This project is a comprehensive audit for Social Buzz, a social media and content creation company faced with the challenge of managing massive volumes of data arising from its constantly growing number of users.
The project involves:
- Auditing their big data practices
- Offering strategic recommendations for a successful IPO
- Analyzing content categories to highlight the top 5 by aggregate popularity.
Over 100, 000 posts per day
36,500,000 pieces of content per year
But how to capitalize on it when there is so much?
Analysis to find Social Buzz’s top 5 most popular categories of content
- Data Understanding – Data understanding
- Data Cleaning – Empty rows, Irrelevant columns, appropriate data types
- Data Modeling – Merging datasets, relevant columns
- Data Analysis – Methodology, Summations
- Uncover Insights - Presentation/Reporting Findings
- Top 5 content categories are : Animals, Science, Healthy eating, Technology and Food
- There were 16 different content categories
- The most frequent user reactions are Scared, Peeking, Hate, Cherish whereas Indifferent, Like and interested were the least frequent reactions.
- May 2021 experienced most posts closely followed by Jan ‘21, and Aug ‘20.
- The months of June in both ‘20 and ‘21 recorded significantly lower posts.
Implications: Insights can refine Social Buzz’s content strategy to boost user engagement. Reaction patterns can guide marketing and content placement decisions.
Recommendations:
Focus on expanding content in the top-performing categories to boost engagement further. Use reaction data to personalize user experiences and drive higher engagement rates.
Future Work: Further analysis could explore the impact of specific content types on user reactions. Investigate how external factors (e.g., seasonality, trends) influence content performance.