Personalization & Decisioning

We are well past the era when "Hello firstname" was considered to be the height of personalization. Today's customer demands every element of their experience be personalized.

Advanced marketers require that each attribute of every customer action be available as dynamic content for personalization. They need a system that can enable them to express this dynamic content not only on email, but also in channels like mobile push notifications and websites.

Companies at the cutting edge of customer engagement today are delivering 1:1 recommendations in many different forms, powered by Artificial Intelligence (AI) or Machine Learning. Today's digital marketer finally has access to data about customer behavior and preferences in real-time. However, synthesizing this data into recommendations is extremely hard. That's where AI powered systems come in.

RFP Questions for Personalization & Decisioning


  1. Describe how your solution creates dynamic content. What user and event attributes can be used to personalize content?

  2. Can your solution fetch external content in real-time from in-house personalization systems and include them in messages right before sending?

  3. Does your solution autonomously update the text, images, offers, and other template elements in real-time for each user based on the latest user data and behaviors?

  4. For which channels do you support dynamic content personalization?


  1. Can your solution optimize message content, timing, and channel at the user-level based on real-time data in the customer profile, predictive scoring values, and customer behaviors?

  2. Can your solution trigger a message in one channel based on a real-time interaction in another channel?

  3. Can your solution optimize message send times at the user and geographic levels?


  1. Does your solution have a visual interface in which marketers can create and manage recommendation types without technical knowledge? Is this a native capability or through a partnership/ additional integration?

  2. Describe what types of recommendations your solution supports (i.e. collaborative filtering, trending items, similar items, new items, expiring items, etc).

  3. Does your solution ingest product, content, and offer catalog data and use dimensions of that data (i.e. product name, price, etc.) within dynamic recommendations?

  4. Does your solution allow us to import recommendations from our in-house team? If yes, describe how.

  5. Does your solution support combining multiple types of recommendations within a template? If yes, describe how.

  6. Does your solution generate unique recommendations for every user based on real-time data? Does it offer the ability to preview recommendations for different users and add filters?

Read Blueshift's responses to these questions