Retail is being renamed to Vertex AI Search for retail. We are in the process of updating content to reflect the new branding.

Export your analytics metrics into BigQuery

This page describes how to export Vertex AI Search for retail analytics metrics into BigQuery. Exporting analytics metrics lets you retain the metrics and write SQL for your own analysis.

Vertex AI Search for retail analytics metrics are generated automatically for you. For more information about measuring the business impact of Vertex AI Search for retail with analytics metrics, see View analytics.

Exporting metrics data to BigQuery has the following limitations:

The data that is exported to BigQuery tables in your Vertex AI Search for retail project are authorized views, not materialized views.
You cannot change or update the data in these tables. If you need to change or update your data, export or copy the view to another destination.
You can't export metrics for user events that are older than 180 days or metrics that were generated more than 180 days ago. Any metrics generated before the 180-day window are not retrievable.

Before you begin

Before you can export your metrics into BigQuery, you must have completed the procedures in Before you begin. This includes importing your user events and, optionally, your catalog information.

For more information about importing, see:

Create a dataset in BigQuery

You need to create a dataset in BigQuery to hold your analytics metrics data.

You must create the datasets in the same project where you implemented Vertex AI Search for retail.

If you haven't used BigQuery in your project before, enable the BigQuery API and make sure that you have the IAM role that lets you create datasets and tables.

See Before you begin and Access control with IAM

in the BigQuery documentation.
Create a dataset in BigQuery in the US (multiple regions in the United States) multi-region. For example, name it retail_data. If you create your dataset in a different region, exporting your data into BigQuery fails. CMEK for the dataset is not supported; enabling it causes the export to fail.

For information about creating a dataset, see Creating datasets in the BigQuery documentation.

This dataset will be used to hold the data table(s) that you export. The following procedures describe how to export.

Export your analytics metrics to a BigQuery table

Use the exportAnalyticsMetrics method to export your retail catalog into a BigQuery table.

Before using any of the request data, make the following replacements:

PROJECT_ID: The ID of the Vertex AI Search for retail API project where you created the BigQuery dataset.
DATASET_ID: The name of the dataset that you created in Create a dataset in BigQuery. For example, use retail_data or retail_metrics_data.
TABLE_ID_PREFIX: A prefix for the table ID. This prefix can't be an empty string. A suffix of retail_analytics_metrics is added to complete the table name. For example, if the prefix is test, the table is named test_retail_analytics_metrics.

Request JSON body:

{
  "catalog": "projects/PROJECT_ID/locations/global/catalogs/default_catalog",
  "outputConfig":
  {
    "bigqueryDestination":
    {
      "datasetId": "DATASET_ID",
        "tableIdPrefix": "TABLE_ID_PREFIX",
        "tableType": "view"
    }
  }
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "catalog": "projects/PROJECT_ID/locations/global/catalogs/default_catalog",
  "outputConfig":
  {
    "bigqueryDestination":
    {
      "datasetId": "DATASET_ID",
        "tableIdPrefix": "TABLE_ID_PREFIX",
        "tableType": "view"
    }
  }
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog:exportAnalyticsMetrics"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "catalog": "projects/PROJECT_ID/locations/global/catalogs/default_catalog",
  "outputConfig":
  {
    "bigqueryDestination":
    {
      "datasetId": "DATASET_ID",
        "tableIdPrefix": "TABLE_ID_PREFIX",
        "tableType": "view"
    }
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog:exportAnalyticsMetrics" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/global/catalogs/default_catalog/operations/12310973052792775280",
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.retail.v2.ExportAnalyticsMetricsResponse",
    "outputResult": {
      "bigqueryResult": [
        {
          "datasetId": "DATASET_ID",
          "tableId": "TABLE_ID_PREFIX_retail_analytics_metrics"
        }
      ]
    }
  }
}

Metrics definitions

The exported metrics are the count metrics. There can be slight drift for some count metrics because bot filtering might exclude some events from metrics computation. Bot filtering can help deliver more accurate performance measurements. This does not affect A/B testing, which depends on the dimension external_experiment_ids, whereas bot filtering is applied to all traffic.

Metrics dimensions

Count metrics are the raw counts directly aggregated from relevant user events with the same dimensions. To interpret metrics, you need to know which dimensions the metrics are based upon. For the purpose of analytics metrics, search and browse events are referred to as impression events, and detail_page_view, add_to_cart and purchase events are referred to as conversion events. The following table defines metric dimensions.

