Recipes for e-commerce and retail platforms, focused on product discovery and cart recovery. Featured recipes (☆) are listed first. Expand a recipe to see its use cases, requirements and how it works.
Recipes for other industries: Cross-industry · Media · Marketplaces · Automotive · Online Learning · Location-Based Businesses
Active users
For users who engage regularly.
☆ Featured · Abandoned cart
Remind users and help them continue where they left off
☆ Featured · Abandoned cart
Remind users and help them continue where they left off
Use cases
Remind users and help them continue where they left off.
Combine with similar items product recommendations to let them explore and discover more.
Use multi touch recommendations for high intent users and prevent lost revenue.
Requirements
Add_to_Cart event or equivalent with product_ids.
Purchase event or equivalent with product_ids.
Item catalogs with those product_ids.
Inputs
Timeline on add_to_cart events.
Timeline on purchase events.
How it works
Replay Items added to cart but not purchased during checkout process and bring back users to where they left off.
☆ Featured · Derived event - price drop
Price drop reminders
☆ Featured · Derived event - price drop
Price drop reminders
Use cases
Price drop reminders.
Combine with similar items to help users compare various products and let them explore.
Weekly / Monthly newsletters.
Requirements
Events saved on timeline with product_ids in payload.
Price change threshold.
Item Catalogs matching those product_ids.
How it works
Items with price change which have been browsed and not yet purchased by users.
Next best items based on recent purchases
Post purchase follow up alerts
Next best items based on recent purchases
Post purchase follow up alerts
Use cases
Post purchase follow up alerts.
Shipping/delivery confirmation alerts.
Requirements
Purchase or equivalent events with product_ids.
Item catalogs with matching product_ids.
Inputs
Recent purchase timeline.
Precomputed purchase to purchase rankings based on other users with similar activity.
Category backfill.
Site backfill.
How it works
Top items to consider after last purchase based on users with similar purchase history.
Feed type
Product-Product
Post purchase cross sell
Post purchase follow ups
Post purchase cross sell
Post purchase follow ups
Use cases
Post purchase follow ups.
Expand item discovery based on users with similar purchase patterns.
Requirements
Item catalogs with category.
User purchase activity timeline.
Inputs
Purchase event timeline.
Category top items ranking to use for each cross-sell category.
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Based on recent purchase categories, recommend items in other categories users typically buy.
Next best items by price difference
Abandoned cart/browse/search follow ups
Next best items by price difference
Abandoned cart/browse/search follow ups
Use cases
Abandoned cart/browse/search follow ups.
Post purchase upsell.
Requirements
Users recent activity timeline.
Item catalog with price.
Inputs
User recent activity events and timeline.
Related products ranking.
Absolute price (%) difference allowed between recent and related items.
Number of related items per item in recent activity (default 3) 4. Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Related items to items in recent user activity limited to selected price difference in absolute range.
Popular items in co-browsed brands
Popular items in co-browsed brands
Popular items in co-browsed brands
Popular items in co-browsed brands
Use cases
Suggested content based on co-browsed brands to brands already interacted by the user.
Requirements
Users recent activity timeline.
Item catalog with brands.
Inputs
User recent activity events and timeline.
Number of co-browsed brands to consider per brand in recent activity (default 3).
Product ranking for co-browsed brands.
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Popular items in co-browsed brands to the brands in recent activity.
Cross selling brand items
Popular items in brands that are bought together
Cross selling brand items
Popular items in brands that are bought together
Use cases
Suggested content based on cross-sold brands to brands already interacted by the user.
Requirements
Users recent activity timeline.
Item catalog with brands.
Inputs
User recent activity events and timeline.
Number of cross-sold brands to consider per brand in recent activity (default 3).
Product ranking for cross-sold brands.
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Popular Items from brands that are bought together based on recent purchases.
Similar items to previous block
Use products in previous blocks to recommend similar items
Similar items to previous block
Use products in previous blocks to recommend similar items
Use cases
Use products in previous blocks to recommend similar items.
Multi block personalization based on recent activity.
Requirements
Events with product_ids.
Item catalogs with matching product_ids.
Inputs
Previous block name.
Number of items to consider from recent activity.
Category backfill for given category level. Select None, if not required.
Site backfill. Select None, if not required.
How it works
Items similar to previous block with backfills based on catalog attributes or other users with similar activity.
Feed type
Product-Product
Intermittently active users
For users who engage now and then.
☆ Featured · Wishlist reminders
Wishlist reminder for items not purchased
☆ Featured · Wishlist reminders
Wishlist reminder for items not purchased
Use cases
Wish-list reminders to users for items they wish-listed but did not purchase.
Requirements
Users activity with wish-lists and purchase events.
Inputs
User wish-list event and timeline.
User purchase event timeline.
How it works
Items added to wish-list but not purchased yet.
Abandoned browse
Remind users and help them continue browsing where they left off
Abandoned browse
Remind users and help them continue browsing where they left off
Use cases
Remind users and help them continue browsing where they left off.
