Blueshift provides many pre-configured recommendation recipes that are created for industry specific use cases and which take into account individual user behavior and aggregate behavior over all users.

When you select a recipe from the Content tab, you can view all the available recipes along with the data requirements, inputs and use cases. Each recipe provides controls for contextual input specific to that recipe, past messaging and behavior suppression, filters for audience specific logic and sorting options.

Recipes are broadly mapped to following user journey touch points throughout their life cycle. Consider picking a recipe based on the intended audience.

User Life Cycle Sample Recipes
New Users
  • Popular or trending items site wide or specific catalog attributes
Active Users
  • Similar Items to recent activity
  • Similar Items other users like them consider
  • Next Best Items based on recent activity 
  • Related Items based on frequent visits 
  • Related Items to current session viewing history
  • Affinity Items ordered by popularity or auto optimized
  • Items based on explicit user preferences/subscriptions
  • Alerts like price drops, low in stock or new in collection
  • Predictive Content feeds auto optimized based on engagement
Intermittently Active Users
  • Affinity Items based ordered by recency or auto optimized
  • Alerts like new in collections, seasonal discounts, back in stock
  • Predictive Content feeds auto optimized based on prior activity
Churned Users
  • Winback promotions ordered by popularity
  • Predictive Content based on previous transactions
  • Promotional Content related to prior activity
  • Affinity items ordered by recency or auto optimized
All Users
  • Predictive content based on user attributes
  • Seasonal Items ordered by popularity or auto optimized
  • Newly added items ordered by recency
  • Editorial/Curated Items


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