Metric Dimension	Description
date	The date of the relevant event(s) that generated the metrics. For impression metrics, this is the date of the impression events. For session and visitor metrics, this is the date of the first impression event of the session/visitor.
external_experiment_ids	The experiment IDs of the relevant event(s) that generated the metrics. For impression metrics, this the experiment ID of the impression event. For session and visitor metrics, this is the concatenation of the unique external experiment ids of all impression events that appears within the session/visitor. For example, if there are two search events within a session/visitor, the first search event has external_experiment_ids 'test_arm_1,test_arm_2', and the second search event has external_experiment_ids 'test_arm_1,test_arm_3', then external_experiment_ids of the session/visitor is 'test_arm_1,test_arm_2,test_arm_3'
device_type	The device type of the relevant event(s) that generated the metrics, for example, if the event is generated from a mobile or desktop device. For impression metrics, this is the device type of the impression event. For session and visitor metrics, this is empty if the session/visitor has more than one distinct device type, or the one if there is only one device type for the whole session/visitor.
serving_config	The serving configuration of the relevant event(s) that generated the metrics. For impression metrics, this is the serving config of the impression event. For session and visitor metrics, this is empty if the session/visitor has more than one distinct serving configs, or the one if there is only one serving config for the whole session/visitor.
traffic_destination	The source of the relevant event(s) that generated the metrics. For impression metrics, this is either "Google" if the impression is served by Google or "Non-Google" if the impression is served by your own solution. For session and visitor metrics, this is "Mixed" if the session/visitor has both impression events served both by Google and your own solution. This is "Google" if the impression events are purely served by Google or "Non-Google" if the impression events are purely served by your solution for the whole session/visitor.
entity	This is used by retailers that serve multiple brand sites or regions with one product catalog. Entity usually represents the brands or locations. For impression metrics, this is the entity of the impression event. For session and visitor metrics, this is the top five counted entities of the impression events for the session/visitor.

Impression metrics

Impression metrics are computed according to prerequisites. Conversion events (detail_page_view, add_to_cart, and purchase events), can be attributed back to an impression event only if they match the following criteria.

Impression event and conversion event have the same visitor.
The product ID of the conversion event must appear in the impression event's results.

Conversion event is within the required time window after the occurrence of the impression event. Different conversion time windows apply for different types of conversion events.

Conversion path	Conversion time window
Impression -> Detail Page View	60 seconds
Impression -> Add To Cart	24 hours
Impression -> Purchase	48 hours

Search impression metrics

Metric Name	Description
search_event	Search event count
detail_page_view_from_search	Detail page view event count from search
add_to_cart_unit_from_search	Add to cart unit count from search
purchase_unit_from_search	Purchase unit count from search
revenue_from_search	Revenue from search
search_event_without_result	Search event count which yields no results
search_request_personalization_enabled	Count of search requests with personalization enabled
search_request_personalization_result_served	Count of search requests that are served with personalized results

Browse impression metrics

Metric Name	Description
browse_event	Browse event count
detail_page_view_from_browse	Detail page view event count from browse
add_to_cart_unit_from_browse	Add to cart unit count from browse
purchase_unit_from_browse	Purchase unit count from browse
revenue_from_browse	Revenue from browse
browse_event_without_result	Browse event count which yields no results
browse_request_personalization_enabled	Count of browse requests with personalization enabled
browse_request_personalization_result_served	Count of browse requests that are served with personalized results

Session metrics

A session is defined as a continuous period of interaction between a user and the system. Any user event marks the start of a session, and the end of a session is marked when no other event occurs for the next 30 minutes. The session is extended another 30 minutes whenever a user event occurs within a started session. After a session ends, a new session begins when the user engages with the system again.

The dimensions that characterize the session metrics are jointly defined by all user events within the session. Refer to metrics dimensions as how dimensions values are set for the session.

Metric Name	Description
session	Session count

Search session metrics

A search session is a session contains at least one search event. Note that a search session can also be a browse session as defined by browse session metrics.

