Google Ads API Shopping Segments: Filter by Brand, Product Type & Custom Labels

If you're building custom analytics for Google Shopping campaigns in 2026, understanding how to segment and filter data by product attributes is essential. The Google Ads API provides powerful options for filtering Shopping data by product brand, product type, custom labels, and category — but the documentation can be hard to navigate. This guide covers all available Shopping segments and shows how to use them with practical GAQL query examples.

The Google Ads API provides rich segmentation capabilities for Shopping campaigns through the shopping_performance_view resource. You can break down performance data by product dimensions like brand, category, type, and custom attributes—giving you the granular insights needed for SKU-level optimization.

Shopping Resources: Which One to Use?

The Google Ads API provides two main resources for Shopping data, each with different purposes and segmentation capabilities:

Resource	Purpose	Date Segmentation
shopping_performance_view	Historical performance data aggregated by product dimensions	Supported
shopping_product	Current product state, status, and feed issues	Not Supported

When to Use shopping_performance_view

Use shopping_performance_view when you need:

• Performance metrics (impressions, clicks, conversions, cost) by product
• Data segmented by date ranges
• Brand, product type, or custom label performance breakdowns
• Time-series analysis of Shopping campaign performance

When to Use shopping_product

Use shopping_product when you need:

• Current product status and approval state
• Feed issues and disapproval reasons
• Products that have never served ads
• Current product attributes (not historical)

Complete List of Product Segments

The shopping_performance_view resource supports a comprehensive set of product segments. Here's the complete reference for filtering and segmenting your Shopping data:

Product Identity Segments

These segments identify individual products and their basic attributes:

Product Type Segments (5 Levels)

Product type segments correspond to the product_type attribute in your Merchant Center feed, split by hierarchy level. If your product type is "Apparel > Men > Shirts > T-Shirts", the levels would be:

Segment	Example Value
segments.product_type_l1	Apparel
segments.product_type_l2	Men
segments.product_type_l3	Shirts
segments.product_type_l4	T-Shirts
segments.product_type_l5	(empty if not defined)

Google Product Category Segments (5 Levels)

Similar to product type, but based on the Google Product Taxonomy (the google_product_category attribute):

• segments.product_bidding_category_level1 through level5

These use Google's standardized category IDs rather than your custom product types. Useful for benchmarking against industry categories.

Custom Attribute Segments (Custom Labels)

Custom labels from your Merchant Center feed are available through these segments:

API Segment	Merchant Center Attribute
segments.product_custom_attribute0	custom_label_0
segments.product_custom_attribute1	custom_label_1
segments.product_custom_attribute2	custom_label_2
segments.product_custom_attribute3	custom_label_3
segments.product_custom_attribute4	custom_label_4

Channel and Condition Segments

Additional Product Segments

Segment	Description
segments.product_country	Target country of the product
segments.product_language	Language of the product feed
segments.product_store_id	Store ID for local inventory
segments.product_channel_exclusivity	Single-channel or multi-channel product

GAQL Query Examples

Here are practical Google Ads Query Language (GAQL) examples for common Shopping segment use cases:

Filter by Product Brand

Get performance data for a specific brand:

SELECT
  segments.product_brand,
  segments.date,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
  AND segments.product_brand = 'Nike'
ORDER BY metrics.conversions_value DESC

Brand Performance Comparison

Compare all brands in your portfolio:

SELECT
  segments.product_brand,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
  AND metrics.impressions > 0
ORDER BY metrics.conversions_value DESC
LIMIT 50

Product Type Level 1 Analysis

Analyze performance by top-level product categories:

SELECT
  segments.product_type_l1,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value,
  metrics.search_impression_share
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC

Custom Label Segmentation

Segment by custom label (e.g., margin tier in custom_label_0):

SELECT
  segments.product_custom_attribute0,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
  AND segments.product_custom_attribute0 IS NOT NULL
ORDER BY metrics.conversions_value DESC

Combined Brand + Product Type Analysis

Get granular data by combining multiple segments:

SELECT
  segments.product_brand,
  segments.product_type_l1,
  segments.product_type_l2,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
  AND metrics.cost_micros > 0
ORDER BY metrics.conversions_value DESC
LIMIT 100

