Import historical user events

This page describes how to import user event data from past events in bulk into Vertex AI Search for commerce. User event data is required for training models. After you've set up real-time event recording, it can take a considerable amount of time to record sufficient user event data to train your models. Importing historical data can accelerate the process.

The import procedures on this page apply to both recommendations and search. After you import data, both services are able to use those events, so you don't need to import the same data twice if you use both services.

You can import events from:

Cloud Storage.
BigQuery.
BigQuery for Google Analytics 360 events.
BigQuery for Google Analytics 4 events.
Events inline using the userEvents.import method.

Before you begin

To avoid import errors and verify that there is sufficient data to generate good results, review the following information before importing your user events.

Review the best practices for recording user events.
User event formatting differs depending on the user event type. See User event types and examples schema for the format to specify when creating tables for each event type.
Recommendations and search have differing minimum data requirements. Make sure your user events meet the requirements of the service you plan to use:
- See User event requirements and best practices for search and recommendations requirements.
- If you use recommendations models, also see User event data requirements, which lists additional requirements depending on the recommendation model type and optimization objective you plan to use.

Event import considerations

This section describes the methods that can be used for batch importing of your historical user events, when you might use each method, and some of their limitations.

Import source	Description	When to use	Limitations
Cloud Storage	Import JSON data from files (≤ 2 GB, max 100) using Google Cloud console or `curl`. Supports custom attributes.	High-volume data loads in a single step.	GA data requires an extra export to BigQuery before moving to Cloud Storage.
BigQuery	Import from a BigQuery table using the Vertex AI Search for commerce schema using Google Cloud console or `curl`.	Preprocessing or analyzing event data before import.	Requires manual schema mapping; higher resource cost for high event volumes.
BigQuery + Google Analytics 360	Direct import of existing Google Analytics 360 data into Vertex AI Search for commerce.	Existing GA360 conversion tracking; no manual schema mapping needed.	Limited attribute subset available; search requires GA impression tracking.
BigQuery + Google Analytics 4	Direct import of existing Google Analytics 4 data into Vertex AI Search for commerce.	Existing GA4 conversion tracking; no manual schema mapping needed.	Limited attribute subset available; search requires `search_query` event parameter keys.
Inline import	Import using direct `userEvents.import` method calls.	High-privacy backend authentication requirements.	More complex implementation than standard web-based imports.

Size limitations

There is a total system limit of 40 billion user events. The following size limitations for data imports according to ingest platform are:

For bulk import from Cloud Storage, each file must be 2 GB or smaller, and you can include up to 100 files in a single bulk import request.
For BigQuery imports, the size limitation is 128 GB.
For inline imports, a recommended maximum of 10,000 user events is suggested per request.
For the minimum days to record live or import historical user events for model training and search optimization, do model training and tuning. Initial model training and tuning can take two to five days or, for larger datasets, longer.

Link search events to user interactions

To enable revenue-optimized ranking (Tier 3) and personalization (Tier 4), uploading general search events is not enough. Vertex AI Search models require a strong signal of user intent and satisfaction to learn which products are performing well for specific queries. This signal is provided by attributable user interactions. You must upload subsequent events — specifically, detail-page-view, add-to-cart, and purchase-complete.

Why user interactions matter

Relevance signals: A search event tells the model what the user wanted. A detail-page-view (click) tells the model which result was relevant.
Revenue signals: add-to-cart and purchase-complete events tell the model which results drive actual business value.

Data quality thresholds for optimization

To activate the revenue optimization models, your data must meet specific volume and quality thresholds.

The following metrics are required:

Metric	Requirement	Context
Attributable click volume	250,000 `detail-page-view` events in last 30 days	These must have a valid user interaction linking them to a search result.
Search event volume	2,000,000 events in last 90 days	A large baseline of historical search traffic is required to establish statistical significance.
Click density	10 average `detail-page-view` events per product	Ensures the model has enough signal coverage across your catalog (in the last 30 days).
Conversion signal	0.5 average `add-to-cart` events per product	Recommended to fully utilize revenue-maximizing objectives.
Price coverage	95% of searched products have price	The model cannot optimize for revenue if it doesn't know the price of the products being returned.

For more information, refer to the Data quality page.