Combine with similar items product recommendations to let them explore and discover more.
Use multi touch recommendations for high intent users and prevent lost revenue.
Requirements
View event or equivalent with product_ids.
Purchase event or equivalent with product_ids.
Item catalogs with those product_ids.
Inputs
Timeline on view events.
Timeline on purchase events.
How it works
Replay Items viewed or browsed on sites or apps but not purchased and remind them to continue exploring.
Last saved cart
Remind users and help them continue where they left off
Last saved cart
Remind users and help them continue where they left off
Use cases
Remind users and help them continue where they left off.
Combine with similar items product recommendations to let them explore and discover more.
Requirements
Last_saved_cart event or equivalent with product_ids.
Item catalogs with those product_ids.
Inputs
Include last saved cart or equivalent event.
How it works
Replay items saved to cart which has metadata and bring back users to where they left off.
Purchase again
Replay the items purchased within X days to recommend repurchase
Purchase again
Replay the items purchased within X days to recommend repurchase
Use cases
Replay the items purchased within X days to recommend repurchase.
Combine with similar items product recommendations to let them explore and discover more.
Requirements
Purchase event or equivalent with product_ids.
Item catalogs with those product_ids.
Inputs
Timeline on purchase events.
How it works
Replay Items purchased within last X days.
Related items to last search
Remind users where they left off in their last search
Related items to last search
Remind users where they left off in their last search
Use cases
Remind users where they left off in their last search.
Event triggered campaigns on abandoned search.
Requirements
Event search with product_ids.
Item catalog matching those product_ids.
Inputs
Product to product ranking.
Category ranking backfill.
Site-wide backfill.
How it works
Items related to last search based on other users searching for similar items.
Feed type
Product-Product
Derived event - back in stock
Back in stock items reminders
Derived event - back in stock
Back in stock items reminders
Use cases
Back in stock items reminders.
Combine with similar items to help users compare various products and let them explore.
Weekly / Monthly newsletters.
Requirements
Events saved on timeline with product_ids in payload.
Availability status.
Item Catalogs matching those product_ids.
How it works
Items previously unavailable and are now in stock which have been browsed and not yet purchased by users.
Derived event - low in stock
Low in stock items reminders
Derived event - low in stock
Low in stock items reminders
Use cases
Low in stock items reminders.
Combine with similar items to help users compare various products and let them explore.
Weekly / Monthly newsletters.
Requirements
Events saved on timeline with product_ids in payload.
Inventory level of items.
Item Catalogs matching those product_ids.
How it works
Items with change in inventory level which have been browsed and not yet purchased by users.
Derived event - new in collection
New in collection items reminders
Derived event - new in collection
New in collection items reminders
Use cases
New in collection items reminders.
Recommend related items to recently viewed / purchased 2. Combine with similar items to help users compare various products and let them explore.
Weekly / Monthly newsletters.
Requirements
Events saved on timeline with product_ids in payload.
Item Catalogs matching those product_ids.
Sku and parent_sku relationship to denote collections.
How it works
Recently added items in collections recently browsed.
Churned users
For users who have stopped engaging.
☆ Featured · Brand affinity items
Popular, trending or recent items in affinity brands
☆ Featured · Brand affinity items
Popular, trending or recent items in affinity brands
Use cases
Daily/Weekly newsletter with top/trending items in user affinity brands.
App/Site live content with top/trending items in user affinity brands.
Requirements
Item catalogs with brand.
Items activity/event timeline.
Inputs
Number of items to recommend per affinity brand (Default 2).
Brand items ranking to fetch candidates.
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Popular, Trending, Recent or Deal Items in user affinity brands.
Store name affinity items
Popular, trending or recent items in store name affinities
Store name affinity items
Popular, trending or recent items in store name affinities
Use cases
Daily/Weekly newsletter with top/trending items in user affinity store names.
App/Site live content with top/trending items in user affinity store names.
Requirements
Item catalogs with store name.
Items activity/event timeline.
Inputs
Number of items to recommend per affinity store name (Default 2).
Store Name Ranking.
Site-wide Backfill (if recommended products less than return count). Set 'None' if not required.
How it works
Popular, Trending or Recent Items by user affinity store names.
All users
For users at any lifecycle stage.
Seller popular, trending, recent items
Popular or trending or recent items by seller with size filter
Seller popular, trending, recent items
Popular or trending or recent items by seller with size filter
Use cases
Show case your most popular or trending offerings to your new customers by specific sellers.
Alert all users to recent additions to your catalogs by specific sellers.
Alert all users to seasonal or sponsored promotions by specific sellers.
Win back your inactive customers with exclusive but popular items by specific sellers.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Seller attribute.
Inputs
Seller id.
One of the precomputed rankings across all seller activity or catalogs.
How it works
Popular, Trending, Recent items based on all user activity on your sites and apps at seller level.
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 →