Instagram is a prevalent social media platform. With over 1 billion monthly active users, it has become one of the most sought-after platforms for people to share their daily lives, connect with others, build portfolios, and even grow their businesses. Instagram is not only limited to posting pictures and videos but also offers features like stories, reels, and IGTV. The competition to stand out has also increased with the increase in the number of users. This is where data analysis can help. By analyzing data, we can gain insights into the performance of we content and tailor it accordingly to grow our social media presence. In this document, I will analyze Instagram data using Python, explain what I discover, and provide solutions to boost content performance.
The data is provided by Statso from their Instagram account for learning purposes.
Impressions: Number of impressions in a post (Reach)
From Home: Reach from home
From Hashtags: Reach from Hashtags
From Explore: Reach from Explore
From Other: Reach from other sources
Saves: Number of saves
Comments: Number of comments
Shares: Number of shares
Likes: Number of Likes
Profile Visits: Number of profile visits from the post
Follows: Number of Follows from the post
Caption: Caption of the post
Hashtags: Hashtags used in the post
How can these data can help us improve our marketing strategy?
The impressions from Home, Hashtags, and Explore data can help us indicate the sources of our impressions. Analyzing the sources can help us understand where our audience is coming from and where we might need to focus more effort. For instance, we should optimize our hashtag strategy if we're getting low reach from hashtags but high reach from home, and we should optimize our posting strategy if we're getting low reach from home.