Bulk import using BigQuery or Cloud Storage

Using BigQuery or Cloud Storage as a staging area for user event data in Vertex AI Search for commerce offers distinct advantages:

Enhanced resiliency: Storing events in BigQuery or Cloud Storage provides a reliable backup mechanism, enabling purging and reingestion if necessary. This resiliency safeguards against data loss and simplifies recovery in case of errors or inconsistencies. There is also built-in resiliency in the import method where events that were not ingested are stored in error buckets along with the error details.
In-place custom analytics: With events readily accessible in BigQuery, custom analytics can be performed directly on the user event data without the need for additional data export or transfer processes. This enables analysis workflows and real-time insights.
Using existing events: Bulk imports can use existing user event data collected in various formats. A straightforward extract, transform, load (ETL) process can convert this data into the Vertex AI Search for commerce format, eliminating the need for extensive frontend changes or complex integrations.

Potential downsides of bulk import are:

Limited real-time personalization: Real-time personalization capabilities are constrained by the frequency of bulk imports. The time lag between event generation and ingestion can impact the responsiveness of personalized search results.
Slower KPI measurement and error reporting: Compared to real-time streaming, bulk imports introduce delays in KPI measurement and error reporting due to the batch-oriented nature of the process. This can hinder immediate responses to emerging trends or issues.
ETL pipeline infrastructure: Compared to real-time streaming, ETL pipelines need to be built and monitored for failures. Mechanism to retry imports for failed events (after fixing) also needs to be implemented. Implementing this might need some initial development efforts.

Understanding these trade-offs can guide you in selecting the most suitable user event ingestion approach for your specific use cases and priorities within Vertex AI Search for commerce.

Import user events from Cloud Storage

Import user events from Cloud Storage using the Google Cloud console or the userEvents.import method.

Console

Go to the Data> page in the Search for commerce console.

Go to the Data page
Click Import to open the Import Data panel.
Choose User events.
Select Google Cloud Storage as the data source.
Choose Retail User Events Schema as the schema.
Enter the Cloud Storage location of your data.
Click Import.

curl

Use the userEvents.import method to import your user events.

Create a data file for the input parameters for the import. Use the GcsSource object to point to your Cloud Storage bucket.

You can provide multiple files, or just one.
- INPUT_FILE: A file or files in Cloud Storage containing your user event data. See About user events for examples of each user event type format. Make sure each user event is on its own single line, with no line breaks.
- ERROR_DIRECTORY: A Cloud Storage directory for error information about the import.
The input file fields must be in the format gs://<bucket>/<path-to-file>/. The error directory must be in the format gs://<bucket>/<folder>/. If the error directory does not exist, Vertex AI Search for commerce creates it. The bucket must already exist.
```
{
"inputConfig":{
 "gcsSource": {
   "inputUris": ["INPUT_FILE_1", "INPUT_FILE_2"],
  },
  "errorsConfig":{"gcsPrefix":"ERROR_DIRECTORY"}
}
```

Import your catalog information by making a POST request to the userEvents:import REST method, providing the name of the data file.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

curl -X POST \
     -v \
     -H "Content-Type: application/json; charset=utf-8" \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     --data @./DATA_FILE.json \
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import"
  }
}'

Import user events from BigQuery

Import user events from BigQuery using the Google Cloud console or the userEvents.import method.

Set up BigQuery access

Follow the instructions in Setting up access to your BigQuery dataset to give your Vertex AI Search for commerce service account the minimum BigQuery User role required for the import to succeed and the additional BigQuery Data Editor role for your BigQuery dataset. The BigQuery Data Owner role is unnecessary.

Import your user events from BigQuery

You can import user events using the Search for commerce console or the userEvents.import method.

Console

Go to the Data> page in the Search for commerce console.

Go to the Data page
Click Import to open the Import Data panel.
Choose User events.
Select BigQuery as the data source.
Select the data schema.
- Google Analytics 4: Use for Google Analytics 4 events.
- Google Analytics 360: Use for Google Analytics 360 events, unless you are importing events only home-page-views from Google Analytics 360 (in that case, use Retail User Events Schema).
- Retail User Events Schema: Use for importing events from sources other than Google Analytics and for importing events only home-page-views from Google Analytics 360.
Enter the BigQuery table where your data is located.
Optional: Enter the location of a Cloud Storage bucket in your project as a temporary location for your data.
If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
Optional: Under Show advanced options, enter the location of a Cloud Storage bucket in your project as a temporary location for your data.

