Google Merchant Center transfers

Beta

This product is covered by the Pre-GA Offerings Terms of the Google Cloud Platform Terms of Service. Pre-GA products may have limited support, and changes to pre-GA products may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

The BigQuery Data Transfer Service for Google Merchant Center allows you to automatically schedule and manage recurring load jobs for Google Merchant Center reporting data.

Supported reports

The BigQuery Data Transfer Service for Google Merchant Center currently supports the following data:

Products and product issues

• This includes product data provided to Merchant Center through feeds or via the Content API for Shopping. It also includes item level issues detected by Google for your products. This data can be viewed in the Google Merchant Center or by querying the Content API for Shopping.

• For information on how this data is loaded into BigQuery, see the Google Merchant Center product table schema.

Price benchmarks

• This includes product level price benchmark data and is based on the same definitions as the price competitiveness report available through Google Merchant Center.

• For information on how this data is loaded into BigQuery, see the Google Merchant Center price benchmarks table schema.

Best sellers

• This includes data about the most popular products and brands in Shopping ads and unpaid listings, as well as whether or not you have them in your inventory.

• It is based on the best sellers report available through Google Merchant Center.

• Best sellers reports are composed of the following tables:

  1. BestSellers_TopProducts_ is a ranked list of top products per country and Google product category. This table represents data from all merchants across Shopping ads and unpaid listings.
  2. BestSellers_TopProducts_Inventory_ is a mapping table between top products and your inventory.
  3. BestSellers_TopBrands_ is a ranked list of top brands, computed per country and Google product category. It represents data from all merchants across Shopping ads and unpaid listings.

• For information on how best sellers data is loaded into BigQuery, see the Google Merchant Center top products, product inventory, and top brands table schemas.

The BigQuery Data Transfer Service for Google Merchant Center currently supports the following reporting options:

Reporting option	Support
Schedule	Configurable to daily, weekly, monthly, or custom. By default, this is set to daily when the transfer is created. The minimum interval between transfers is 6 hours.

Limitations

All Merchant Center transfers are subject to the following limitations:

• After creating a transfer for your Merchant ID, you may need to wait up to 90 minutes before your Merchant account data is prepared and available for transfer.
• You cannot configure a maximum backfill duration. Historical backfills are currently unsupported. You can set up a backfill only for today.

Product and product issues

• Products and product issues data in BigQuery does not represent the real-time view of your Merchant account. To see the timeline of your data, refer to the product_data_timestamp field in the schema.

Price benchmarks

• Price benchmarks data does not represent the real-time view of price benchmarks. To see the timeline of your data, refer to the price_benchmark_timestamp field in the Price Benchmarks schema.
• Not all products will have a price benchmark.

Best sellers

• Not all countries will contain ranking data. Countries included in the report: US, DE, FR, UK, NL, JP, AU, CA, SE, IT, ES, CH, DK, CZ, BE, and RU.
• Best sellers data does not represent a real-time view of brand rankings. To see the timeline of your data, refer to the rank_timestamp field in the BestSellers_TopProducts_ and BestSellers_TopBrands_ schema.
• Only the most popular items in Shopping ads and unpaid listings are included in the BestSellers_TopProducts_ table.
• Only the most popular brands in Shopping ads and unpaid listings are included in the BestSellers_TopBrands_ table. There may be rows in the Products_ table with a google_brand_id that does not map to any brands in the BestSellers_TopBrands_ table.
• Brand IDs in the google_brand_id field might change over time to reflect the changing nature of businesses and brands. Because of this, tracking a set of brands over a long period of time is not guaranteed.
• While brands are ranked across many different categories, all products in the Products_ table are in leaf categories. To join brands and products on non-leaf categories, use the google_product_category_ids field, as shown in Best sellers sample queries.

Note: For more information about product categories, including a full list of category codes, see Definition:google_product_category.

Multi-client account (MCA) support

Existing customers with multiple Merchant IDs are encouraged to configure a parent Multi-Client Account (MCA). Configuring an MCA allows you to create a single transfer for all your Merchant IDs.

Using Google Merchant Center MCAs provides several benefits over using individual Merchant IDs:

• You no longer need to manage multiple transfers to transfer reporting data for multiple Merchant IDs.
• Queries involving multiple Merchant IDs are much simpler to write because all Merchant ID data is stored in the same table.
• Using MCAs alleviates potential BigQuery load job quota issues because all your Merchant ID data is loaded in the same job.

One possible disadvantage of using MCAs is that your subsequent query costs are likely to be higher. Because all of your data is stored in the same table, queries that retrieve data for an individual Merchant ID must still scan the entire table.

