View All Posts

Blueshift’s Recommendation Studio Adds New Derived Events and Tag-Based Recommendations

Gergely Marton

|
Tag-Based Recommendations and Derived Events Recommendations

We all want brands to make our shopping experience intuitive and effortless. That includes keeping us notified and up-to-date on the products and content we’re interested in. According to Forbes, 83% of consumers expect products to be personalized within moments of their engagement. But for brands, it’s no easy task to surface timely, relevant recommendations  — let alone being able to do this at scale to hundreds of thousands or millions of customers. That’s where Blueshift’s SmartHub CDP comes into play to help lighten the load and equip marketers with the complete toolkit to deliver the dynamic experiences consumers now expect. Marketers have instant access to our built-in Recommendation Studio that’s purpose-built for brands to start creating dynamic, targeted messages that get users to convert.

Blueshift’s Recommendation Engine has been providing marketers with an intuitive interface to build dynamic recommendation blocks that can be used across channels. As campaigns run, recommendations adapt to each customer based on first-party data and customer behaviors. Now with the release of newly added Derived Events and Tag-Based Recommendations that are powered by Blueshift’s AI, marketers can drive even more engagement and product discovery by dynamically surfacing the most relevant product offers that consider the latest catalog data and activities for each customer. 

Derived Event Recommendations screenshot

So how can you use Derived Event Recommendations?

Derived Events indicate correlated situations that help marketers understand customers’ affinities so they can alert them later on with relevant content that drives them back to their brand. With our expanded Derived Events Recommendations within the Recommendation Studio, marketers can now trigger timely messages for frequent catalog visits and new items added to a catalog collection in addition to price drop alerts, subscription expiration notices, and back in stock notifications.

But how can marketers put our expanded Derived Event Recommendations to use?

  • Frequent catalog visits: Trigger personalized messages to customers who’ve frequently visited catalog items. For example, if you’re an e-learning company and know a student is frequently visiting a Winter course, automatically trigger a tailored message as soon as enrollment opens.
  • New items in collection: Automatically trigger notifications when a new item is available to customers who have expressed interest or recently viewed an item in that collection. For example, if you’re an online furnishing store with a catalog collection called “holiday products” that includes products like ornaments, lights, and decorations — marketers can automatically notify customers if new products like wrapping paper or Christmas stockings are added.

How does it work?

Derived Events consider historical behaviors and campaign engagement, as well as how frequently customers perform actions such as viewing a product, favoriting, or even sharing it with friends over a certain period. Blueshift’s AI understands when customers are expressing more interest than normal and empowers marketers to respond by automatically triggering smarter alerts and notifications.

Like other events such as ‘add to cart’ or ‘cart abandoned’;  Derived Events live at the customer profile level, but are only generated when a customer performs a set of actions that indicate interest. Once created, they become immediately accessible for marketers to leverage across marketing campaigns.

In a few steps, marketers can start using Derived Events to trigger timely, relevant messages. Simply go to the recommendation builder, select a custom Derived Event in the drop-down, and click save. Once created, marketers can place these blocks into any messaging template and start notifying customers across email, push, In-App, or any other channel.  Once triggers are set up, Blueshift’s AI does the heavy lifting and will message customers once they meet the defined criteria. Marketers become more efficient than ever before as these notifications adapt to each user based on their real-time activities.

Edit screen for tag-based recommendations

So how can we use Derived Events in the real-world?

Let’s imagine you’re an apartment rental marketplace and you know customers are looking for two-bedroom, pet-friendly apartments in a specific San Francisco apartment building. Marketers can automatically notify customers of new floorplans and listings in this building once they are added to the rental listing catalog. This not only saves marketers time and resources by auto-delivering the latest apartment recommendations but it also provides customers with a better experience as they are getting immediately alerted about new listings based on their affinities and preferences.

Introducing Tag-Based Recommendations

Blueshift uses state-of-the-art Natural Language Processing (NLP) algorithms to understand human behaviors and to find similar products based on real-time catalog context such as title, tags, categories and other textual catalog attributes. These recommendations can be used to surface similar products related to recent views or purchase behaviors within the Recommendation Studio.

Tag-based recommendations screenshot

An e-Learning company can use Tag-Based Recommendations to surface relevant class recommendations tailored to each student. For example, if you know students are interested in Health & Wellness courses, Blueshift’s AI model finds classes with semantically similar tags and surfaces those courses to the students that  will be interested in them.

How does it work?

Within Blueshift, every product in a catalog is associated with one or multiple tags.

Tag-based recommendations screenshot

In parallel, every customer profile shows which tags they are interested in or recently viewed.

Customer data profile markup

Behind the scenes, Blueshift’s AI generates a ranked list of similar products using their textual attributes. Marketers can then use these lists across campaigns to surface the best recommendations to customers that encourage them to take action.

Tag-Based Recommendations empower marketers to deliver more engaging experiences throughout the customer lifecycle. With these recommendations, marketers can help customers discover new products and keep them interacting with their brand. Lists dynamically adapt to every customer, so you can be confident you’re always delivering the most relevant message that nudges customers in the right direction.

With Derived Events and Tag-Based Recommendations, marketers are truly able to deliver the most up-to-date notifications and alerts based on a brand’s real-time catalog data and customer context. That helps marketers create more loyal and satisfied customers with meaningful messages at key moments of the omnichannel journey.