If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
Click Import.

curl

Import your user events by including the data for the events in your call to the userEvents.import method. See the userEvents.import API reference.

The value you specify for dataSchema depends on what you're importing:

user_event_ga4: Use for Google Analytics 4 events.
user_event_ga360: Use for Google Analytics 360 events, unless you are importing only home-page-views from Google Analytics 360 (in that case, use user_event).
user_event: Use for importing events from sources other than Google Analytics and for importing events only home-page-views from Google Analytics 360.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

curl \
-v \
-X POST \
-H "Content-Type: application/json; charset=utf-8" \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
"https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import" \
--data '{
  "inputConfig": {
    "bigQuerySource": {
      "datasetId": "DATASET_ID",
      "tableId": "TABLE_ID",
      "dataSchema": "SCHEMA_TYPE"
  }
}
}'

Import Analytics 360 user events with BigQuery

The following procedures assume you are familiar with using BigQuery and Analytics 360.

Before you begin

Before you begin the next steps, make sure:

You're using Enhanced Ecommerce.
You have BigQuery linked to Analytics 360.

Check your data source

Make sure that the user event data that you will import is correctly formatted in a BigQuery table you have access to.

Make sure that the table is named project_id:ga360_export_dataset.ga_sessions_YYYYMMDD.

See the Google Analytics documentation for more about the table format and naming.
In the BigQuery Google Cloud console, select the table from the Explorer panel to preview the table.

Check that:
1. The clientId column has a valid value—for example, 123456789.123456789.
  
  Note that this value is different from the full _ga cookie value (which has a format such as GA1.3.123456789.123456789).
2. The hits.transaction.currencyCode column has a valid currency code.
  
  Note: The Vertex AI Search for commerce purchase-complete event type requires a currency code. If you're importing purchase events, which some Vertex AI Search for commerce models require, update Enhanced Ecommerce reporting to include currency code.
3. If you plan to import search events, check that either a hits.page.searchKeyword or hits.customVariable.searchQuery column is present.
  
  While Vertex AI Search for commerce requires both searchQuery and productDetails to return a list of search results, Analytics 360 doesn't store both search queries and product impressions in one event. For Vertex AI Search for commerce to work, you need to create a tag at the data layer or a JavaScript Pixel to be able to import both types of user events from Google Analytics sources:
  - searchQuery, which is read from the search_term parameter, or from view_search_results events, is derived from either hits.page.searchKeyword, or from hits.customVariables.customVarValue if hits.customVariables.customVarName is searchQuery.
  - productDetails, the product impression which is read from the items parameter of the view_item_list event, is taken from hits.product if hits.product.isImpressions is TRUE.

Check the consistency of item IDs between the uploaded catalog and the Analytics 360 user event table.

Using any product ID from the hits.product.productSKU column in the BigQuery table preview, use the product.get method to make sure the same product is in your uploaded catalog.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

   curl \
     -v \
     -X GET \
     -H "Content-Type: application/json; charset=utf-8" \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     "https://retail.googleapis.com/v2/projects/PROJECT_NUMBER/locations/global/catalogs/default_catalog/branches/default_branch/products/PRODUCT_ID"

You can import Google Analytics 360 events using the Search for commerce console or the userEvents.import method.

Console

Go to the Data> page in the Search for commerce console.

Go to the Data page
Click Import to open the Import Data panel.
Choose User events.
Select BigQuery as the data source.
Select the data schema.
- Google Analytics 4: Use for Google Analytics 4 events.
- Google Analytics 360: Use for Google Analytics 360 events, unless you are importing events only home-page-views from Google Analytics 360 (in that case, use Retail User Events Schema).
- Retail User Events Schema: Use for importing events from sources other than Google Analytics and for importing events only home-page-views from Google Analytics 360.
Enter the BigQuery table where your data is located.
Optional: Enter the location of a Cloud Storage bucket in your project as a temporary location for your data.
If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
Optional: Under Show advanced options, enter the location of a Cloud Storage bucket in your project as a temporary location for your data.

If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
Click Import.

REST

Import your user events by including the data for the events in your call to the userEvents.import method.

