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.
The data consists of:
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
The Big Question:
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.
The comments, shares, and likes data are the indicators of engagement. More engagement usually means more interest in our content, and high levels of engagement can lead to increased visibility through social media algorithms. If these numbers are low, our content may not resonate with our audience.
The caption data can help us understand how captions affect our engagement. Testing different types of captions (such as asking questions, making statements, or using humor) can help determine what our audience responds to best.
The hashtags data can help us categorize our content and help it reach a wider audience. Monitor which hashtags drive the most engagement and adjust our strategy accordingly.
By paying attention to these, we can refine our content and distribution strategies, improve engagement, reach more potential customers, and ultimately achieve better results with our marketing efforts.
There are multiple analysis that I have to perform to answer the questions above:
1. Descriptive Analysis: Using basic statistical tools to get an overview of the data.
2. Correlation Analysis: Determining the relationship between different variables such as:
Does an increase in reach from hashtags lead to an increase follows, likes, shares, comments, and saves?
Does an increase in reach from Explore lead to a rise in follows, likes, shares, comments, and saves?
Does an increase in reach from others lead to a rise in follows, likes, shares, comments, and saves?
3. Regression Analysis: Verifying findings and determining growth such as:
How much does the reach from different sources (home, hashtags, explore, other) impact the number of follows, likes, shares, and comments a post gets?
For each additional reach from 'hashtags', how many more follows, likes, shares, comments, or saves does a post get?
For each additional reach from 'explore', how many more follows, likes, shares, comments, or saves does a post get?
For each additional reach from 'other', how many more follows, likes, shares, comments, or saves does a post get?
4. Business Marketing Analysis: Determining conversion rate.
5. Content Analysis:
Are certain themes or topics in the caption associated with higher engagement rates?
Are there certain hashtags that lead to a higher reach from 'explore' & 'hashtags'?
Click the link below to see the full analysis: