Use cases for list-attribute filtering

This guide demonstrates how to apply list-attribute filtering across common merchandising scenarios: promotional campaigns, tag-based collections, content curation, regional targeting, and compliance filtering. Each use case includes the business example, catalog structure, filter configuration, and expected results. Before implementing, complete the prerequisites and setup for list-attribute filtering.

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Note

The use cases in this guide show static catalog filtering configured in your recommendation model setup. For per-customer filtering based on individual preferences, use dynamic list-type operators in your recommendation requests.

Campaign and promotional filtering

Run multi-promotional campaigns

Example: Feature products from three simultaneous promotions (Summer Sale, Clearance, New Arrivals) on your homepage.

Catalog structure:

Product A: promo_list = ["Summer_Sale", "Featured"]
Product B: promo_list = ["Clearance", "Limited_Time"]
Product C: promo_list = ["New_Arrivals", "Trending"]
Product D: promo_list = ["Standard"]

Configuration:

• Attribute: promo_list
• Operator: any item
• Condition: in
• Values: Summer_Sale, Clearance, New_Arrivals

Result: Recommends Products A, B, and C.

Business value: Maximize promotional visibility across campaigns without separate recommendation models.

Build tag-based collections

Example: Launch an Earth Day campaign featuring eco-friendly, recycled, or sustainable products.

Catalog structure:

Product A: tags_list = ["eco_friendly", "organic", "bestseller"]
Product B: tags_list = ["recycled", "sustainable"]
Product C: tags_list = ["new_arrival", "trending"]
Product D: tags_list = ["sustainable", "vegan", "cruelty_free"]

Configuration:

• Attribute: tags_list
• Operator: any item
• Condition: in
• Values: eco_friendly, recycled, sustainable

Result: Recommends Products A, B, and D.

Business value: Leverage existing Shopify tags for themed collections without data restructuring.

Feature brand portfolios

Example: Run a luxury shopping event featuring only premium and designer brands.

Catalog structure:

Product A: brand_tags = ["Premium", "Established"]
Product B: brand_tags = ["Luxury", "Designer", "Exclusive"]
Product C: brand_tags = ["Value", "Everyday"]
Product D: brand_tags = ["Designer", "Limited_Edition"]

Configuration:

• Attribute: brand_tags
• Operator: any item
• Condition: in
• Values: Premium, Luxury, Designer

Result: Recommends Products A, B, and D.

Business value: Maintain strategic brand positioning during special events.

Content and media filtering

Create genre collections

Example: Build an "Edge of Your Seat" collection featuring thriller, mystery, and suspense content.

Catalog structure:

Movie A: genres_list = ["Thriller", "Action"]
Movie B: genres_list = ["Mystery", "Drama"]
Movie C: genres_list = ["Comedy", "Romance"]
Movie D: genres_list = ["Suspense", "Psychological"]

Configuration:

• Attribute: genres_list
• Operator: any item
• Condition: in
• Values: Thriller, Mystery, Suspense

Result: Recommends Movies A, B, and D.

Business value: Create curated content experiences that drive engagement through thematic collections.

Availability and compliance filtering

Filter by regional availability

Example: Recommend only products available in your operating regions (US, UK, Canada).

Catalog structure:

Product A: available_regions = ["US", "UK", "CA", "EU"]
Product B: available_regions = ["US", "CA"]
Product C: available_regions = ["EU", "APAC"]
Product D: available_regions = ["UK", "CA", "AU"]

Configuration:

• Attribute: available_regions
• Operator: any item
• Condition: in
• Values: US, UK, CA

Result: Recommends Products A, B, and D.

Business value: Prevent recommending unavailable items and reduce cart abandonment.

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Filter by certifications and compliance

Example: Recommend industrial equipment with both ISO 9001 and CE certifications for B2B buyers.

Catalog structure:

Product A: certifications_list = ["ISO_9001", "CE_certified", "RoHS"]
Product B: certifications_list = ["ISO_9001", "UL_listed"]
Product C: certifications_list = ["CE_certified", "FCC"]
Product D: certifications_list = ["ISO_9001", "CE_certified"]

Configuration:

• Attribute: certifications_list
• Operator: contains all
• Values: ISO_9001, CE_certified

Result: Recommends Products A and D.

Business value: Ensure recommendations meet mandatory compliance requirements for business buyers.

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Filter by multiple required attributes

Example: Promote a wellness collection with products that are both organic and non-GMO.

Catalog structure:

Product A: attributes_list = ["organic", "non-GMO", "gluten_free"]
Product B: attributes_list = ["organic", "vegan"]
Product C: attributes_list = ["non-GMO", "kosher"]
Product D: attributes_list = ["organic", "non-GMO"]

Configuration:

• Attribute: attributes_list
• Operator: contains all
• Values: organic, non-GMO

Result: Recommends Products A and D.

Business value: Maintain campaign integrity by ensuring products match your messaging.

Choosing the right operator

Related resources

• Set up list-attribute filtering - Configuration steps and prerequisites
• List-type operators for dynamic filtering - Per-customer filtering via API
• Catalogs guide - Configure searchable fields