure). It performs the following steps:

  1. Compute the model from the input query and options.

  2. Compute the revenue model_shap, model_stats tables (see the output description for more details).

Input parameters

  • revenue_model_data: STRING table with the revenue model data generated with the BUILD_REVENUE_MODEL_DATA procedure.

  • options: STRING JSON string to overwrite the model default options. The following fixed options cannot be modified:

    • ENABLE_GLOBAL_EXPLAIN: TRUE

    • INPUT_LABEL_COLS: ['revenue_avg']

If set to NULL or empty, it will use the default options for each allowed model type, as detailed below. Models currently supported are BOOSTED_TREE_REGRESSOR (DEFAULT), RANDOM_FOREST_REGRESSOR and LINEAR_REG.

  • BOOSTED_TREE_REGRESSOR:

    • SUBSAMPLE: 0.85

    • EARLY_STOP: FALSE

    • MAX_ITERATIONS: 50

    • DATA_SPLIT_METHOD: NO_SPLIT

  • RANDOM_FOREST_REGRESSOR:

    • COLSAMPLE_BYTREE: 0.5

    • DATA_SPLIT_METHOD: NO_SPLIT

  • LINEAR_REG:

    • EARLY_STOP: FALSE

    • MAX_ITERATIONS: 50

    • DATA_SPLIT_METHOD: NO_SPLIT

Check the model documentation for more information.

  • output_prefix: STRING destination prefix for the output tables. It must contain the project, dataset and prefix. For example <my-project>.<my-dataset>.<output-prefix>.

Output

The procedure will output the following:

  1. Model: contains the trained model to be used for the revenue prediction. The name of the model includes the suffix _revenue_model, for example <my-project>.<my-dataset>.<output-prefix>_revenue_model.

  2. Shap table: contains a list of the features and their attribution to the model, computed with ML.GLOBAL_EXPLAIN. The name of the table includes the suffix _revenue_model_shap, for example <my-project>.<my-dataset>.<output-prefix>_revenue_model_shap.

  3. Stats table: contains the model stats (mean_error, variance, etc.), computed with ML.EVALUATE. The name of the table includes the suffix _revenue_model_stats, for example <my-project>.<my-dataset>.<output-prefix>_revenue_model_stats.

To learn more about how to evaluate the results of your model through the concept of explainability, refer to this article (https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-xai-overview).

Example

CALL `carto-un`.carto.BUILD_REVENUE_MODEL(
    -- Model data
    '<my-project>.<my-dataset>.<output-prefix>_revenue_model_data',
    -- Options
    '{"MAX_ITERATIONS": 100}',
    -- Output destination prefix
    '<my-project>.<my-dataset>.<output-prefix>'
);
-- Model `<my-project>.<my-dataset>.<output-prefix>_revenue_model` will be created
-- Table `<my-project>.<my-dataset>.<output-prefix>_revenue_model_shap` will be created
-- Table `<my-project>.<my-dataset>.<output-prefix>_revenue_model_stats` will be created

PREDICT_REVENUE_AVERAGE

PREDICT_REVENUE_AVERAGE(index, revenue_model, revenue_model_data, candidate_data, stores_variables)

Description

This procedure is the third and final step of the Revenue Prediction analysis workflow. It predicts the average revenue of an additional store located in the specified grid cell. It requires as input the model data (output of the BUILD_REVENUE_MODEL_DATA procedure) and the trained model (output of the BUILD_REVENUE_MODEL procedure).

Input parameters

  • index: ANY TYPE cell index where the new store will be located. It can be an H3 or a Quadbin index. For Quadbin, the value should be INT64 whereas for H3 the value should be STRING. It can also be 'ALL', in which case the prediction for all the grid cells of the model data are returned.

  • revenue_model: STRING the fully qualified model name.

  • revenue_model_data: STRING the fully qualified revenue_model_data table name.

  • candidate_data: STRING the fully qualified 1-row table containing the values for any of the stores_variables to be aggregated with the current values in the grid cell(s) specified by the index parameter. If set to NULL, only the store_count variable is considered, with 1 store added in the grid cell(s) specified by the index parameter.

  • stores_variables: ARRAY<STRUCT<variable STRING, aggregation STRING>> list with the columns of the stores_query and their corresponding aggregation method (sum, avg, max, min, count) that will be used for prediction. It can be set to NULL.

Output

The procedure will output the index, predicted_revenue_avg value in the cell (in the same units of the revenue column), and shap_values, an array of key value pairs with the shap values of the features for each prediction. It also includes a baseline_prediction, which is the expected revenue without considering the impact of any other features.

