Recipes for auto parts and vehicle-specific recommendations based on year, make and model. Featured recipes (☆) are listed first. Expand a recipe to see its use cases, requirements and how it works.
Recipes for other industries: Cross-industry · Retail · Media · Marketplaces · Online Learning · Location-Based Businesses
New users
For users who have recently joined or started engaging.
☆ Featured · Items by user vehicle's year, make and model
Items by user's vehicle
☆ Featured · Items by user vehicle's year, make and model
Items by user's vehicle
Use cases
Items by user's vehicle.
Backfill Recommendations.
Daily Newsletters.
Requirements
Catalog with product_ids.
User year, make and model attributes.
Inputs
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
How it works
Items based on user vechicle's year, make and model.
Active users
For users who engage regularly.
☆ Featured · Similar items to previous block by user's vehicle year, make, model and category
Best Sellers by user's year, make and model at a given category level
☆ Featured · Similar items to previous block by user's vehicle year, make, model and category
Best Sellers by user's year, make and model at a given category level
Use cases
Best Sellers by user's year, make and model at a given category level.
Top viewed by user's year, make and model at a given category level.
Promotional content by user's year, make and model at a given category level.
Trending items by user's year, make and model at a given category level.
New additions by user's year, make and model at a given category level.
Homepage recommendations.
Requirements
Catalog with category and product_ids.
User year, make and model attributes.
Pre-computed rankings.
Inputs
Previous block name.
Item Category Level. The default value is Leaf level.
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Vehicle year/make/model by Category Ranking 1. Select None, if not needed.
Vehicle year/make/model by Category Ranking 2. Select None, if not needed.
Vehicle year/make/model by Category Ranking 3. Select None, if not needed. 9 Vehicle year/make/model by Category Ranking 4. Select None, if not needed. 10. Site-wide Backfill. Select None, if not needed.
How it works
Top engaged items by user's vehicle year, make, model and previously interacted categories by previous block items.
Top items by user's vehicle year, make, model and previously interacted categories
Best Sellers by user's year, make and model at a given category level
Top items by user's vehicle year, make, model and previously interacted categories
Best Sellers by user's year, make and model at a given category level
Use cases
Best Sellers by user's year, make and model at a given category level.
Top viewed by user's year, make and model at a given category level.
Promotional content by user's year, make and model at a given category level.
New additions by user's year, make and model at a given category level.
Homepage recommendations.
Requirements
Events with product_ids.
Catalog with category and product_ids.
User year, make and model attributes.
Pre-computed rankings.
Inputs
User Recent Event Activity.
Item Category Level. The default value is Leaf level.
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Vehicle year/make/model by Category Ranking 1. Select None, if not required.
Vehicle year/make/model by Category Ranking 2. Select None, if not required.
Vehicle year/make/model by Category Ranking 3. Select None, if not required.
Vehicle year/make/model by Category Ranking 4. Select None, if not required.
Site-wide Backfill. Select None, if not required.
How it works
Top engaged items by user's vehicle year, make, model and previously interacted categories.
Next best items to previous block by user vehicle's year, make, model and category
Next Best Items
Next best items to previous block by user vehicle's year, make, model and category
Next Best Items
Use cases
Next Best Items.
Post purchase follow ups.
Expand item discovery based on users with similar purchase patterns.
Homepage recommendations.
Requirements
Catalog with category and product_ids.
User year, make and model attributes.
Pre-computed cross-sell categories and rankings.
Inputs
Previous block name.
Item Category Level. The default value is Leaf level.
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Vehicle year/make/model by Category Ranking for cross-sell categories.
Univeral categories for backfill.
Delimeter string.
Site-wide Backfill. Select None, if not required.
How it works
Based on item's in previous block, recommend next best items users typically buy/interact together in user vechicle's year, make and model.
Similar items to previous block by user's vehicle ymm and category from top brands
Best Sellers by user's year, make and model at a given category level
Similar items to previous block by user's vehicle ymm and category from top brands
Best Sellers by user's year, make and model at a given category level
Use cases
Best Sellers by user's year, make and model at a given category level.
Top viewed by user's year, make and model at a given category level.
Promotional content by user's year, make and model at a given category level.
Trending items by user's year, make and model at a given category level.
New additions by user's year, make and model at a given category level.
Homepage recommendations.
Requirements
Catalog with category and product_ids.
User year, make and model attributes.
Pre-computed rankings.
Pre-computed top brands at category level.
Inputs
Previous block name.
Item Category Level. The default value is Leaf level.
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Vehicle year/make/model by Category Ranking 1. Select None, if not needed.
Vehicle year/make/model by Category Ranking 2. Select None, if not needed.
Vehicle year/make/model by Category Ranking 3. Select None, if not needed. 9 Vehicle year/make/model by Category Ranking 4. Select None, if not needed. 10. Include items from top brands per category. The default value is Yes. 11. Site-wide Backfill. Select None, if not needed.
How it works
Top engaged items by user's vehicle year, make, model and previously interacted categories by previous block items from top brands per category.
Vehicle services
Suggest services due to the user based on vehicle selected
Vehicle services
Suggest services due to the user based on vehicle selected
Use cases
Suggest services due to the user based on vehicle selected.
Requirements
User vehicles.
Service list.
Mapping of user vehicles to services.
Inputs
Previous vehicle block and vehicle index to use for fitment.
How it works
User vehicle can have services scheduled at set intervals varying per vehicle. Given a list of user vehicle, make, model and correspondn.
Intermittently active users
For users who engage now and then.
Newly added items by user vehicle's year, make and model
Alert users to newest additions to catalog
Newly added items by user vehicle's year, make and model
Alert users to newest additions to catalog
Use cases
Alert users to newest additions to catalog.