Note: The BigQuery Data Transfer Service pulls reports for all listed Merchant IDs. If there are no products in Google Shopping for a specific day, you may not see Merchant IDs in the BigQuery table.

If you are using an MCA, the MCA ID is listed under aggregator_id and the individual sub-accounts are listed under merchant_id. For accounts that do not use an MCA, aggregator_id is set to null.

Before you begin

Before you create a Google Merchant Center transfer:

• Verify that you have completed all actions required to enable the BigQuery Data Transfer Service.
• Create a BigQuery dataset to store the Google Merchant Center data.
• If you intend to setup transfer run notifications for Pub/Sub, you must have pubsub.topics.setIamPolicy permissions. Pub/Sub permissions are not required if you just set up email notifications. For more information, see BigQuery Data Transfer Service Run Notifications.

Required permissions

Ensure that the person creating the transfer has the following required permissions:

• BigQuery:

  • bigquery.transfers.update permissions to create the transfer
  • bigquery.datasets.update permissions on the target dataset

  The bigquery.admin predefined IAM role includes bigquery.transfers.update and bigquery.datasets.update permissions. For more information on IAM roles in BigQuery Data Transfer Service, see Access control reference.

• Google Merchant Center:

  • Access to the Google Merchant Center account that is used in the transfer configuration. You can verify access by clicking the Users section in the Google Merchant Center UI.

  • To access price competitiveness and best sellers data, you must first enable the Market Insights Program.

Setting up a Google Merchant Center transfer

Setting up a data transfer for Google Merchant Center reporting requires the following:

• Merchant ID or Multi-client account ID: This is the Merchant ID shown in the Google Merchant Center UI.

To create a data transfer for Google Merchant Center reporting:

Console

1. Go to the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Click Transfers.

3. Click Create Transfer.

4. On the Create Transfer page:

   • In the Source type section, for Source, choose Google Merchant Center.

   • In the Transfer config name section, for Display name, enter a name for the transfer such as My Transfer. The transfer name can be any value that allows you to easily identify the transfer if you need to modify it later.

     

   • In the Schedule options section, for Schedule, click Start at a set time.

     • For Repeats, choose an option for how often to run the transfer.

       • Daily (default)
       • Weekly
       • Monthly
       • Custom
       • On-demand

     • For Start date and run time, enter the date and time to start the transfer. This value should be at least 90 minutes from the current UTC time. If you chose Start now, this option is disabled.

       

       If you leave the schedule options set to Start now, the first transfer run will start immediately, and it will fail with the following error message: No data to transfer found for the Merchant account. If you have just created this transfer, you may need to wait for up to 90 minutes before the data of your Merchant account are prepared and available for the transfer. If this occurs, wait for 90 minutes and then set up a backfill for today or wait until tomorrow for the next scheduled run.

   • In the Destination settings section, for Destination dataset, choose the dataset you created to store your data.

     

   • In the Data source details section, for Merchant ID, enter your Merchant ID or MCA ID.

     Select the report(s) that you would like to transfer. See Supported Reports for more details.

     

   • (Optional) In the Notification options section:

     • Click the toggle to enable email notifications. When you enable this option, the transfer administrator receives an email notification when a transfer run fails.
     • For Select a Pub/Sub topic, choose your topic name or click Create a topic. This option configures Pub/Sub run notifications for your transfer.

5. Click Save.

bq

Enter the bq mk command and supply the transfer creation flag — --transfer_config. The following flags are also required:

• --data_source
• --target_dataset
• --display_name
• --params

You can also supply the --project_id flag to specify a particular project. If --project_id isn't specified, the default project is used.

bq mk \
--transfer_config \
--project_id=project_id \
--target_dataset=dataset \
--display_name=name \
--params='parameters' \
--data_source=data_source

Where:

• project_id is your project ID.
• dataset is the target dataset for the transfer configuration.
• name is the display name for the transfer configuration. The transfer name can be any value that allows you to easily identify the transfer if you need to modify it later.
• parameters contains the parameters for the created transfer configuration in JSON format. For example: --params='{"param":"param_value"}'.
  • For Google Merchant Center transfers, you must supply the merchant_id parameter.
  • The export_products parameter specifies whether to transfer product and product issues data. This parameter is included by default, even if you don't specify the export_products parameter. Google recommends that you include this parameter explicitly and set it to "true".
  • The export_price_benchmarks parameter specifies whether to transfer price benchmarking data.
  • The export_best_sellers parameter specifies whether to transfer best sellers data.
• data_source is the data source — merchant_center.