Example

CREATE TABLE '<my-project>.<my-dataset>.<output-prefix>_candidate_data'  AS (SELECT 25 store_area_sum);

CALL `carto-un`.carto.PREDICT_REVENUE_AVERAGE(
    '862676d1fffffff',
    '<my-project>.<my-dataset>.<output-prefix>_revenue_model',
    '<my-project>.<my-dataset>.<output-prefix>_revenue_model_data',
    '<my-project>.<my-dataset>.<output-prefix>_candidate_data',
    [('store_area','sum')]
);
-- index, predicted_revenue_avg

FIND_WHITESPACE_AREAS

FIND_WHITESPACE_AREAS(revenue_model, revenue_model_data, generator_query, aoi_query, minimum_revenue, max_results, with_own_stores, with_competitors)

Description

This is a postprocessing step that may be used after completing a Revenue Prediction analysis workflow. It allows you to identify cells with the highest potential revenue (whitespaces), while satisfying a series of criteria (e.g. presence of competitors).

It requires as input the model data (output of the BUILD_REVENUE_MODEL_DATA procedure) and the trained model (output of the BUILD_REVENUE_MODEL procedure), as well as a query with points to use as generators for the area of applicability of the model, plus a series of optional filters.

A cell is eligible to be considered a whitespace if it complies with the filtering criteria (minimum revenue, presence of competitors, etc.) and is within the area of applicability of the revenue model provided.

Input parameters

  • revenue_model: STRING with the fully qualified model name.

  • revenue_model_data: STRING with the fully qualified model_data table name.

  • generator_query: STRING query with the location of a set of generator points as a geography column named geom. The algorithm will look for whitespaces in the surroundings of these locations, therefore avoiding offering results in locations that are not of the interest of the user. Good options to use as generator locations are, for instance, the location of the stores and competitors, or a collection of POIs that are known to drive commercial activity to an area.

  • aoi_query: STRING query with the geography of the area of interest in which to perform the search. May be NULL, in which case no spatial filter will be applied.

  • minimum_revenue: FLOAT64 the minimum revenue to filter results by. May be NULL, in which case no revenue threshold will be applied.

  • max_results: INT64 of the maximum number of results, ordered by decreasing predicted revenue. May be NULL, in which case all eligible cells are returned.

  • with_own_stores: BOOL specifying whether to consider cells that already have own stores in them. If NULL, defaults to TRUE.

  • with_competitors: BOOL specifying whether to consider cells that already have competitors in them. If NULL, defaults to TRUE.

Output

The procedure will output a table of cells with the following columns:

  • index: identifying the H3, or Quadbin cell.

  • predicted_revenue_avg: average revenue of an additional store located in the grid cell.

  • store_count: number of own stores present in the grid cell.

  • competitor_count: number of competitors present in the grid cell.

Example

CALL `carto-un`.carto.FIND_WHITESPACE_AREAS(
    '<my-project>.<my-dataset>.<output-prefix>_model',
    '<my-project>.<my-dataset>.<output-prefix>_model_data',
    'SELECT geom FROM `<my-project>.<my-dataset>.<generator-table>`',
    'SELECT geom FROM `<my-project>.<my-dataset>.<area_of_interest_table>`', -- Area of Interest filter
    10000, -- Minimum predicted revenue filter
    5, -- Maximum number of results
    TRUE, -- Whether to include cells with own stores
    FALSE -- Whether to include cells with competitors
);

COMMERCIAL_HOTSPOTS

COMMERCIAL_HOTSPOTS(input, output, index_column, index_type, variable_columns, variable_weights, kring, pvalue_thresh)

Description

This procedure is used to locate hotspot areas by calculating a combined Getis-Ord Gi* statistic using a uniform kernel over several variables. The input data should be in either an H3 or Quadbin grid. The individual Gi* statistics are combined using Stouffer's Z-score method, which also allows to introduce individual weights, with the combined statistics following a standard normal distribution. The hotspots are identified as those cells with a positive combined the Gi* statistics which is significant at the specified significance level, i.e. whose p-value is below the p-value threshold (pvalue_thresh) set by the user.

Input parameters

  • input: STRING name of the table containing the input data. It should include project and dataset, i.e., follow the format <project-id>.<dataset-id>.<table-name>.

  • output: STRING name of the table where the output data will be stored. It should include project and dataset, i.e., follow the format <project-id>.<dataset-id>.<table-name>. If NULL, the procedure will return the output but it will not be persisted.

  • index_column: STRING name of the column containing the H3 or Quadbin indexes.

  • index_type: STRING type of the input cell indexes. Supported values are 'h3', or 'quadbin'.