Daily Newsletters.
Backfill for other recommendations.
Requirements
Catalog with category and product_ids.
User year, make and model attributes.
Pre-computed recent additions rankings.
Inputs
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Recent Items Ranking.
Use Backfill Items.
How it works
Recent items added to catalog via API or feeds based on user vechicle's year, make and model.
Churned users
For users who have stopped engaging.
☆ Featured · Category affinity items by user's vehicle year, make, model
Best Sellers by user's year, make and model by category affinity
☆ Featured · Category affinity items by user's vehicle year, make, model
Best Sellers by user's year, make and model by category affinity
Use cases
Best Sellers by user's year, make and model by category affinity.
Top viewed by user's year, make and model by category affinity.
Promotional content by user's year, make and model by category affinity.
New additions by user's year, make and model by category affinity.
Price ordered items by user's year, make and model by category affinity.
Homepage recommendations.
Requirements
Events with product_ids.
Catalog with brand and product_ids.
User year, make and model attributes.
Pre-computed rankings.
Inputs
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Enter the number of user category affinities to consider.
Vehicle year/make/model by Category Ranking 1. Select None, if not needed.
Vehicle year/make/model by Category Ranking 2. Select None, if not needed.
Vehicle YMM Backfill Ranking. Select None, if not needed.
How it works
Top category affinity items by user's vehicle year, make and model.
All users
For users at any lifecycle stage.
☆ Featured · Live content - Next Best Items to seed items + category level trending items
Next best items based on seed items in live context payload…
☆ Featured · Live content - Next Best Items to seed items + category level trending items
Next best items based on seed items in live context payload…
Use cases
Live content on site or in app when users are actively in session to suggest next best items to purchase.
Requirements
Live Content request payload with seed_items.
Item Catalogs with matching seed_items.
Inputs
Name of event payload attribute for seed_items.
Name of event payload attribute for exclude_items.
Name of event payload attribute for exclude_categories.
Next best product ranking.
Category backfill level.
Upto 3 category rankings.
How it works
Based on seed items in recommendation context, recommend next best products and category top items. Category will be collected based on seed item and next best item. Category level can be defined as leaf, parent or root. If leaf - leaf level trending items will be added, if leaf + parent, then leaf + parent level trending items will be added. If leaf + parent + root, then the 3 level trending items will be added. Upto 3 category rankings can be selected.
Feed type
Product-Product
Personalized items by user's vehicle year, make, model in given brand
Best Sellers by user's year, make and model in a given brand
Personalized items by user's vehicle year, make, model in given brand
Best Sellers by user's year, make and model in a given brand
Use cases
Best Sellers by user's year, make and model in a given brand.
Top viewed by user's year, make and model in a given brand.
Promotional content by user's year, make and model in a given brand.
New additions by user's year, make and model in a given brand.
Price ordered items by user's year, make and model in a given brand.
Homepage recommendations.
Requirements
Events with product_ids.
Catalog with brand and product_ids.
User year, make and model attributes.
Pre-computed rankings.
Inputs
User Vehicle Year Attribute.
User Vehicle Make Attribute.
User Vehicle Model Attribute.
Select Brand Name.
Vehicle year/make/model by Brand Ranking 1. Select None, if not needed.
Vehicle year/make/model by Brand Ranking 2. Select None, if not needed.
Vehicle YMM Backfill Ranking. Select None, if not needed.
How it works
Top engaged items by user's vehicle year, make and model in a given brand.
Live content - Seed category based recommendations aligned to user affinity
Items based on seed categories passed in live content payload…
Live content - Seed category based recommendations aligned to user affinity
Items based on seed categories passed in live content payload…
Use cases
Live content on site or in app when users are actively in session based on item/category being browsed.
Requirements
Live Content request payload with seed_categories.
Item Catalogs with matching seed_categories.
Inputs
Category ranking to use to fetch items.
User top x ranking to use to determine user favorite items.
Should user affinity brands be prioritized?
Should account backfill be used of insufficient results in seed category provided?
How it works
Based on seed categories or categories of seed items in recommendation context, recommend category top items per user affinity (brand affinity, top time spent items) Possible values in payload:.
{
"seed_item_ids": [],
"seed_categories": [],
"exclude_item_ids": [],
"exclude_categories": []
}Blogs/Articles based on vehicle
Suggest articles related to vehicles user has, or has been searching
Blogs/Articles based on vehicle
Suggest articles related to vehicles user has, or has been searching
Use cases
Suggest articles related to vehicles user has, or has been searching.
Add filters to bubble articles by certain category or author or other criteria.
Requirements
Articles with make model and make tags.
User vehicle block with vehicle index to use for fitment.
Inputs
Previous vehicle block and vehicle index to use for fitment.
Options of fitment preference - Make model and then make, Only make model, only make.
Ranking to use for blog selection.
Backfill ranking if insufficient blogs for vehicle (optional).
How it works
Articles corresponding to vehicle make model and make (if insufficient by make model).
Parent products of previous block
Recommend parent parts corresponding to any prior block
Parent products of previous block
Recommend parent parts corresponding to any prior block
Use cases
Recommend parent parts corresponding to any prior block.
Requirements
Prior block with items.
Parent sku for items in catalog.
Inputs
Previous block names.
How it works
Parent items of items in previous block - E.g.: single part Axle assembly can have multiple individual axle assembly parts.
Connect with Blueshift for personalized recommendations
Whether you're exploring new ways to use recommendation recipes or looking to design a custom model for your business, our team can guide you through the next steps.
Reach out to [email protected] or your Customer Success Manager (CSM) for assistance.
What's next
Learn how Recommendations Studio works and understand the workflow for creating personalized recommendations.
How Recommendations Studio works →