Understanding Twitter Engagement:
An Overview of KPIs, Trends, and Top Performers

Project Description

The "Understanding Twitter Engagement: An Overview of KPIs, Trends, and Top Performers" project is a personal endeavor that I have undertaken to provide users with valuable insights into Twitter engagement. By analyzing a diverse sample of Twitter data, I aim to offer a holistic understanding of key performance indicators, emerging trends, and top performers on the platform.

To analyze and visualize the data, I utilize a combination of Python and Tableau. Python serves as a powerful tool for conducting exploratory data analysis (EDA), enabling me to delve deep into the data's structure and quality. Through Python, I identify patterns, anomalies, and other valuable insights within the dataset.

Following the EDA phase, I leverage Tableau's capabilities to build an interactive dashboard that empowers users to interact with the data in a meaningful way. The dashboard I create offers filters, allowing users to explore specific aspects of Twitter engagement based on their interests and needs. By providing an intuitive user interface and dynamic visualization, the dashboard enables users to gain actionable insights and make informed decisions regarding their Twitter presence.

Throughout the data processing and analysis stages, I employ various techniques to enhance the data's comprehensibility and facilitate effective analysis. This includes creating calculated fields to derive additional metrics that provide a deeper understanding of Twitter engagement dynamics.

This project aims to equip users with a comprehensive understanding of Twitter engagement, empowering them to optimize their presence on the platform. With its insightful analysis, interactive dashboard, and unique insights into Twitter's performance metrics and trends, this project serves as a valuable tool for individuals and businesses seeking to enhance their Twitter engagement and achieve their goals in the dynamic realm of social media.

Analytics Approach

The Data Analysis in this project involved several steps to process, analyze, and visualize the data. First, we downloaded the dataset from the data.world website and imported it into Python for exploratory data analysis (EDA). During the EDA phase, we used Python to examine the structure and quality of the data, looking for any inconsistencies, missing values, or potential outliers.

After conducting exploratory data analysis (EDA) in Python and ensuring the data was clean and reliable, I turned to Tableau to create an interactive dashboard that would illuminate key insights from the Twitter dataset. Utilizing the findings from the EDA phase, I embarked on analyzing the data further and identifying meaningful patterns and trends.

To present the data in a visually compelling manner, I crafted a variety of informative charts and visualizations. These visuals were strategically designed to showcase important metrics, relationships, and comparisons within the dataset. Leveraging Tableau's capabilities, I incorporated calculated fields to enhance the level of understanding and interactivity within the dashboard.

By creating these calculated fields, I aimed to extract additional insights and facilitate a deeper understanding of the data. These fields allowed for customized calculations, transformations, and aggregations that provided users with clear and meaningful information.

Furthermore, in designing the interactive dashboard, I strived to ensure its user-friendliness and accessibility. I carefully considered the most informative charts and integrated them into a cohesive layout. By incorporating intuitive design principles and interactive features, I aimed to enable users to delve into the data, uncover trends, and make informed interpretations.

Through the combination of Python's data processing capabilities and Tableau's visualization prowess, I successfully created an interactive dashboard that highlights key findings and encourages exploration of the Twitter dataset. The integration of calculated fields further enhances the interpretability and value of the dashboard, providing users with a comprehensive and insightful data analysis experience.

Results

Dashboard

This interactive dashboard allows you to filter based on month and day and also each KPI, making it easy to find any anomalies or trends. This dashboard also provides interesting insights into the data, such as:

• • All calculated KPIs are decreasing (Compared to September) including:

    • • Impression

      • Likes

      • Engagement

  • The important tweets should be tweeted between 10 a.m. to 11 a.m.

  • The hours of tweeting should be adjusted according to the engagement rates associated with the time of the day.

This dashboard provides a wealth of information and is a helpful reporting tool for anyone planning run and analyze Twitter Campaign.

Feel free to explore the interactive dashboard further here by adjusting the filters or experimenting with different charts and visualizations. 

Project Credits

This project was created by [Maryam Aliakbari].

Maryam were responsible for designing the project, conducting the data analysis, and creating the interactive dashboard. She utilized Python for exploratory data analysis and Tableau for creating the dashboard.