The Blueshift Recommendations studio

Great experiences start with the right recommendation. Whether it's a product, article, or offer, intelligent recommendations help customers quickly discover what matters to them — driving deeper engagement and measurable business impact.

Behind every effective recommendation is a balance of AI-powered personalization, real-time signals, and contextual understanding. Blueshift helps teams deliver personalized suggestions without complex setup or long development cycles.

By unifying behavioral data, catalog insights, and predictive models, Blueshift surfaces relevant content across key channels — email, push, SMS, in-app, and web — adapting as user behavior evolves.

  Real results from leading brands

  • 🛏️ Tuft & Needle increased email revenue by 181%.
  • 🚗 CarParts.com boosted engagement by 400%.

These capabilities come together in what we call recommendation recipes — the foundation of Blueshift's personalization strategy.

Why Blueshift recipes drive results

  • Rapid deployment: Access 100+ AI-powered recipes mapped to industry goals and lifecycle stages.
  • Enterprise flexibility: Combine Blueshift's models with your own data science by importing external feeds .

  Unlock the power of 100+ AI-powered recipes

Retail

Recipes for e-commerce and retail platforms focused on product discovery, cart recovery, and personalized shopping.

Media

Recipes for streaming platforms, publishing, and content discovery focused on engagement and personalized feeds.

Marketplaces

Recipes for multi-seller platforms focused on seller-based recommendations and time-sensitive listings.

Automotive

Recipes for auto parts and vehicle-specific recommendations based on year, make, and model.

Online learning

Recipes for educational platforms focused on course, teacher, and content recommendations.

Location-based

Recipes for location-specific recommendations using geographic data like city, state, zipcode, and coordinates.

General

Cross-industry recipes that work across verticals based on universal personalization patterns.

Choosing recipes by user lifecycle

Recipes can be mapped to different stages in the user lifecycle. Consider selecting a recipe based on your intended audience.

User lifecycle stage Sample recipes
New users
  • Popular or trending items site-wide or by category
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
  • Alerts like price drops, low stock, or new arrivals
  • Predictive content feeds auto-optimized based on engagement
Intermittently active users
  • Affinity items ordered by recency or auto-optimized
  • Alerts like new collections, seasonal discounts, or restocks
  • Predictive content feeds 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 or curated items

  What's next

Browse our library of ready-to-use recommendation recipes designed for common use cases across industries.
Explore recommendation recipes →

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