Feed Relevance & Personalization API

Understand your users' interests and personalize your app. Personalization improves engagement, retention, and conversion within your app.

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Feed Ranking

Discovery

Show content that is most likely to engage the user, based on their interests. (Similar to Instagram's explore/search page)

Feed Ranking

Feed Ranking

Rank the feed via machine learning. (Similar to how Facebook uses EdgeRank and machine learning to sort their feed)

Feed Ranking

Follow Suggestions

Suggest relevant users or topics that a user may like to follow, based on our graph analysis algorithms.

Feed Ranking

Email Personalization

Utilize the user's unique interest profile to tailor and optimize the content or products shown in emails they are sent.

  • Social
  • Social
  • E-Commerce
  • Content

This is Liz. Initially your app knows little about her and what she is interested in. Stream works by tracking impressions and user engagement. A short example:

EVENTS

Liz loves browsing hashtags of #barcelona as she plans her vacation.

She spends the most time enaging with posts made by @travelbarcelona and also with her friend Chloe.

 PERSONALIZATION

Your app understands that Liz is interested in Barcelona and likes to see updates from Chloe.

Feed ranking, emails and follow suggestions are optimized based on Liz's interests. So posts about Barcelona and posts from her friend Chloe will appear higher in the feed.

Increase Conversion

In conjunction with our analytics platform, our personalization engine enables you to understand what each of your users are interested in. We create a detailed interest profile for every user. You can use this profile to optimize feed ranking, emails, follow suggestions and more. Schedule a demo


Social Network Personalization

Various social networks have implemented some version of personalization with success. Personalization improves the user experience, retention and engagement.

Here are a few notable implementations of personalization for social networks:

  • Instagram wanted to show users more than their follow feed, so they created the "Explore" section. The explore section uses an interest profile for every user to show content they might like. Stream's personalization API makes it easy to build an Instagram style feed while Stream handles the analytics and machine learning.
  • Quora took a different approach to personalization. Every time you open the feed or digest email they will show you various topics. Based on your engagement with the content they build up a detailed interest profile. Next, Quora uses this profile to optimize emails, follow suggestions and the activity feed. These personalized emails are highly effective in improving user retention.
  • Facebook uses advanced machine learning. They prioritize posts from the friends you engage with most, after starting out with just a basic edge rank algorithm.

Stream makes it really easy to build personalized feeds! Want to learn more? Contact us to talk to our Data Science team!

  • Social
  • Social
  • E-Commerce
  • Content

This is Rose. Initially your app knows little about her and what she is interested in. Stream works by tracking impressions and user engagement. A short example:

EVENTS

While browsing your store, Rose views a number of handbags.

She likes the Coach bag and shares it with her followers.

On her next visit, Rose likes a pair of shoes shared by Kayture, a famous influencer.

 PERSONALIZATION

Over time your app learns which brands and influencers Rose is interested in.

You can use this interest profile to optimize which items you show to Rose, the ranking of feeds and of course emails.

Increase Conversion

In conjunction with our analytics platform, our personalization engine enables you to understand what your individual users are interested in. We create a detailed interest profile for every user. You can use this profile to optimize feed ranking, emails, follow suggestions and more. Schedule a demo


Ecommerce Personalization

The above use case makes it clear that E-commerce Personalization can increase sales. It does so by learning about the user's interests based on clicks, impressions, purchase history and reviews. With Stream you can Personalize your e-commerce platform to tailor recommendations to each of your unique visitors with our highly advanced machine learning.

Here are a few examples of E-Commerce Personalization:

  • User to product recommendations: Recommend products based on the user's interest profile. This is a good fit for weekly emails or display on your homepage.
  • Cart recommendations: Suggest relevant products that a customer may want based on what is in their cart.
  • Product to product recommendations: Show related items that the user might be interested in based on the item they are looking at and their interest profile.
  • Follow suggestions: Suggest influencers the user might be interested in following. Note that this only applies to social ecommerce sites

Personalization is like a virtual salesman of your e-commerce store. With Stream you get to know what customers need and make recommendations that can boost orders and grow your revenue.

  • Social
  • Social
  • E-Commerce
  • Content

This is Lisa. Initially your app knows little about her and what she is interested in. Stream works by tracking impressions and user engagement. A short example:

EVENTS

Lisa opens your homepage for your digital magazine and browses through a set of headlines.

She clicks on an article about day trip hikes in the Colorado Rocky Mountains.

 PERSONALIZATION

Stream learns that Lisa is interested in hiking. Each time she returns to the site we learn a little bit more about her interests.

You can use this interest profile to make sure Lisa never misses out on the content that is most relevant to her.

Increase Conversion

In conjunction with our analytics platform, our personalization engine enables you to understand what your individual users are interested in. We create a detailed interest profile for every user. You can use this profile to optimize feed ranking, emails, follow suggestions and more. Schedule a demo


Content Personalization

Reader retention is an important metric for publishers. Personalization can be a very important technique to surface your content to the right users. According to Evergage, content that is personalized is also more likely to convert visitors.

Here are a few examples of personalization for news and content sites:

  • User to article recommendations: Recommend articles based on the user's interest profile. This is a good fit for weekly emails or display on your homepage.
  • While you were away: highlight your best content for the user since their last visit.
  • Related articles: highlight articles that are related to what the user is reading.

Stream makes it easy to automatically show personalized content based on the interests of your visitors. Contact us to talk to our Data Science team and learn more about personalization.

Stream is For Passionate Developers

Use Cases

Common use cases for our personalization technology include: discovery feeds (like Instagram's explore section), feed ranking (similar to Facebook's edgerank and timeline algorithms), follow suggestions and email personalization.

Developers love Stream API

It’s a conversation

Your users are all unique. Why would your app show the same products to everybody, over and over again? Personalization makes your app more human and shows your users what they are interested in.

Stream is for smart developers who want to save time coding

Get Smarter

The more your users engage with your app, the more personalized the experience will become. Our recommendation engine for news feeds improves engagement, retention and conversion.

Stream Personalization

Personalization is a great way to improve engagement. Get in touch with us to see how Stream can help with your personalization needs.