For dataSchema, use the value user_event_ga360.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json
curl \\
  -v \\
  -X POST \\
  -H "Content-Type: application/json; charset=utf-8" \\
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \\
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import" \\
  --data '{
    "inputConfig": {
      "bigQuerySource": {
        "datasetId": "some_ga360_export_dataset",
        "tableId": "ga_sessions_YYYYMMDD",
        "dataSchema": "user_event_ga360"
    }
  }
}'

Java

public static String importUserEventsFromBigQuerySource()
    throws IOException, InterruptedException, ExecutionException {
  UserEventServiceClient userEventsClient = getUserEventServiceClient();

  BigQuerySource bigQuerySource = BigQuerySource.newBuilder()
      .setProjectId(PROJECT_ID)
      .setDatasetId(DATASET_ID)
      .setTableId(TABLE_ID)
      .setDataSchema("user_event")
      .build();

  UserEventInputConfig inputConfig = UserEventInputConfig.newBuilder()
      .setBigQuerySource(bigQuerySource)
      .build();

  ImportUserEventsRequest importRequest = ImportUserEventsRequest.newBuilder()
      .setParent(DEFAULT_CATALOG_NAME)
      .setInputConfig(inputConfig)
      .build();

  String operationName = userEventsClient
      .importUserEventsAsync(importRequest).getName();

  userEventsClient.shutdownNow();
  userEventsClient.awaitTermination(2, TimeUnit.SECONDS);

  return operationName;
}

You can import Analytics 360 user events if you have integrated Analytics 360 with BigQuery and use Enhanced Ecommerce.

Import your Analytics 360 home-page-views with BigQuery

In Analytics 360, home-page-view events are not distinguished from other page-view events. This means that home-page-view events are not imported as events with the other event types (such as detail-page-view) in Import your Analytics 360 events.

The following procedure explains how you can extract home-page-view events from your Analytics 360 data and import them into Vertex AI Search for commerce. In short, this is done by extracting users' views of the home page (identified by the home-page path) into a new BigQuery table and then importing data from that new table into Vertex AI Search for commerce.

To import home-page-view events from Analytics 360 into Vertex AI Search for commerce:

Create a BigQuery dataset or make sure that you have a BigQuery dataset available that you can add a table to.

This dataset can be in your Vertex AI Search for commerce project or in the project where you have your Analytics 360 data. It is the target dataset into which you'll copy the Analytics 360 home-page-view events.
Create a BigQuery table in the dataset as follows:
1. Replace the variables in the following SQL code as follows.
  - target_project_id: The project where the dataset from step 1 is located.
  - target_dataset: The dataset name from step 1.
```
CREATE TABLE TARGET_PROJECT_ID.TARGET_DATASET.ga_homepage (
 eventType STRING NOT NULL,
 visitorId STRING NOT NULL,
 userId STRING,
 eventTime STRING NOT NULL
);
```
2. Copy the SQL code sample.
3. Open the BigQuery page in the Google Cloud console.
  
  Go to the BigQuery page
4. If it's not already selected, select the target project.
5. In the Editor pane, paste the SQL code sample.
6. Click Run and wait for the query to finish running.
Running this code creates a table in the format target_project_id:target_dataset.ga_homepage_YYYYMMDD—for example, my-project:view_events.ga_homepage_20230115.
Copy the Analytics 360 home-page-view events from your Analytics 360 data table into the table created in the preceding step 2.
1. Replace the variables in the following SQL example code as follows:
  - source_project_id: The ID of the project that contains the Analytics 360 data in a BigQuery table.
  - source_dataset: The dataset in the source project that contains the Analytics 360 data in a BigQuery table.
  - source_table: The table in the source project that contains the Analytics 360 data.
  - target_project_id: The same target project ID as in the preceding step 2.
  - target_dataset: The same target dataset as in the preceding step.
  - path: This is the path to the home page. Usually this is /—for example, if the home page is example.com/. However, if the home page is like examplepetstore.com/index.html, the path is /index.html.
```
INSERT INTO `TARGET_PROJECT_ID.TARGET_DATASET.ga_homepage(eventType,visitorId,userID,eventTime)`

SELECT
  "home-page-view" as eventType,
  clientId as visitorId,
  userId,
  CAST(FORMAT_TIMESTAMP("%Y-%m-%dT%H:%M:%SZ",TIMESTAMP_SECONDS(visitStartTime)) as STRING) AS eventTime

FROM
  `SOURCE_PROJECT_ID.SOURCE_DATASET.SOURCE_TABLE`, UNNEST(hits) as hits

WHERE hits.page.pagePath = "PATH" AND visitorId is NOT NULL;
```
2. Copy the SQL code sample.
3. Open the BigQuery page in the Google Cloud console.
  