Caution: Currently, you cannot configure notifications by using the command-line tool.

For example, the following command creates a Google Merchant Center transfer named My Transfer using Merchant ID 1234 and target dataset mydataset. The transfer is created in your default project.

bq mk \
--transfer_config \
--target_dataset=mydataset \
--display_name='My Transfer' \
--params='{"merchant_id":"1234","export_products":"true","export_price_benchmarks":"true","export_best_sellers":"true"}' \
--data_source=merchant_center

The first time you run the command, you receive a message like the following:

[URL omitted] Please copy and paste the above URL into your web browser and follow the instructions to retrieve an authentication code.

Follow the instructions in the message and paste the authentication code on the command line.

Caution: When you create a Google Merchant Center transfer by using the command-line tool, the transfer configuration is set up using the default values for Schedule (every 24 hours at creation time). The first transfer run will start immediately, and it will fail with the following error message: `No data to transfer found for the Merchant account.

If you have just created this transfer, you may need to wait for up to 90 minutes before the data of your Merchant account are prepared and available for the transfer.` If this occurs, wait for 90 minutes and then set up a backfill for today or wait until tomorrow for the next scheduled run.

API

Use the projects.locations.transferConfigs.create method and supply an instance of the TransferConfig resource.

Troubleshooting Google Merchant Center transfer setup

If you are having issues setting up your transfer, see Google Merchant Center transfer issues in Troubleshooting BigQuery Data Transfer Service transfer setup.

Querying your data

When your data is transferred to BigQuery, the data is written to ingestion-time partitioned tables. For more information, see Introduction to partitioned tables.

When you query your Google Merchant Center table, you must use the _PARTITIONTIME or _PARTITIONDATE pseudo-column in your query. For more information, see Querying partitioned tables.

The Products_ table contains nested and repeated fields. For information on handling nested and repeated data, see Differences in repeated field handling in the standard SQL documentation.

Google Merchant Center sample queries

You can use the following Google Merchant Center sample queries to analyze your transferred data. You can also use the queries in a visualization tool such as Google Data Studio.

In each of the following queries, replace dataset with your dataset name. Replace merchant_id with your Merchant ID. If you're using an MCA, replace merchant_id with your MCA ID.

Products and product issues sample queries

Products and product issues statistics

The following sample query provides the number of products, products with issues, and issues by day.

Console

SELECT
  _PARTITIONDATE AS date,
  COUNT(*) AS num_products,
  COUNTIF(ARRAY_LENGTH(issues) > 0) AS num_products_with_issues,
  SUM(ARRAY_LENGTH(issues)) AS num_issues
FROM
  dataset.Products_merchant_id
WHERE
  _PARTITIONDATE >= 'YYYY-MM-DD'
GROUP BY
  date
ORDER BY
  date DESC

bq

bq query --use_legacy_sql=false '
SELECT
  _PARTITIONDATE AS date,
  COUNT(*) AS num_products,
  COUNTIF(ARRAY_LENGTH(issues) > 0) AS num_products_with_issues,
  SUM(ARRAY_LENGTH(issues)) AS num_issues
FROM
  dataset.Products_merchant_id
WHERE
  _PARTITIONDATE >= 'YYYY-MM-DD'
GROUP BY
  date
ORDER BY
  date DESC'

Products disapproved for Shopping Ads

The following sample query provides the number of products that are not approved for display in Shopping Ads. Disapproval can be a result of being excluded or because of an issue with the product.

Console

SELECT
  _PARTITIONDATE AS date,
  COUNT(*) AS num_products
FROM
  dataset.Products_merchant_id,
  UNNEST(destinations) AS destination
WHERE
  _PARTITIONDATE >= 'YYYY-MM-DD' AND
  destination.status = 'disapproved'
GROUP BY
  date
ORDER BY
  date DESC

bq

bq query --use_legacy_sql=false '
SELECT
  _PARTITIONDATE AS date,
  COUNT(*) AS num_products
FROM
  dataset.Products_merchant_id,
  UNNEST(destinations) AS destination
WHERE
  _PARTITIONDATE >= 'YYYY-MM-DD' AND
  destination.status = 'disapproved'
GROUP BY
  date
ORDER BY
  date DESC'

Products with disapproved issues

The following sample query retrieves the number of products with disapproved issues.

