The Amazing Ways Instagram Uses Big Data And Artificial Intelligence

800 million monthly active users and is owned by Facebook. There are 70 million photos uploaded to Instagram every day. People interact with each of those posts by showing their love with a heart, commenting and using hashtags. What all of this activity does is create an enormous amount of data. Once analyzed, by humans as well as increasingly through artificial intelligence algorithms, it can provide incredible business intel and insights into human behavior causing Instagram CEO Kevin Systrom to say , “We’re also going to be a big data company.”

Here are some ways Instagram uses big data and artificial intelligence today.

Via the use of tags and trending information, Instagram users are able to find photos for a particular activity, topic or event or discover experiences, restaurants and places around the world that are trending. Enabled by tagging, the search tools help Instagram users discover things of interest among the millions of uploaded images.

In order to make the data that Instagram collects valuable, it must extract customer insights from it. By assessing the search preferences and engagement insights from its users, Instagram can sell advertising to companies who want to reach that particular customer profile and who might be most interested in receiving a particular marketing message. Since Facebook with 1.8 billion users owns Instagram they have a powerful network of analytics information to help target advertising based on what people like, who they follow and interact with and what they save.

In order to ensure users find value in the platform, it’s important for Instagram to show them what they will like. As the amount of content grows, finding content that each user will find relevant becomes exponentially more challenging. When Instagram changed its feed from reverse-chronological order to showing posts that they believe users would like and share, machine-learning algorithms were put on the job to help sort the information and to better learn over time what is most valued and relevant for each user to create a personalized feed.

Instagram, the social networking app for sharing photos and videos, launched in 2010. Today, it boasts 800 million monthly active users and is owned by Facebook. There are 70 million photos uploaded to Instagram every day. People interact with each of those posts by showing their love with a heart, commenting and using hashtags. What all of this activity does is create an enormous amount of data. Once analyzed, by humans as well as increasingly through artificial intelligence algorithms, it can provide incredible business intel and insights into human behavior causing Instagram CEO Kevin Systrom to say, “We’re also going to be a big data company.”

Here are some ways Instagram uses big data and artificial intelligence today.

Via the use of tags and trending information, Instagram users are able to find photos for a particular activity, topic or event or discover experiences, restaurants and places around the world that are trending. Enabled by tagging, the search tools help Instagram users discover things of interest among the millions of uploaded images.

In order to make the data that Instagram collects valuable, it must extract customer insights from it. By assessing the search preferences and engagement insights from its users, Instagram can sell advertising to companies who want to reach that particular customer profile and who might be most interested in receiving a particular marketing message. Since Facebook with 1.8 billion users owns Instagram they have a powerful network of analytics information to help target advertising based on what people like, who they follow and interact with and what they save.

In order to ensure users find value in the platform, it’s important for Instagram to show them what they will like. As the amount of content grows, finding content that each user will find relevant becomes exponentially more challenging. When Instagram changed its feed from reverse-chronological order to showing posts that they believe users would like and share, machine-learning algorithms were put on the job to help sort the information and to better learn over time what is most valued and relevant for each user to create a personalized feed.