  Go to the BigQuery page
4. If it's not already selected, select the target project.
5. In the Editor pane, paste the SQL code sample.
6. Click Run and wait for the query to finish running.
Follow the instructions in Import user events from BigQuery to import the home-page-view events from the target table. During schema selection, if you import using console, select Retail User Events Schema; if you import using userEvents.import, specify user_event for the dataSchema value.
Delete the table and dataset that you created in steps 1 and 2.

Import Google Analytics 4 user events with BigQuery

You can import Google Analytics 4 user events if you have integrated Google Analytics 4 with BigQuery and use Google Analytics Ecommerce.

The following procedures assume you are familiar with using BigQuery and Google Analytics 4.

Before you begin

Before you begin the next steps, make sure:

You're using Google Analytics Ecommerce.
You have BigQuery linked to Google Analytics 4.

Check your data source

To make sure that your user event data is prepared for importing, follow these steps.

For a table of Google Analytics 4 fields that Vertex AI Search for commerce uses and which Vertex AI Search for commerce fields they map to, see Google Analytics 4 user event fields.

For all Google Analytics event parameters, see the Google Analytics Events reference documentation.

Make sure that the user event data that you will import is correctly formatted in a BigQuery table you have access to.
- The dataset should be named analytics_PROPERTY_ID.
- The table should be named events_YYYYMMDD.
For information about the table names and format, see the Google Analytics documentation.
In the BigQuery Google Cloud console, select the dataset from the Explorer panel and find the table of user events that you plan to import.

Check that:
1. The event_params.key column has a currency key and that its associated string value is a valid currency code.
  
  Note: The Vertex AI Search for commerce purchase-complete event type requires a currency code. If you're importing purchase events, which some Vertex AI Search for commerce models require, update your event reporting to include currency codes.
2. If you plan to import search events, check that the event.event_params.key column has a search_term key and an associated value.
  
  While Vertex AI Search for commerce requires both searchQuery and productDetails to return a list of search results, Google Analytics 4 doesn't store both search queries and product impressions in one event. For Vertex AI Search for commerce to work, you need to create a tag at the data layer or from a JavaScript Pixel to be able to import both types of user events from Google Analytics sources:
  - searchQuery, which is read from the search_term parameter, or from view_search_results events.
  - productDetails, the product impression which is read from the items parameter of the view_item_list event.
  For information about search in Google Analytics 4, see search in the Google Analytics documentation.

Check the consistency of item IDs between the uploaded catalog and the Google Analytics 4 user event table.

To make sure that a product in the Google Analytics 4 user table is also in your uploaded catalog, copy a product ID from the event.items.item_id column in the BigQuery table preview and use the product.get method to check if that product ID is in your uploaded catalog.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json

   curl \
     -v \
     -X GET \
     -H "Content-Type: application/json; charset=utf-8" \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     "https://retail.googleapis.com/v2/projects/PROJECT_NUMBER/locations/global/catalogs/default_catalog/branches/default_branch/products/PRODUCT_ID"

Import your Google Analytics 4 events

You can import Google Analytics 4 events using the Search for commerce console or the userEvents.import method.

Use the console

Go to the Data> page in the Search for commerce console.

Go to the Data page
Click Import to open the Import Data panel.
Choose User events.
Select BigQuery as the data source.
Select the data schema.
- Google Analytics 4: Use for Google Analytics 4 events.
- Google Analytics 360: Use for Google Analytics 360 events, unless you are importing events only home-page-views from Google Analytics 360 (in that case, use Retail User Events Schema).
- Retail User Events Schema: Use for importing events from sources other than Google Analytics and for importing events only home-page-views from Google Analytics 360.
Enter the BigQuery table where your data is located.
Optional: Enter the location of a Cloud Storage bucket in your project as a temporary location for your data.
If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
Optional: Under Show advanced options, enter the location of a Cloud Storage bucket in your project as a temporary location for your data.

