Algorithms for Engagement: AI to Personalize Your Email Newsletter

Constructing the most engaging newsletter

At, we believe that newsletters containing relevant content create higher subscriber engagement. To put it simply: people interact with email when it brings them the information they want. We have seen this happen over and over, and our case studies document the impact.

And when your subscribers rely on you to deliver relevant information, you become a part of their daily habit. They depend on you.

Personalizing your emails with AI algorithms

Our role is to build newsletters containing the personalized recommendations that will be most likely to engage your readership. To do this, employs several different AI algorithms, blending information to bring a relevant curation of articles to every individual subscriber every day.

Two of the best-known models for generating recommendations are the content filtering and collaborative filtering models.  

Content filtering model

Content filtering is a model that considers the preferences expressed by a reader and attempts to locate new content that aligns with those preferences. A user who has presented a consistent pattern of reading articles about international finance and the English Premier League Soccer will likely be interested in a newly published BBC article about the revenue sharing arrangements of a new television rights deal for the league.

With sophisticated, AI-generated, article-based information, can identify new articles that are most likely to be relevant for each subscriber based on what they have read and enjoyed previously.  

Pandora music service looks at many different facets of the songs a user has liked: the tempo, the tone, the pitch. With a history of such data for a user, it can identify and recommend new songs.

This technique offers the powerful ability to match content to a user’s history. However, it comes with the risk of placing people in a “Walled Garden,” a world where they come to see only the articles that already fit their world view. But, with AI, you can incorporate multiple models to prevent that from happening.

Collaborative filtering model

Collaborative filtering is a model that generates recommendations for one reader based on the similarity of that person’s behavior with the behavior of other readers on your subscriber list. For example, many subscribers who read about English Premier League Soccer have also read about the World Cricket League.  

Our algorithms will use this information to suggest an article about the Scottish Saltires for someone who has previously read articles about the Premier League. This model builds recommendations for a reader based not on their own content preferences, but on the behavior of other similar users.

As another example, Netflix movies streaming service leverages member ratings of films to help suggest other movies for you based on the movies you have watched. The more ratings people provide, the more chances there are to identify common patterns of behavior, and generate recommendations based upon this.

This technique offers the ability to stretch boundaries for relevant content by leveraging the preferences of other readers, not evaluating just the preferences of a single reader.

Using AI to better inform your world

At, our core purpose is to better inform the world, so we can’t rest with just a single algorithm. The best information comes from combinations of multiple algorithms. We strive to bring your readership not just individual articles that are relevant, but an entire newsletter that they will look forward to receiving.

You can begin to engage your readers in a meaningful, personalized way today. Use artificial intelligence to automate and personalize your newsletters.

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