  • variable_columns: ARRAY<STRING> names of the columns containing the variables to take into account when computing the combined Gi* statistic.

  • variable_weights: ARRAY<FLOAT64> containing the weights associated with each of the variables. These weights can take any value but will be normalized to sum up to 1. If NULL, uniform weights will be considered

  • kring: INT64 size of the k-ring (distance from the origin). This defines the area around each cell that will be taken into account to compute its Gi* statistic.

  • pvalue_thresh: Threshold for the Gi* value significance, ranging from 0 (most significant) to 1 (least significant). It defaults to 0.05. Cells with a p-value above this threshold won't be returned.

Output The output will contain the following columns:

  • index: STRING containing the cell index.

  • combined_gi: FLOAT64 with the resulting combined Gi*.

  • p_value: FLOAT64 with the p-value associated with the combined Gi* statistic.

If the output table is not specified when calling the procedure, the result will be returned but it won't be persisted.

Examples

CALL `carto-un`.carto.COMMERCIAL_HOTSPOTS(
    'project_id.dataset_id.my_input_table',
    'project_id.dataset_id.my_output_table',
    'index',
    'h3',
    ['feature_0', 'feature_1'],
    [0.7, 0.3],
     3,
     0.01
)
-- Table project_id.dataset_id.my_output_table will be created.
-- with columns: index, combined_gi, p_value
CALL `carto-un`.carto.COMMERCIAL_HOTSPOTS(
    'project_id.dataset_id.my_input_table',
    'project_id.dataset_id.my_output_table',
    'index',
    'quadbin',
    ['feature_0', 'feature_1'],
    [0.5, 0.5],
    1,
    0.05
);
-- Table project_id.dataset_id.my_output_table will be created.
-- with columns: index, combined_gi, p_value

Additional examples

BUILD_CANNIBALIZATION_DATA

BUILD_CANNIBALIZATION_DATA(grid_type, store_query, resolution, method, do_variables, do_urbanity_index, do_source, output_destination, output_prefix, options)

Description

This procedure is the first of two from the Cannibalization analysis workflow. It builds the dataset for the existing locations to be used by the procedure CANNIBALIZATION_OVERLAP to estimate the overlap between existing stores and the potentially new ones.

  1. For each store location, the urbanity level based on CARTO Spatial Features dataset is retrieved.

  2. For each store location, given the radius specified, the cells of the influence area are found.

  3. All cells are enriched with the specified features from Data Observatory subscriptions (e.g. population, footfall, etc.).

  4. A table with store_id, cell_id, and features values are created.

For the isoline method, the use of this procedure requires providing authorization credentials. Two parameters are needed: api_base_url and api_access_token. Both the API base url and your API access token can be accessed through the developers section of the CARTO user interface. Please check our documentation for Developers for more details.

Input parameters

  • grid_type: STRING type of the cell grid. Supported values are h3 and quadbin.

  • store_query: STRING query with variables related to the stores to be used in the model, including their id and location. It must contain the columns store_id (store unique id) and geom (the geographical point of the store). Optionally it can contain a third column custom_geom which is the custom Polygon that represents the trade area for each location. It is used only when method is set to custom. The values of these columns cannot be NULL.

  • resolution: INT64 level or resolution of the cell grid. Check the available H3 levels and Quadbin levels.

  • method: STRING indicates the method of trade area generation. Three options available: buffer, kring, isoline and custom. This method applies to all locations provided.

  • do_variables: ARRAY<STRUCT<variable STRING, aggregation STRING>> variables of the Data Observatory that will be used to enrich the grid cells and therefore compute the overlap between store locations in the subsequent step of the Cannibalization workflow. For each variable, its slug and the aggregation method must be provided. Use default to use the variable's default aggregation method. Valid aggregation methods are: sum, avg, max, min, count. The catalog procedure DATAOBS_SUBSCRIPTION_VARIABLES can be used to find available variables and their slugs and default aggregation. It can be set to NULL.

  • do_urbanity_index: STRING|NULL urbanity index variable slug_id in a CARTO Spatial Features subscription from the Data Observatory. If set to NULL then the urbanity is not considered and only one distance, the first one from the options arguments is being taken into account.

  • do_source: STRING name of the location where the Data Observatory subscriptions of the user are stored, in <my-dataobs-project>.<my-dataobs-dataset> format. If only the <my-dataobs-dataset> is included, it uses the project carto-data by default. It can be set to NULL or ''.

  • output_destination: STRING destination dataset the output tables. It must contain the project and dataset. For example <my-project>.<my-dataset>.

  • output_prefix: STRING prefix for the output table.