If not specified, a default location is used. If specified, the BigQuery and Cloud Storage bucket have to be in the same region.
Click Import.

Use the API

Import your user events by including the data for the events in your call to the userEvents.import method. See the userEvents.import API reference.

For dataSchema, use the value user_event_ga4.

export GOOGLE_APPLICATION_CREDENTIALS=/tmp/my-key.json
curl \\
  -v \\
  -X POST \\
  -H "Content-Type: application/json; charset=utf-8" \\
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \\
  "https://retail.googleapis.com/v2/projects/PROJECT_ID/locations/global/catalogs/default_catalog/userEvents:import" \\
  --data '{
    "inputConfig": {
      "bigQuerySource": {
        "projectId": "PROJECT_ID",
        "datasetId": "DATASET_ID",
        "tableId": "TABLE_ID",
        "dataSchema": "user_event_ga4"
    }
  }
}'

Import user events inline

You can import user events inline by including the data for the events in your call to the userEvents.import method.

The easiest way to do this is to put your user event data into a JSON file and provide the file to curl.

For the formats of the user event types, see About user events.

curl

Create the JSON file:

{
  "inputConfig": {
    "userEventInlineSource": {
      "userEvents": [
        \{
          "<userEvent1>"
        \},
        \{
          "<userEvent2>"
        \},
        \....
      \]
    \}
  }
}

Call the POST method:

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

Java

public static String importUserEventsFromInlineSource(
    List<UserEvent> userEventsToImport)
    throws IOException, InterruptedException, ExecutionException {
  UserEventServiceClient userEventsClient = getUserEventServiceClient();

  UserEventInlineSource inlineSource = UserEventInlineSource.newBuilder()
      .addAllUserEvents(userEventsToImport)
      .build();

  UserEventInputConfig inputConfig = UserEventInputConfig.newBuilder()
      .setUserEventInlineSource(inlineSource)
      .build();

  ImportUserEventsRequest importRequest = ImportUserEventsRequest.newBuilder()
      .setParent(DEFAULT_CATALOG_NAME)
      .setInputConfig(inputConfig)
      .build();

  String operationName = userEventsClient
      .importUserEventsAsync(importRequest).getName();

  userEventsClient.shutdownNow();
  userEventsClient.awaitTermination(2, TimeUnit.SECONDS);

  return operationName;
}

Historical catalog data

You can also import catalog data that appears in your historical user events. This catalog data can be helpful because past product information can be used to enrich user event capturing, which can in turn improve model accuracy.

For more details, see Import historical catalog data.

View imported events

View event integration metrics in the Events tab on the Search for commerce console Data page. This page shows all events written or imported in last year. Metrics can take up to 24 hours to appear after successful data ingestion.

Go to the Data page

A/B testing evaluation

The following parameters for A/B apply, depending on your testing objective:

For a click-through rate (CTR) objective, at least 21 days of user events are required, or specific event volumes. For example, more than 2 million search events and more than 500,000 search clicks.
For a conversion rate (CVR) or revenue objective, at least 28 days of user events are required, or specific event volumes. For example, over 4 million search events, over 1 million search clicks, over 0.5 purchase events per searchable product.

Refer to the A/B testing page For more information about A/B testing and best practices.

What's next

Start recording user events.
Manage your user events.

Import historical user events Stay organized with collections Save and categorize content based on your preferences.

Before you begin

Event import considerations

Size limitations

Link search events to user interactions

Why user interactions matter

Data quality thresholds for optimization

Bulk import using BigQuery or Cloud Storage

Import user events from Cloud Storage

Console

curl

Import user events from BigQuery

Set up BigQuery access

Import your user events from BigQuery

Console

curl

Import Analytics 360 user events with BigQuery

Before you begin

Check your data source

Console

REST

Java

Import your Analytics 360 home-page-views with BigQuery

Import Google Analytics 4 user events with BigQuery

Before you begin

Check your data source

Import your Google Analytics 4 events

Use the console

Use the API

Import user events inline

curl

Java

Historical catalog data

View imported events

A/B testing evaluation

What's next

Import historical user events