Console

SELECT
  _PARTITIONDATE AS date,
  COUNT(DISTINCT CONCAT(CAST(merchant_id AS STRING), ':', product_id))
      AS num_distinct_products
FROM
  dataset.Products_merchant_id,
  UNNEST(issues) AS issue
WHERE
  _PARTITIONDATE >= 'YYYY-MM-DD' AND
  issue.servability = 'disapproved'
GROUP BY
  date
ORDER BY
  date DESC

bq

bq query --use_legacy_sql=false '
SELECT
  _PARTITIONDATE AS date,
  COUNT(DISTINCT CONCAT(CAST(merchant_id AS STRING), ':', product_id))
      AS num_distinct_products
FROM
  dataset.Products_merchant_id,
  UNNEST(issues) AS issue
WHERE
  _PARTITIONDATE >= 'YYYY-MM-DD' AND
  issue.servability = 'disapproved'
GROUP BY
  date
ORDER BY
  date DESC'

Note: This query constructs a unique key by using merchant_id and product_id. This is only required if you have an MCA account. When you use an MCA account, there is the potential for product_id collisions across multiple sub-accounts.

Price benchmark sample queries

Compare product prices to benchmarks

The following query joins Products and Price Benchmarks data to return the list of products and associated benchmarks.

Console

WITH products AS
(
  SELECT
    _PARTITIONDATE AS date,
    *
  FROM
    dataset.Products_merchant_id
  WHERE
   _PARTITIONDATE >= 'YYYY-MM-DD'
),
benchmarks AS
(
  SELECT
    _PARTITIONDATE AS date,
    *
  FROM
    dataset.Products_PriceBenchmarks_merchant_id
  WHERE
    _PARTITIONDATE >= 'YYYY-MM-DD'
)
SELECT
  products.date,
  products.product_id,
  products.merchant_id,
  products.aggregator_id,
  products.price,
  products.sale_price,
  benchmarks.price_benchmark_value,
  benchmarks.price_benchmark_currency,
  benchmarks.country_of_sale
FROM
  products
INNER JOIN
  benchmarks
ON products.product_id = benchmarks.product_id AND
   products.merchant_id = benchmarks.merchant_id AND
   products.date = benchmarks.date

bq

bq query --use_legacy_sql=false '
WITH products AS
(
  SELECT
    _PARTITIONDATE AS date,
    *
  FROM
    `dataset.Products_merchant_id`
  WHERE
   _PARTITIONDATE >= 'YYYY-MM-DD'
),
benchmarks AS
(
  SELECT
    _PARTITIONDATE AS date,
    *
  FROM
    `dataset.Products_PriceBenchmarks_merchant_id`
  WHERE
   _PARTITIONDATE >= 'YYYY-MM-DD'
)
SELECT
  products.date,
  products.product_id,
  products.merchant_id,
  products.aggregator_id,
  products.price,
  products.sale_price,
  benchmarks.price_benchmark_value,
  benchmarks.price_benchmark_currency,
  benchmarks.country_of_sale
FROM
  products
INNER JOIN
  benchmarks
ON products.product_id = benchmarks.product_id AND
   products.merchant_id = benchmarks.merchant_id AND
   products.date = benchmarks.date'

Notes on joining the data:
1. Not all products have benchmarks, so use INNER JOIN or LEFT JOIN accordingly.
2. Each product may have multiple benchmarks (one per country).

Best sellers sample queries

Top products for a given category and country

The following query returns top products for the smartphone category in the US.

Console

SELECT
  rank,
  previous_rank,
  (SELECT name FROM top_products.product_title WHERE locale = 'en-US') AS product_title,
  brand,
  price_range
FROM
  dataset.BestSellers_TopProducts_merchant_id AS top_products
WHERE
  _PARTITIONDATE = 'YYYY-MM-DD' AND
  ranking_category = 267 /*Smartphones*/ AND
  ranking_country = 'US'
ORDER BY
  rank

bq

bq query --use_legacy_sql=false '
SELECT
  rank,
  previous_rank,
  (SELECT name FROM top_products.product_title WHERE locale = 'en-US') AS product_title,
  brand,
  price_range
FROM
  dataset.BestSellers_TopProducts_merchant_id AS top_products
WHERE
  _PARTITIONDATE = 'YYYY-MM-DD' AND
  ranking_category = 267 /*Smartphones*/ AND
  ranking_country = 'US'
ORDER BY
  rank'

Note: For more information about product categories, including a full list of category codes, see Definition:google_product_category.

Top products in your inventory

The following query joins BestSellers_TopProducts_Inventory_ and BestSellers_TopProducts_ data to return a list of top products you currently have in your inventory.