SKU-Level Daily Performance

Get daily time-series data for individual products:

SELECT
  segments.date,
  segments.product_item_id,
  segments.product_title,
  segments.product_brand,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value,
  metrics.search_impression_share,
  metrics.search_click_share
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
  AND metrics.cost_micros > 0
ORDER BY segments.date DESC, metrics.cost_micros DESC
LIMIT 1000

Filtering with Segments in WHERE

Segments can be used in both SELECT (to break down data) and WHERE (to filter data). Here are the key filtering patterns:

Exact Match Filter

WHERE segments.product_brand = 'Adidas'

Multiple Values (IN)

WHERE segments.product_brand IN ('Nike', 'Adidas', 'Puma')

Not Equal

WHERE segments.product_brand != 'Unknown'

IS NOT NULL (Has Value)

WHERE segments.product_custom_attribute0 IS NOT NULL

LIKE Pattern Matching

WHERE segments.product_type_l1 LIKE '%Electronics%'

Segment Compatibility

Not all segments work together. The Google Ads API enforces compatibility constraints that you need to understand:

Date Segmentation Rules

For shopping_performance_view, date segments (segments.date, segments.week, segments.month, etc.) are fully supported. However, for shopping_product, date segmentation is NOT allowed and will return an UNSUPPORTED_DATE_SEGMENTATION error.

Incompatible Segments

Critical: Selecting any segments.keyword.* field implicitly filters results to Search Network keyword traffic only, excluding Shopping campaigns entirely. Never use keyword segments when analyzing Shopping data.

Checking Compatibility

Use the GoogleAdsFieldService or the Interactive Query Builder in Google's documentation to verify which segments and metrics can be selected together. Each field has a selectable_with list showing compatible fields.

Best Practices

1. Start Broad, Then Drill Down

Begin with high-level segments (product_type_l1, product_brand) to identify patterns, then add detail (product_item_id, date) for specific analysis. This keeps initial queries fast and helps you understand where to focus.

2. Use Meaningful Custom Labels

The power of product_custom_attribute0-4 segments depends entirely on how you've set up your custom labels in Merchant Center. Common high-value strategies:

• Margin tier: "high_margin", "medium_margin", "low_margin"
• Performance tier: "hero", "standard", "long_tail"
• Season: "spring_2026", "evergreen"
• Stock status: "high_stock", "low_stock", "clearance"
• Price range: "under_50", "50_100", "over_100"

3. Handle Empty Values

Products may have empty values for some segments (e.g., no brand set, only 2 product type levels). Your queries and analysis should handle these cases:

-- Exclude products without brand data
WHERE segments.product_brand IS NOT NULL
  AND segments.product_brand != ''

4. Account for Historical Context

Remember that shopping_performance_view reflects product attributes at the time of each event. If you changed a product's brand or category last week, older data will show the old values. This is actually useful for before/after analysis.

5. Paginate Large Queries

For large catalogs with long date ranges, implement pagination:

• Use LIMIT and offset patterns
• Break date ranges into smaller chunks
• Consider async processing for very large exports

6. Cache Reference Data

Product attributes don't change often. Cache segment values (list of brands, product types) separately from metrics to reduce API calls and improve application performance.

Frequently Asked Questions

Conclusion

The Google Ads API provides powerful segmentation capabilities for Shopping campaigns through the shopping_performance_view resource. Key product segments include:

• Identity: product_item_id, product_title, product_brand
• Category hierarchy: product_type_l1 through l5
• Google taxonomy: product_bidding_category_level1 through level5
• Custom labels: product_custom_attribute0 through 4
• Attributes: product_channel, product_condition, product_country

Use these segments in SELECT to break down metrics by product dimensions, or in WHERE to filter for specific products, brands, or categories. Remember that shopping_performance_view supports date segmentation while shopping_product does not. Be sure to check our v23 update guide for the latest changes to segment availability and API features.

For more on the metrics you can combine with these segments, see our Google Ads API Metrics guide. Tools like SKU Analyzer handle all this API complexity automatically, pulling Shopping performance data segmented by brand, product type, and custom labels into a unified dashboard.