  • options: JSON A JSON string containing the required parameters for the specified method. For buffer: {distances: [radius km for Remote/Rural/Low_density_urban, radius km for Medium_density_urban, radius km for High/Very_High_density_urban locations] - ARRAY<FLOT64>}, kring:{distances: [number of layers for Remote/Rural/Low_density_urban, number of layers for Medium_density_urban, number of layers for High/Very_High_density_urban locations] - INT64} - ARRAY<INT64>}, isoline : {mode: type of transport. Supported: 'walk', 'car' - [type for Remote/Rural/Low_density_urban, type for Medium_density_urban, type for High/Very_High_density_urban locations] ARRAY<STRING>, time: range of the isoline in seconds - [seconds for Remote/Rural/Low_density_urban, seconds for Medium_density_urban, seconds for High/Very_High_density_urban locations] ARRAY<INT64>, api_base_url : url of the API where the customer account is stored - STRING, api_access_token: an API Access Token that is allowed to use the LDS API - STRING}.

Output

This procedure will output one table:

  • Table containing the store_id, cell_id, distance from store_id (integer), the values for each Data Observatory feature, method type and parameters for each method. The output table can be found at the output destination with name <output-prefix>_output. Overall path <my-project>.<my-dataset>.<output-prefix>_output.

Examples

CALL `carto-un`.carto.BUILD_CANNIBALIZATION_DATA(
    --grid_type
    'h3',
    --store_query
    '''SELECT store_id, geom, FROM `<project>.<dataset>.<table_name_with_stores>`''',
    --resolution
    8,
    --method,
    'buffer',
    --do_variables
    [('population_f5b8d177','sum')],
    --do_urbanity_index
    'urbanity_e1a58891',
    --do_source
    '<my-dataobs-project>.<my-dataobs-dataset>',
    --output_destination
    '<my-project>.<my-dataset>',
    --output_prefix
    '<output-prefix>',
    --options
    '{"distances":[5., 3., 1.]}'
);
CALL `carto-un`.carto.BUILD_CANNIBALIZATION_DATA(
    --grid_type
    'h3',
    --store_query
    '''SELECT store_id, geom, FROM `<project>.<dataset>.<table_name_with_stores>`''',
    --resolution
    8,
    --method,
    'kring',
    --do_variables
    [('population_f5b8d177','sum')],
    --do_urbanity_index
    'urbanity_e1a58891',
    --do_source
    '<my-dataobs-project>.<my-dataobs-dataset>',
    --output_destination
    '<my-project>.<my-dataset>',
    --output_prefix
    '<output-prefix>',
    --options
    '{"distances":[10, 5, 3]}',
);
CALL `carto-un`.carto.BUILD_CANNIBALIZATION_DATA(
    --grid_type
    'h3',
    --store_query
    '''SELECT store_id, geom, FROM `<project>.<dataset>.<table_name_with_stores>`''',
    --resolution
    8,
    --method,
    'isoline',
    --do_variables
    [('population_f5b8d177','sum')],
    --do_urbanity_index
    'urbanity_e1a58891',
    --do_source
    '<my-dataobs-project>.<my-dataobs-dataset>',
    --output_destination
    '<my-project>.<my-dataset>',
    --output_prefix
    '<output-prefix>',
    --options
    '{"modes":[car, car, walk], "distances":[600, 600, 300], "api_base_url":"your url", "api_access_token":"your token"}'
);
CALL `carto-un`.carto.BUILD_CANNIBALIZATION_DATA(
    --grid_type
    'h3',
    --store_query
    '''SELECT store_id, geom, custom_geom FROM `<project>.<dataset>.<table_name_with_stores>`''',
    --resolution
    8,
    --method,
    'custom',
    --do_variables
    [('population_f5b8d177','sum')],
    --do_urbanity_index
    NULL,
    --do_source
    '<my-dataobs-project>.<my-dataobs-dataset>',
    --output_destination
    '<my-project>.<my-dataset>',
    --output_prefix
    '<output-prefix>',
    --options
    NULL
);

Additional examples

CANNIBALIZATION_OVERLAP

CANNIBALIZATION_OVERLAP(data_table, new_locations_query, method, do_urbanity_index, do_source, output_destination, output_prefix, options)

Description

This procedure is the second step of the Cannibalization analysis workflow. It takes as input the generated table from BUILD_CANNIBALIZATION_DATA and the location of the new store, and estimates the overlap of areas and spatial features that the new store would have with the existing stores included into the generated table.

For the isoline method, the use of this procedure requires providing authorization credentials. Two parameters are needed: api_base_url and api_access_token. Both the API base url and your API access token can be accessed through the developers section of the CARTO user interface. Please check our documentation for Developers for more