Console

WITH latest_top_products AS
(
  SELECT
    *
  FROM
    dataset.BestSellers_TopProducts_merchant_id
  WHERE
    _PARTITIONDATE = 'YYYY-MM-DD'
),
latest_top_products_inventory AS
(
  SELECT
    *
  FROM
    dataset.BestSellers_TopProducts_Inventory_merchant_id
  WHERE
    _PARTITIONDATE = 'YYYY-MM-DD'
)
SELECT
  top_products.rank,
  inventory.product_id,
  (SELECT ANY_VALUE(name) FROM top_products.product_title) AS product_title,
  top_products.brand,
  top_products.gtins
FROM
  latest_top_products AS top_products
INNER JOIN
  latest_top_products_inventory AS inventory
USING (rank_id)

bq

bq query --use_legacy_sql=false '
WITH latest_top_products AS
(
  SELECT
    *
  FROM
    dataset.BestSellers_TopProducts_merchant_id
  WHERE
    _PARTITIONDATE = 'YYYY-MM-DD'
),
latest_top_products_inventory AS
(
  SELECT
    *
  FROM
    dataset.BestSellers_TopProducts_Inventory_merchant_id
  WHERE
    _PARTITIONDATE = 'YYYY-MM-DD'
)
SELECT
  top_products.rank,
  inventory.product_id,
  (SELECT ANY_VALUE(name) FROM top_products.product_title) AS product_title,
  top_products.brand,
  top_products.gtins
FROM
  latest_top_products AS top_products
INNER JOIN
  latest_top_products_inventory AS inventory
USING (rank_id)'

Top brands for a given category and country

The following query returns top brands for the smartphone category in the US.

Console

SELECT
  rank,
  previous_rank,
  brand
FROM
  dataset.BestSellers_TopBrands_merchant_id
WHERE
  _PARTITIONDATE = 'YYYY-MM-DD' AND
  ranking_category = 267 /*Smartphones*/ AND
  ranking_country = 'US'
ORDER BY
  rank

bq

  bq query --use_legacy_sql=false '
    SELECT
      rank,
      previous_rank,
      brand
    FROM
      dataset.BestSellers_TopBrands_merchant_id
    WHERE
      _PARTITIONDATE = 'YYYY-MM-DD' AND
      ranking_category = 267 /*Smartphones*/ AND
      ranking_country = 'US'
    ORDER BY
      rank'

Note: For more information about product categories, including a full list of category codes, see Definition:google_product_category.

Products of top brands in your inventory

The following query returns a list of products in your inventory from top brands, listed by category and country.

Console

  WITH latest_top_brands AS
  (
    SELECT
      *
    FROM
      dataset.BestSellers_TopBrands_merchant_id
    WHERE
      _PARTITIONDATE = 'YYYY-MM-DD'
  ),
  latest_products AS
  (
    SELECT
      product.*,
      product_category_id
    FROM
      dataset.Products_merchant_id AS product,
      UNNEST(product.google_product_category_ids) AS product_category_id
    WHERE
      _PARTITIONDATE = 'YYYY-MM-DD'
  )
  SELECT
    top_brands.brand,
    (SELECT name FROM top_brands.ranking_category_path
    WHERE locale = 'en-US') AS ranking_category,
    top_brands.ranking_country,
    top_brands.rank,
    products.product_id,
    products.title
  FROM
    latest_top_brands AS top_brands
  INNER JOIN
    latest_products AS products
  ON top_brands.google_brand_id = products.google_brand_id AND
     top_brands.ranking_category = product_category_id AND
     top_brands.ranking_country = products.target_country

bq

bq query --use_legacy_sql=false '
    WITH latest_top_brands AS
  (
    SELECT
      *
    FROM
      dataset.BestSellers_TopBrands_merchant_id
    WHERE
      _PARTITIONDATE = 'YYYY-MM-DD'
  ),
  latest_products AS
  (
    SELECT
      product.*,
      product_category_id
    FROM
      dataset.Products_merchant_id AS product,
      UNNEST(product.google_product_category_ids) AS product_category_id
    WHERE
      _PARTITIONDATE = 'YYYY-MM-DD'
  )
  SELECT
    top_brands.brand,
    (SELECT name FROM top_brands.ranking_category_path
    WHERE locale = 'en-US') AS ranking_category,
    top_brands.ranking_country,
    top_brands.rank,
    products.product_id,
    products.title
  FROM
    latest_top_brands AS top_brands
  INNER JOIN
    latest_products AS products
  ON top_brands.google_brand_id = products.google_brand_id AND
     top_brands.ranking_category = product_category_id AND
     top_brands.ranking_country = products.target_country'

Note: For more information about product categories, including a full list of category codes, see Definition:google_product_category.