Blueshift’s Recommendation studio provides an intuitive, easy-to-use drop-down interface to build recommendation schemas that pull directly from your catalog in a self-service manner, without the need for IT teams or data scientists. You can create content blocks with product recommendations, recommended offers, or other brand content like blogs or videos that adapt and dynamically personalize to every individual user in real-time based on their current context, recent activity, and their product, category or brand interests.
You have full control and flexibility to define what kind of content or products to include or exclude within each content block. You can mix and match recommendation types and set how to backfill recommendations to ensure that users are always receiving the most relevant offers, products, and content. You can also preview what recommendations look like for any given user or segment.
Product, content, and offer recommendations can then be easily used in messaging templates across email, push, SMS, in-app, web personalization via live content, and more. As campaigns run, the recommendations presented autonomously adapt to each user in real-time based on their current context, their affinities, their latest activity and behaviors, and the latest catalog data, which ensures that content remains current and relevant and avoids fatiguing users. This allows you to set up message templates that incorporate recommendations once and be confident that the content delivered is always relevant even as users' interests and intentions evolve over time.
Recommendation Types
Blueshift easily ingests your content and product catalogs (products, blog articles, videos, offers, and so on) in a very flexible way and leverages all the metadata fields from your catalogs. Using our built-in recommendation engine, Blueshift can then easily recommend the most relevant next-best items (i.e., offers, content, products, etc.) from the catalogs based on the catalog metadata and user data and behaviors and serve that content dynamically within user communications across channels.
Within Blueshift’s recommendation studio, you can define recommendation schemes for everything from abandoned content, related items, top trending items, top sellers, most viewed, “users also bought,” next-best-product, expiring items, recent additions, and more. For recommending relevant, contextual predictive content/items/products, we use matrix factorization and collaborative filtering algorithms. You also have advanced controls to define what kinds of content to include or exclude within each content scheme, mix and match recommendation types, and set how to backfill recommendations to ensure that users receive the most relevant and highest converting offers and messages.
External recommendations
You can bring your own external recommendations by uploading them directly into Blueshift as recommendation feeds. If you have a data science team and have your own recommendations, you can combine these external recommendations with our AI-powered recommendations that are built using our recommendation studio and then test, experiment, and optimize the best performing combination of recommendations that result in the highest engagement and conversions in your marketing messages.
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