An interactive Power BI dashboard created to analyze social media performance across platforms, content types, categories, sentiments, posting days, and time slots.
The Social Media Engagement Dashboard provides a complete overview of social media performance.
It helps identify:
- The best-performing social media platform
- The most engaging content type
- Monthly engagement trends
- Audience sentiment
- The best day to publish content
- The best time slot for posting
- Overall views, shares, posts, and engagement rate
The dashboard contains interactive filters that allow users to explore the data according to platform, content type, category, sentiment, and date.
The main objectives of this project are:
- Track overall social media performance
- Compare engagement across different platforms
- Identify the best-performing content types
- Analyze monthly engagement trends
- Understand audience sentiment
- Find the best day and time to publish content
- Provide interactive filtering for detailed analysis
- Support data-driven social media decisions
| KPI | Value |
|---|---|
| Total Posts | 5,000 |
| Total Views | 554.84M |
| Total Engagement | 47.33M |
| Average Engagement Rate | 11.96% |
| Total Shares | 4.18M |
This visual compares total engagement across different social media platforms.
| Platform | Total Engagement |
|---|---|
| TikTok | 23.42M |
| 7.50M | |
| YouTube | 6.68M |
| 5.16M | |
| 3.81M | |
| 0.76M |
Insight: TikTok generated the highest engagement, while LinkedIn recorded the lowest engagement.
The monthly engagement trend visual shows how engagement changed across 2024 and 2025.
This chart helps identify:
- High-performing months
- Low-performing months
- Monthly increases and decreases
- Seasonal engagement patterns
- Audience activity over time
| Content Type | Total Engagement |
|---|---|
| Video | 10.52M |
| Stitch | 7.90M |
| Duet | 7.78M |
| Story | 3.18M |
| Live | 2.95M |
Insight: Video content generated the highest engagement among all content types.
| Sentiment | Engagement | Percentage |
|---|---|---|
| Positive | 24.43M | 51.61% |
| Neutral | 16.12M | 34.06% |
| Negative | 6.78M | 14.33% |
Insight: Positive sentiment contributed more than half of the total engagement.
| Day | Total Engagement |
|---|---|
| Monday | 6.66M |
| Tuesday | 6.93M |
| Wednesday | 7.49M |
| Thursday | 6.97M |
| Friday | 6.53M |
| Saturday | 6.43M |
| Sunday | 6.32M |
Insight: Wednesday was the best-performing day, generating 7.49M total engagement.
The heatmap compares engagement rates across different days and posting time slots.
It helps identify:
- The most effective posting time
- The best day and time combination
- High-engagement publishing periods
- Low-performing posting periods
- Audience activity patterns
The dashboard includes the following interactive filters:
- Date
- Platform
- Content Type
- Category
- Sentiment
These filters allow users to explore specific sections of the data and perform detailed analysis.
- TikTok was the strongest platform with 23.42M engagement.
- Video was the best-performing content type with 10.52M engagement.
- Wednesday generated the highest day-wise engagement with 7.49M.
- Positive sentiment represented 51.61% of total engagement.
- LinkedIn recorded the lowest platform engagement.
- Monthly engagement showed noticeable fluctuations across 2024 and 2025.
- The heatmap helps identify the most effective day and time for publishing content.
- The dashboard recorded an average engagement rate of 11.96%.
- A total of 554.84M views were generated from 5,000 posts.
- Total shares reached 4.18M.
- Power BI
- Power Query
- DAX
- Microsoft Excel
- CSV Dataset
- Data Cleaning
- Data Transformation
- Data Modelling
- Data Visualization
- Business Intelligence
- Collected the social media engagement dataset.
- Imported the dataset into Power BI.
- Reviewed the dataset structure.
- Checked for missing values and duplicate records.
- Corrected column names and data types.
- Cleaned and transformed the data using Power Query.
- Created a date table for time-based analysis.
- Established relationships between tables.
- Created calculated columns where required.
- Created DAX measures for dashboard KPIs.
- Designed KPI cards.
- Created platform and content-type analysis charts.
- Created the monthly engagement trend.
- Created the sentiment distribution chart.
- Created the best-day-to-post visual.
- Created the day and time-slot heatmap.
- Added interactive dashboard filters.
- Applied formatting and visual improvements.
- Tested dashboard interactions.
- Extracted key business insights.
Some of the important measures used in the dashboard are:
Total Posts =
COUNTROWS('Social Media Data')
Total Views =
SUM('Social Media Data'[Views])
Total Likes =
SUM('Social Media Data'[Likes])
Total Comments =
SUM('Social Media Data'[Comments])
Total Shares =
SUM('Social Media Data'[Shares])
Total Engagement =
[Total Likes] + [Total Comments] + [Total Shares]
Average Engagement Rate =
DIVIDE(
[Total Engagement],
[Total Views],
0
)
The table and column names may need to be changed according to the names used in the Power BI file.
Social-Media-Engagement-Dashboard/
│
├── README.md
├── Dashboard_Preview.png
├── Social_Media_Engagement_Dashboard.pbix
└── social_media_engagement_dataset.csv
- Download or clone this repository.
- Install Power BI Desktop.
- Open the
Social_Media_Engagement_Dashboard.pbixfile. - Update the dataset location if Power BI requests a new file path.
- Refresh the data.
- Use the dashboard filters to explore the report.
- Select different platforms, content types, sentiments, categories, and dates for detailed analysis.
This dashboard can help social media managers, marketing teams, content creators, and business analysts to:
- Understand social media performance
- Identify the strongest platform
- Select the best-performing content format
- Improve content planning
- Optimize posting schedules
- Track audience sentiment
- Compare engagement across platforms
- Make data-driven marketing decisions
- Improve future social media strategies
- Focus resources on high-performing content
- Data Cleaning
- Data Transformation
- Data Modelling
- DAX Calculations
- KPI Development
- Dashboard Designing
- Data Visualization
- Trend Analysis
- Sentiment Analysis
- Business Insight Generation
- Interactive Reporting
- Power BI Development
The dashboard can be improved further by adding:
- Follower growth analysis
- Campaign performance analysis
- Paid versus organic engagement
- Geographic audience analysis
- Device-based engagement analysis
- Hashtag performance
- Conversion rate analysis
- Revenue generated from social media
- Platform-wise engagement rate
- Content performance forecasting
Aftab Monye
Aspiring Data Analyst
Excel | SQL | Power BI | Tableau | Python
Feedback and suggestions are welcome.
Feel free to explore the project and share your thoughts.
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