Metric Name	Description
search_session	Session count involving a search
search_event_in_search_session	Search event count within search sessions
browse_event_in_search_session	Browse event count within search sessions
detail_page_view_in_search_session	Detail page view count within search sessions
add_to_cart_in_search_session	Add to cart event count within search sessions
add_to_cart_unit_in_search_session	Add to cart unit count within search sessions
purchase_order_in_search_session	Purchase order count within search sessions
purchase_unit_in_search_session	Purchase unit count within search sessions
revenue_in_search_session	Revenue within search sessions
bounce_count_in_search_session	Session count of the visitor who navigate away from the site after viewing only one page within search sessions

Browse session metrics

A browse session is a session contains at least one browse event. Note that a browse session can also be a search session as defined by search session metrics.

Metric Name	Description
browse_session	Session count involving browse
search_event_in_browse_session	Search event count within browse sessions
browse_event_in_browse_session	Browse event count within browse sessions
detail_page_view_in_browse_session	Detail page view count within browse sessions
add_to_cart_in_browse_session	Add to cart event count within browse sessions
add_to_cart_unit_in_browse_session	Add to cart unit count within browse sessions
purchase_order_in_browse_session	Purchase order count within browse sessions
purchase_unit_in_browse_session	Purchase unit count within browse sessions
revenue_in_browse_session	Revenue within browse sessions
bounce_count_in_browse_session	Session count of the visitor who navigate away from the site after viewing only one page within browse sessions

Visitor metrics

A visitor is uniquely identified by the visitor ID. Visitor metrics aggregate all the user event information for the visitor.

The dimensions that characterize the visitor metrics are jointly defined by all user events for the visitor. Refer to metrics dimensions as how dimensions values are set for the visitor.

Metric Name	Description
visitor	Visitor count

Search visitor metrics

A search visitor is a visitor performing at least one search event. Note that a search visitor can also be a browse visitor as defined by browse visitor metrics.

Metric Name	Description
search_visitor	Visitor count who performs at least one search
search_event_from_search_visitor	Search event count from search visitors
browse_event_from_search_visitor	Browse event count from search visitors
detail_page_view_from_search_visitor	Detail page view count from search visitors
add_to_cart_from_search_visitor	Add to cart event count from search visitors
add_to_cart_unit_from_search_visitor	Add to cart unit count from search visitors
purchase_order_from_search_visitor	Purchase order count from search visitors
purchase_unit_from_search_visitor	Purchase count unit from search visitors
revenue_from_search_visitor	Revenue from search visitors

Browse visitor metrics

A browse visitor is a visitor performing at least one browse event. Note that a browse visitor can also be a search visitor as defined by search visitor metrics.

Metric Name	Description
browse_visitor	Visitor count who performs at least one browse
search_event_from_browse_visitor	Search event count from browse visitors
browse_event_from_browse_visitor	Browse event count from browse visitors
detail_page_view_from_browse_visitor	Detail page view count from browse visitors
add_to_cart_from_browse_visitor	Add to cart event count from browse visitors
add_to_cart_unit_from_browse_visitor	Add to cart unit count from browse visitors
purchase_order_from_browse_visitor	Purchase order count from browse visitors
purchase_unit_from_browse_visitor	Purchase unit count from browse visitors
revenue_from_browse_visitor	Revenue from browse visitors

Example usage

If you copy or export your metrics data to another destination, you can use SQL to get high-level or specific information about the exported metrics. Some common ways to explore your exported metrics using SQL are provided in the following sections.

Compute click-through rate (CTR)

Compute CTR for search per date per device type per traffic destination.

SELECT
  `date`,
  device_type,
  traffic_destination,
  SUM(detail_page_view_from_search) / SUM(search_event) as search_CTR
FROM copied_table
GROUP BY `date`, device_type, traffic_destination

Get browse-driven detail page view count

Get detail page view events that come from browse events for Google traffic with external_experiment_ids containing test_arm, and display all dimension information. external_experiment_ids is repeated string.

SELECT
  `date`,
  external_experiment_ids,
  device_type,
  serving_config,
  traffic_destination,
  entity,
  detail_page_view_from_browse
FROM copied_table
WHERE
  traffic_destination = 'Google' AND 'test_arm' in UNNEST(external_experiment_ids)

Get most-used serving configs

Get the most-used serving configs between '2023-10-01' and '2023-11-01' for Google traffic. Each search event should have an associated serving config.

SELECT
  serving_config,
  SUM(search_event) as total_search_count
FROM copied_table
WHERE `date` between '2023-10-01' and '2023-11-01' AND traffic_destination = 'Google'
GROUP BY serving_config
ORDER BY total_search_count