This project explores the effectiveness of social media campaigns across major platforms like LinkedIn, Instagram, Twitter, and Facebook by analyzing post types, timing, audience demographics, and sentiment. It aims to uncover actionable insights that help optimize campaign strategies for better engagement and reach.
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Engagement Analysis
- How do different platforms perform in terms of engagement rate, reach, and impressions?
- What is the relationship between engagement and content type (video, image, link)?
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Sentiment Impact
- Does sentiment (positive, neutral, negative) influence audience engagement?
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Demographic Insights
- How do audience age, gender, and location impact campaign effectiveness?
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Post Timing
- What’s the correlation between post timing and engagement (likes, comments, shares)?
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Campaign Performance
- Which campaigns drive the highest engagement and what attributes contribute?
The dataset includes structured data across four social media platforms:
- Post Details: Post ID, content, type (video, image, link), timestamp
- Engagement Metrics: Likes, comments, shares, impressions, engagement rate
- Audience Demographics: Age, gender, location, interests
- Campaign Info: Sentiment, campaign ID, influencer ID
Provisional source: Internal structured file with post-campaign data. Additional validation applied.
The following charts and techniques were used to explore and analyze the data:
- Heatmaps
- Bar Charts
- Line Graphs
- Scatter Plots
- Pie Charts
- Area Charts
- Box Plots
These visualizations enabled the extraction of deep insights into user behavior, content performance, and strategic recommendations.
Watch the project walkthrough here:
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- Tableau / Power BI (if used)
- Jupyter Notebook
- Excel / CSV Data Cleaning
This project is part of academic coursework and is for educational purposes only.
Yash Deshpande
📧 yashdd10@gmail.com
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